The DCE reading group webpage was renewed for 2021. Please check the new webpage for the lastest talk shcedule: https://sites.google.com/view/dce-reading-group
This is a weekly reading group of Data-Centric Engineering programme in The Alan Turing Institute (https://www.turing.ac.uk/research/research-programmes/data-centric-engineering). The invited speakers give talks on a variety of topics ranging from methodology to application. Please check the schedule below for upcoming talks. This group is open to everyone. Please feel free to conract organizers if you would like to register for our emailing list or give a talk on your research. We are holding all talks online currently.
15th Wednesday 11:00 - 12:00 @ Lovelace Room at ATI
• Topic: Monte Carlo wavelets: a randomized approach to frame discretization
• Speaker: Lorenzo Rosasco (University of Genoa, Italy)
• Abstract: hide / show
In this paper we propose and study a family of continuous wavelets on general domains, and a corresponding stochastic discretization that we call Monte Carlo wavelets. First, using tools from the theory of reproducing kernel Hilbert spaces and associated integral operators, we define a family of continuous wavelets by spectral calculus. Then, we propose a stochastic discretization based on Monte Carlo estimates of integral operators. Using concentration of measure results, we establish the convergence of such a discretization and derive convergence rates under natural regularity assumptions.
22nd Wednesday 11:00 - 12:00 @ Margaret Hamilton Room at ATI
• Topic: Bayesian Optimal Design for iterative refocussing
• Speaker: Victoria Volodina (The Alan Turing Institute, UK)
• Abstract: hide / show
History matching is a type of calibration, that attempts to find input parameters values to achieve the consistency between observations and computer model representation. History matching is most effective when it is performed in waves, i.e. refocussing steps (Williamson et al., 2017). At each wave a new ensemble is obtained within the current Not Ruled Out Yet space (NROY), the emulator is updated and the procedure of cutting down the input space is performed again. Generating design for each wave is a challenging problem due to the unusual shapes of NROY space. A number of approaches (Williamson and Vernon, 2013; Gong et al., 2016, Andrianakis et al., 2017) are focused on obtaining space-filling design over the NROY space. In this talk we present a new decision-theoretic method for a design problem for iterative refocussing. We employ a Bayesian experimental design and specify a loss function that compares a volume of NROY space obtained with an updated emulator to the volume of `true' NROY space obtained using a `perfect' emulator. The derived expected loss function contains three independent and interpretable terms. In this talk we compare the effect of proposed Bayesian Optimal Design to space-filling design approaches on the iterative refocussing performed on simulation studies. We recognise that adopted Bayesian experimental design involves an expensive optimization problem. Our proposed criterion also could be used to investigate and rank a range of candidate designs for iterative refocussing. In this talk we demonstrate the mathematical justification provided by our Bayesian Design Criterion for each design candidate.
29th Wednesday 11:00 - 12:00 @ Margaret Hamilton Room at ATI
• Topic: Integrated Emulators for Systems of Computer Models
• Speaker: Deyu Ming (University College London, UK)
• Abstract: hide / show
We generalize the state-of-the-art linked emulator for a system of two computer models under the squared exponential kernel to an integrated emulator for any feed-forward system of multiple computer models, under a variety of kernels (exponential, squared exponential, and two key Matérn kernels) that are essential in advanced applications. The integrated emulator combines Gaussian process emulators of individual computer models, and predicts the global output of the system using a Gaussian distribution with explicit mean and variance. By learning the system structure, our integrated emulator outperforms the composite emulator, which emulates the entire system using only global inputs and outputs. Orders of magnitude prediction improvement can be achieved for moderate-size designs. Furthermore, our analytic expressions allow a fast and efficient design algorithm that allocates different runs to individual computer models based on their heterogeneous functional complexity. This design yields either significant computational gains or orders of magnitude reductions in prediction errors for moderate training sizes. We demonstrate the skills and benefits of the integrated emulator in a series of synthetic experiments and a feed-back coupled fire-detection satellite model.
5th Wednesday 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: A kernel log-rank test of independence
• Speaker: Tamara Fernadez (University College London, UK)
• Abstract: hide / show
With the incorporation of new data gathering methods in clinical research, it becomes fundamental for Survival Analysis techniques to be able to deal with high-dimensional or/and non-standard covariates. In this paper we introduce a general non-parametric independence test between right-censored survival times and covariates taking values on a general space $\mathcal{X}$. We show our test statistic has a dual intepretation, first in terms of the supremum of a potentially infinite collection of weight-indexed log-rank tests, with weight functions belonging to a Reproducing kernel Hilbert space of functions (RKHS), and second, as the norm of the difference of the embeddings of certain ``depencency" measures into the RKHS, similarly to the well-know HSIC test-statistic. We provide an easy-to-use test-statistic as well as an economic Wild-Bootstrap procedure and study asymptotic properties of the test, finding sufficient conditions to ensure our test is omnibus. We perform extensive simulations demonstrating that our testing procedure performs, in general, better than competing approaches.
12nd Wednesday 11:00 - 12:00 @ Lovelace Room at ATI
• Topic: Efficient high-dimensional emulation and calibration
• Speaker: James Salter (University of Exeter / The Alan Turing Institute, UK)
• Abstract: hide / show
Expensive computer models often have high-dimensional spatial and/or temporal outputs, all of which we may be interested in predicting for unseen regions of parameter space, and in comparing to real-world observations (calibration/history matching). When emulating such fields, it is common to either emulate each output individually, or project onto some low dimensional basis (e.g. SVD/PCA, rotations thereof). Typically in a calibration exercise, we will require emulator evaluations for extremely large numbers of points in the input space, with this problem becoming more computationally intensive as the output size increases. We demonstrate several benefits of the basis approach to emulation of such fields, compared to emulating each output individually. In particular, the efficiency that the basis structure allows, both when evaluating predictions at unseen inputs, and in calculating the calibration distance metric (implausibility). We discuss how the basis should be chosen and explore examples from climate and engineering models.
19nd Wednesday 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
• Speaker: Yura Perov (Babylon Health, UK)
• Abstract: hide / show
We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference. We show how this can be implemented natively in probabilistic programming. By considering the structure of the counterfactual query, one can significantly optimise the inference process. We also consider design choices to enable further optimisations. We introduce MultiVerse, a probabilistic programming prototype engine for approximate causal reasoning. We provide experimental results and compare with Pyro, an existing probabilistic programming framework with some of causal reasoning tools.
26th Wednesday 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: On the geometry of Stein variational gradient descent
• Speaker: Andrew Duncan (Imperial College London / The Alan Turing Institute, UK)
• Abstract: hide / show
Bayesian inference problems require sampling or approximating high-dimensional probability distributions. The focus of this paper is on the recently introduced Stein variational gradient descent methodology, a class of algorithms that rely on iterated steepest descent steps with respect to a reproducing kernel Hilbert space norm. This construction leads to interacting particle systems, the mean-field limit of which is a gradient flow on the space of probability distributions equipped with a certain geometrical structure. We leverage this viewpoint to shed some light on the convergence properties of the algorithm, in particular addressing the problem of choosing a suitable positive definite kernel function. Our analysis leads us to considering certain nondifferentiable kernels with adjusted tails. We demonstrate significant performs gains of these in various numerical experiments.
4th Wednesday 13:00 - 14:00 @ Lovelace Room at ATI
• Topic: A kernel two-sample test for functional data
• Speaker: George Wynne (Imperial College London, UK)
• Abstract: hide / show
Two-sample testing is the task of observing two collections of data samples and determining if they have the same distribution or not. The use of kernel based two-sample tests has seen a lot of success in machine learning, for example in training GANs, due to its helpful computational and theoretical properties. However the vast majority of work done so far in investigating this method has assumed the underlying data are random vectors. We investigate the generalisation of these methods to the case of functional data, where one data point is a function observed at a discrete number of inputs. Such data could still be considered as random vectors but we show how taking the functional view point opens up a large range of theoretical and practical insight. We introduce a new class of kernels which result in consistent tests and discuss their implementation and theoretical properties. This is joint work with Andrew Duncan.
11th Wednesday 13:00 - 14:00 @ Mary Shelley Room at ATI
• Topic: General Bayesian Updating
• Speaker: Edwin Fong (University of Oxford / The Alan Turing Institute, UK)
• Abstract: hide / show
General Bayesian updating is a coherent framework for updating a prior belief distribution to a posterior, where the beliefs are over a parameter of interest that is related to observed data through a loss function, instead of directly corresponding to a likelihood function. This is allows us to bypass the Bayesian machinery of modelling the true data-generating distribution which is becoming increasingly cumbersome as datasets grow in size and complexity. The belief update is derived through decision theoretic arguments, and in particular requires coherence of beliefs. I will be discussing the foundations of general Bayesian updating, as well as some more recent work on selecting the tempering parameter, links below. Links: https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssb.12158 https://academic.oup.com/biomet/article/106/2/465/5385582
1st Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Augmenting Statistics with a representation of deterministic uncertainty
• Speaker: Jeremie Houssineau (University of Warwick, UK)
• Abstract: hide / show
It is commonly accepted in statistics that the only way of dealing with uncertainty is through the mathematical tools characterising random phenomena. However, it is often information, or rather the lack of it, that is the source of uncertainty. Using probability theory in this case has known limitations such as the absence of non-informative priors in unbounded spaces. The objective of this talk is to show how the measure-theoretic concept of outer measure can be used to model the lack of information about some parameters in a statistical model and to derive practical algorithms that complement the existing wealth of statistical techniques in a principled and yet intuitive way.
8th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Generalized Variational Inference: Three arguments for deriving new Posteriors
• Speaker: Jeremias Knoblauch (University of Warwick / The Alan Turing Institute, UK)
• Abstract: hide / show
We advocate an optimization-centric view on and introduce a novel generalization of Bayesian inference. Our inspiration is the representation of Bayes' rule as infinite-dimensional optimization problem (Csiszar, 1975; Donsker and Varadhan; 1975, Zellner; 1988). First, we use it to prove an optimality result of standard Variational Inference (VI): Under the proposed view, the standard Evidence Lower Bound (ELBO) maximizing VI posterior is preferable to alternative approximations of the Bayesian posterior. Next, we argue for generalizing standard Bayesian inference. The need for this arises in situations of severe misalignment between reality and three assumptions underlying standard Bayesian inference: (1) Well-specified priors, (2) well-specified likelihoods, (3) the availability of infinite computing power. Our generalization addresses these shortcomings with three arguments and is called the Rule of Three (RoT). We derive it axiomatically and recover existing posteriors as special cases, including the Bayesian posterior and its approximation by standard VI. In contrast, approximations based on alternative ELBO-like objectives violate the axioms. Finally, we study a special case of the RoT that we call Generalized Variational Inference (GVI).  GVI posteriors have appealing properties, including consistency and an interpretation as approximate ELBO. The last part of the paper explores some attractive applications of GVI in popular machine learning models, including robustness and more appropriate marginals. After deriving black box inference schemes for GVI posteriors, their predictive performance is investigated on Bayesian Neural Networks and Deep Gaussian Processes, where GVI can comprehensively improve upon existing methods.
15th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: On the stability of general Bayesian inference
• Speaker: Jack Jewson (University of Warwick, UK)
• Abstract: hide / show
Bayesian stability and sensitivity analyses have traditionally investigated the stability of posterior inferences to the subjective specification of the prior. We extend this to consider the stability of inference to the selection of the likelihood function. Rather than making the unrealistic assumption that the model is correctly specified for the process that generated the data, we assume that it is at best one of many possible approximations of the underlying process broadly representing the decision maker's (DM) beliefs. We thus investigate the stability of posterior inferences to the arbitrary selection of one of these approximations. We show that traditional Bayesian updating, minimising the Kullback-Leibler Divergence (KLD) between the sample distribution of the observations and the model, is only stable to a very strict class of likelihood models. On the other hand, generalised Bayesian inference (Bissiri et al., 2016) minimising the beta-divergence (betaD) is shown to be stable across interpretable neighbourhoods of likelihood models. The result is that using Bayes' rule requires a DM to be unrealistically sure about the probability judgements made by their model while updating using the betaD is stable to reasonable perturbations from the chosen model. We illustrate this for several regression and classification examples including a Bayesian on-line changepoint detection algorithm applied to air pollution data from the City of London.
22nd Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Generalized Bayesian Filtering via Sequential Monte Carlo
• Speaker: Ayman Boustati (University of Warwick, UK)
• Abstract: hide / show
We introduce a framework for inference in general state-space hidden Markov models (HMMs) under likelihood misspecification. In particular, we leverage the loss-theoretic perspective of generalized Bayesian inference (GBI) to define generalized filtering recursions in HMMs, that can tackle the problem of inference under model misspecification. In doing so, we arrive at principled procedures for robust inference against observation contamination through the β-divergence. Operationalizing the proposed framework is made possible via sequential Monte Carlo methods (SMC). The standard particle methods, and their associated convergence results, are readily generalized to the new setting. We demonstrate our approach to object tracking and Gaussian process regression problems, and observe improved performance over standard filtering algorithms.
29th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: PAC-Bayes with Backprop: Training Probabilistic Neural Nets by minimizing PAC-Bayes bounds
• Speaker: Omar Rivasplata (University College London / DeepMind, UK)
• Abstract: hide / show
In this talk I will (1) discuss some statistical learning background with special emphasis on the PAC-Bayes framework, and (2) present some highlights of a project where experiments were carried out in order to understand the properties of training objectives for neural nets derived from PAC-Bayes bounds. Previous works that derived a training objective for neural nets from a classical PAC-Bayes bound showed that this approach is capable of producing non-vacuous risk bound values, while other works from the direction of Bayesian learning showed that randomized classifiers are capable of achieving competitive test accuracy on the standard MNIST dataset. Our project experimented with training objectives derived from two PAC-Bayes bounds besides the classical one, whose choice was motivated by being tighter bounds in the regime of small empirical losses. The results of our experiments showed that randomized Gaussian classifiers learned from these training objectives are capable of achieving both goals at the same time: competitive test accuracy and non-vacuous risk bound values, in fact with tighter values that previous work. I will discuss what we have learned from these experiments, including what explains the improvement with respect to previous works.
6th Wednesday 13:00 - 14:00 @ Zoom (online)
• Topic: Optimistic bounds for multi-output prediction
• Speaker: Henry Reeve (University of Birmingham, UK)
• Abstract: hide / show
We investigate the challenge of multi-output learning, where the goal is to learn a vector-valued function based on a supervised data set. This includes a range of important problems in Machine Learning including multi-target regression, multi-class classification and multi-label classification. We begin our analysis by introducing the self-bounding Lipschitz condition for multi-output loss functions, which interpolates continuously between a classical Lipschitz condition and a multi-dimensional analogue of a smoothness condition. We then show that the self-bounding Lipschitz condition gives rise to optimistic bounds for multi-output learning, which are minimax optimal up to logarithmic factors. The proof exploits local Rademacher complexity combined with a powerful minoration inequality due to Srebro, Sridharan and Tewari. As an application we derive a state-of-the-art generalization bound for multi- class gradient boosting.
13th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Distilling importance sampling
• Speaker: Dennis Prangle (Newcastle University, UK)
• Abstract: hide / show
To be efficient, importance sampling requires the selection of an accurate proposal distribution. This talk describes learning a proposal distribution using optimisation over a flexible family of densities developed in machine learning: normalising flows. Training data is generated by running importance sampling on a tempered version of the target distribution, and this is "distilled" by using it to train the normalising flow. Over many iterations of importance sampling and optimisation, the amount of tempering is slow reduced until an importance sampling proposal for an accurate target distribution is generated.
20th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: An overview of nonasymptotic analysis for the stochastic gradient Markov chain Monte Carlo
• Speaker: Omer Deniz Akyildiz (University of Warwick / The Alan Turing Institute, UK)
• Abstract: hide / show
In this talk, I will give a brief overview of stochastic gradient MCMC methods and their nonasymptotic analysis. I will first talk about the nonasymptotic bounds for Unadjusted Langevin Algorithm (ULA) and the stochastic gradient Langevin dynamics (SGLD) in Wasserstein-2 distance, then will move on to the recent results with non-log-concave distributions and finalize with applications to non-convex optimization.
27th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Nonparametric Learning with the Posterior Bootstrap
• Speaker: Edwin Fong (University of Oxford / The Alan Turing Institute, UK)
• Abstract: hide / show
We present a scalable Bayesian nonparametric learning routine that enables posterior sampling through the optimization of suitably randomized objective functions. A Dirichlet process prior on the unknown data distribution accounts for model misspecification and admits an embarrassingly parallel posterior bootstrap algorithm that generates independent and exact samples from the nonparametric posterior distribution. Our method has attractive theoretical properties and is particularly adept at sampling from multimodal posterior distributions via a random restart mechanism. I will be presenting our paper as well as a summary of previous work, links below. Links: http://proceedings.mlr.press/v97/fong19a/fong19a.pdf https://papers.nips.cc/paper/7477-nonparametric-learning-from-bayesian-models-with-randomized-objective-functions.pdf
3rd Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Non-asymptotic bounds for sampling algorithms without log-concavity
• Speaker: Mateusz B. Majka (Heriot-Watt University, UK)
• Abstract: hide / show
Discrete time analogues of ergodic stochastic differential equations (SDEs) are one of the most popular and flexible tools for sampling high-dimensional probability measures. Non-asymptotic analysis in the L2 Wasserstein distance of sampling algorithms based on Euler discretisations of SDEs has been recently developed by several authors for log-concave probability distributions. In this work we replace the log-concavity assumption with a log- concavity at infinity condition. We provide novel L2 convergence rates for Euler schemes, expressed explicitly in terms of problem parameters. From there we derive non-asymptotic bounds on the distance between the laws induced by Euler schemes and the invariant laws of SDEs, both for schemes with standard and with randomised (inaccurate) drifts. We also obtain bounds for the hierarchy of discretisation, which enables us to deploy a multi-level Monte Carlo estimator. Our proof relies on a novel construction of a coupling for the Markov chains that can be used to control both the L1 and L2 Wasserstein distances simultaneously. Finally, we provide a weak convergence analysis that covers both the standard and the ran- domised (inaccurate) drift case. In particular, we reveal that the variance of the randomised drift does not influence the rate of weak convergence of the Euler scheme to the SDE.
10th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Maximum Mean Discrepancy Gradient Flow
• Speaker: Michael Arbel (University College London, UK)
• Abstract: hide / show
We construct a Wasserstein gradient flow of the maximum mean discrepancy (MMD) and study its convergence properties. The MMD is an integral probability metric defined for a reproducing kernel Hilbert space (RKHS), and serves as a metric on probability measures for a sufficiently rich RKHS. We obtain conditions for convergence of the gradient flow towards a global optimum, that can be related to particle transport when optimizing neural networks. We also propose a way to regularize this MMD flow, based on an injection of noise in the gradient. This algorithmic fix comes with theoretical and empirical evidence. The practical implementation of the flow is straightforward, since both the MMD and its gradient have simple closed-form expressions, which can be easily estimated with samples.
24th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Cross-validation based adaptive sampling for Gaussian process models
• Speaker: Hossein Mohammadi (University of Exeter, UK)
• Abstract: hide / show
In many real-world applications, we are interested in approximating black-box, costly functions as accurately as possible with the smallest number of function evaluations. A complex computer code is an example of such a function. In this work, a Gaussian process (GP) emulator is used to approximate the output of complex computer code. We consider the problem of extending an initial experiment sequentially to improve the emulator. A sequential sampling approach based on leave-one-out (LOO) cross-validation is proposed that can be easily extended to a batch mode. This is a desirable property since it saves the user time when parallel computing is available. After fitting a GP to training data points, the expected squared LOO error (ESELOO) is calculated at each design point. ESELOO is used as a measure to identify important data points. More precisely, when this quantity is large at a point it means that the quality of prediction depends a great deal on that point and adding more samples in the nearby region could improve the accuracy of the GP model. As a result, it is reasonable to select the next sample where ESELOO is maximum. However, such quantity is only known at the experimental design and needs to be estimated at unobserved points. To do this, a second GP is fitted to the ESELOOs and where the maximum of the modified expected improvement (EI) criterion occurs is chosen as the next sample. EI is a popular acquisition function in Bayesian optimisation and is used to trade-off between local/global search. However, it has tendency towards exploitation, meaning that its maximum is close to the (current) "best" sample. To avoid clustering, a modified version of EI, called pseudo expected improvement, is employed which is more explorative than EI and allows us to discover unexplored regions. The results show that the proposed sampling method is promising.
1st Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
• Speaker: Adam Foster (University of Oxford, UK)
• Abstract: hide / show
Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, its applicability is hampered by the difficulty of obtaining accurate estimates of the expected information gain (EIG) of an experiment. To address this, we introduce several classes of fast EIG estimators by building on ideas from amortized variational inference. We go on to propose a fully stochastic gradient based approach to BOED. This utilizes variational lower bounds on the EIG of an experiment that can be simultaneously optimized with respect to both the variational and design parameters. This allows the design process to be carried out through a single unified stochastic gradient ascent procedure, in contrast to existing approaches that typically construct a pointwise EIG estimator, before passing this estimator to a separate optimizer.
8th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Optimal Thinning of MCMC Output
• Speaker: Marina Riabiz (King’s College London / Alan Turing Institute, UK)
• Abstract: hide / show
The use of heuristics to assess the convergence and compress the output of Markov chain Monte Carlo can be sub-optimal in terms of the empirical approximations that are produced. Typically a number of the initial states are attributed to “burn in” andremoved, whilst the chain can be “thinned” if compression is also required. In this paper we consider the problem of selecting a subset of states, of fixed cardinality, such that the approximation provided by their empirical distribution is close to optimal. A novel method is proposed, based on greedy minimisation of a kernel Stein discrepancy, that is suitable for problems where heavy compression is required. Theoretical results guarantee consistency of the method and its effectiveness is demonstrated in the challenging context of parameter inference for ordinary differential equations. Software is available in the Stein Thinning package in both Python and MATLAB, and example code is included.
15th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Manifold lifting: scaling MCMC to the vanishing noise regime
• Speaker: Matthew Graham (Newcastle University / Alan Turing Institute, UK)
• Abstract: hide / show
Standard Markov chain Monte Carlo methods struggle to explore distributions that are concentrated in the neighbourhood of low-dimensional structures. These pathologies naturally occur in a number of situations. For example, they are common to Bayesian inverse problem modelling and Bayesian neural networks, when observational data are highly informative, or when a subset of the statistical parameters of interest are non-identifiable. In this talk, I will discuss a strategy that transforms the original sampling problem into the task of exploring a distribution supported on a manifold embedded in a higher dimensional space; in contrast to the original posterior this lifted distribution remains diffuse in the vanishing noise limit. A constrained Hamiltonian Monte Carlo method which exploits the manifold geometry of this lifted distribution, is then used to perform efficient approximate inference. We demonstrate in several numerical experiments that, contrarily to competing approaches, the sampling efficiency of our proposed methodology does not degenerate as the target distribution to be explored concentrates near low dimensional structures. Joint work with Khai Xiang Au and Alex Thiery at National University of Singapore.
22nd Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Statistical Face of a Region Under Monsoon Rainfall in Eastern India
• Speaker: Kaushik Jana (Imperial College London / Alan Turing Institute, UK)
• Abstract: hide / show
A region under rainfall is a contiguous spatial area receiving positive precipitation at a particular time. The probabilistic behavior of such a region is an issue of interest in meteorological studies. A region under rainfall can be viewed as a shape object of a special kind, where scale and rotational invariance are not necessarily desirable attributes of a mathematical representation. For modeling variation in objects of this type, we propose an approximation of the boundary that can be represented as a real valued function, and arrive at further approximation through functional principal component analysis, after suitable adjustment for asymmetry and incompleteness in the data. The analysis of an open access satellite dataset on monsoon precipitation over Eastern Indian subcontinent leads to explanation of most of the variation in shapes of the regions under rainfall through a handful of interpretable functions that can be further approximated parametrically. The most important aspect of shape is found to be the size followed by contraction/elongation, mostly along two pairs of orthogonal axes. The different modes of variation are remarkably stable across calendar years and across different thresholds for minimum size of the region.
29th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Distribution Regression for Continuous-Time Processes via the Expected Signature
• Speaker: Maud Lemercier (University of Warwick / Alan Turing Institute, UK)
• Abstract: hide / show
We introduce a learning framework to infer macroscopic properties of an evolving system from longitudinal trajectories of its components. By considering probability measures on continuous paths we view this problem as a distribution regression task for continuous-time processes and propose two distinct solutions leveraging the recently established properties of the expected signature. Firstly, we embed the measures in a Hilbert space, enabling the application of an existing kernel-based technique. Secondly, we recast the complex task of learning a non-linear regression function on probability measures to a simpler functional linear regression on the signature of a single vector-valued path. We provide theoretical results on the universality of both approaches, and demonstrate empirically their robustness to densely and irregularly sampled multivariate time-series, outperforming existing methods adapted to this task on both synthetic and real-world examples from thermodynamics, mathematical finance and agricultural science.
5th Wednesday 10:30 - 12:00 / 13:00 - 14:30 @ Zoom (online)
• Special Session: Probabilistic Numerical and Kernel-Based Methods
• Chair: Toni Karvonen and Jon Cockayne (Alan Turing Institute, UK)
• Abstract: hide / show
Probabilistic numerical methods (PNMs) are a class of numerical methods that use ideas from probability and statistics in their construction. Since their inception there has been a focus on the application of these methods to integration, to quantify uncertainty in the value of the integral or for other aspects of algorithm design. Most PNMs for integration are based on Gaussian process regression and as such are closely related to kernel-based interpolation and worst-case optimal approximation in the reproducing kernel Hilbert space of the covariance kernel. This session will present recent advances from the literature on PNMs and kernel-based methods for integration by several speakers.
10:30 - 10:55 (Talk #1)
• Topic: Kernel Methods, Gaussian Processes and Uncertainty Quantification for Bayesian Quadrature
• Speaker: Toni Karvonen (Alan Turing Institute, UK)
• Abstract: hide / show
This talk discusses the equivalence of kernel-based approximation and Gaussian process (GP) regression, in particular how Bayesian quadrature rules can be viewed both as conditional Gaussian distributions over integrals and worst-case optimal integration rules in the reproducing kernel Hilbert space of the covariance kernel of the GP prior. These equivalences are used to prove results about uncertainty quantification properties of Bayesian quadrature rules when a covariance scaling parameter is estimated from data using maximum likelihood. It is shown that for a variety of kernels and fixed and deterministic data-generating functions Bayesian quadrature rules can become at most “slowly” overconfident in that their conditional standard deviations decay at most with a rate O(N^−1/2) (up to logarithmic factors) faster than the true integration error, where N is the number of integration points. The latter part of the talk is based on recent work by Karvonen, Wynne, Tronarp, Oates, and Särkkä [1].[1] T. Karvonen, G. Wynne, F. Tronarp, C. J. Oates, and S. Särkkä. Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions. SIAM/ASA Journal on Uncertainty Quantification, 2020. To appear.
11:00 - 11:25 (Talk #2)
• Topic: Generation of Point Sets by Global Optimization for Kernel-Based Numerical Integration
• Speaker: Ken’ichiro Tanaka (The University of Tokyo, Japan)
• Abstract: hide / show
We propose methods for generating nodes (point sets) for Bayesian quadrature. Finding good nodes for Bayesian quadrature has been an important problem. To address this problem, we consider the Gaussian kernel and truncate its expansion to provide tractable optimization problems generating nodes. 1. First, we begin with the 1-dimensional case (d = 1). (a) In this case, we use the technique proposed in [1] generating nodes for approximating functions. The negative logarithm of the determinant of the truncated kernel matrix becomes a logarithmic energy with an external field, which is a convex function with respect to the nodes. The nodes given by its minimizer are called approximate Fekete points. Since this technique yields a convex optimization problem with respect to the nodes, we can generate effectively them. (b) We use the nodes for Bayesian quadrature and observe their good properties via numerical experiments. 2. Second, we consider the higher-dimensional cases (d ≥ 2). (a) In these cases, we have not obtained a concise expression of the logarithmic energy as opposed to the 1-dimensional case. (b) Therefore we directly deal with the approximated determinant given by the truncation of the Gaussian kernel in this article. By numerical experiments, we can observe that higher-dimensional approximate Fekete points are found by minimizing this determinantal logarithmic energy, although there is no mathematical guarantee that this is always the case. We observe similar good properties of the nodes to the 1-dimensional case.[1] T. Karvonen, S. Särkkä, and K. Tanaka: Kernel-based interpolation at approximate Fekete points, Numerical Algorithms (2020). https://doi.org/10.1007/s11075-020-00973-y
11:30 - 11:55 (Talk #3)
• Topic: Design of Computer Experiments based on Bayesian Quadrature
• Speaker: Luc Pronzato (Université Côte d'Azur, France)
• Abstract: hide / show
A standard objective in computer experiments is to predict/interpolate the behaviour of an unknown function f on a compact domain from a few evaluations inside the domain. When little is known about the function, space-filling design is advisable: typically, points of evaluation spread out across the available space are obtained by minimizing a geometrical criterion such as the covering or packing radius, or a discrepancy criterion measuring distance to uniformity. Sequential constructions, for which design points are added one at a time, are of particular interest. Our work is motivated by recent results [2] indicating that the sequence of design points generated by a vertex-direction algorithm applied to the minimization of a convex functional of a design measure can have better space filling properties than points generated by the greedy minimization of a supermodular set function. The presentation is based on the survey [3] and builds on several recent results [1, 4, 5] that show how energy functionals can be used to measure distance to uniformity.[1] S.B. Damelin, F.J. Hickernell, D.L. Ragozin, and X. Zeng. On energy, discrepancy and group invariant measures on measurable subsets of Euclidean space. J. Fourier Anal. Appl., 16:813–839, 2010. [2] L. Pronzato and A.A. Zhigljavsky. Measures minimizing regularized dispersion. J. Scientific Computing, 78(3):1550–1570, 2019. [3] L. Pronzato and A.A. Zhigljavsky. Bayesian quadrature, energy minimization and space-filling design. SIAM/ASA J. Uncertainty Quantification, 2020. (to appear) arXiv preprint arXiv:1808.10722, HAL preprint hal-01864076. [4] S. Sejdinovic, B. Sriperumbudur, A. Gretton, and K. Fukumizu. Equivalence of distance-based and RKHS-based statistics in hypothesis testing. The Annals of Statistics, 41(5):2263–2291, 2013. [5] B.K. Sriperumbudur, A. Gretton, K. Fukumizu, B. Schölkopf, and G.R.G. Lanckriet. Hilbert space embeddings and metrics on probability measures. Journal of Machine Learning Research, 11:1517–1561, 2010.
Lunch Break
13:00 - 13:25 (Talk #4)
• Topic: Quadrature of Bayesian Neural Networks
• Speaker: Takuo Matsubara (Newcastle University / Alan Turing Institute, UK)
• Abstract: hide / show
A mathematical theory called the ridgelet transform [1, 2], which has been developed in the context of harmonic analysis of two layer neural networks, enables constructing a neural network by quadrature methods. The construction via quadrature methods is advantageous not only for the priori convergence analysis of neural networks but also for applications such as a parameter initialisation. Probabilistic numerics [3] aiming to establish a better estimation and a probabilistic interpretation of numerical methods is directly applicable for this construction in order to pursue the better accuracy and efficiency. Probabilistic numerics consider numerical methods as statistical inference and hence itself has an aspect as learning machines. In this talk, we will discuss the intriguing case where probabilistic numerical methods are applied to obtain a learning algorithm.[1] E. Candès Ridgelets: Theory and Applications Doctoral Dissertation, Stanford University, 1998. [2] N. Murata An integral representation of functions using three-layered networks and their approximation bounds Neural Networks 9 (6): 947–956, 1996. [3] P. Hennig, M. Osborne, M. Girolami Probabilistic Numerics and Uncertainty in Computations Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 471 (2179), 2015.
13:30 - 13:55 (Talk #5)
• Topic: On the Positivity of Bayesian Quadrature Weights
• Speaker: Motonobu Kanagawa (EURECOM, France)
• Abstract: hide / show
In this talk I discuss the properties of Bayesian quadrature weights, which strongly affect stability and robustness of the quadrature rule. Specifically, I talk about conditions that are needed to guarantee that the weights are positive. It is shown that the weights are positive in the univariate case if the design points locally minimise the posterior integral variance and the covariance kernel is totally positive (e.g., Gaussian and Hardy kernels). This suggests that gradient-based optimisation of design points may be effective in constructing stable and robust Bayesian quadrature rules. Numerical experiments demonstrate that significant generalisations and improvements appear to be possible, manifesting the need for further research.
14:00 - 14:25 (Talk #6)
• Topic: Variations around kernel quadrature with DPPs
• Speaker: Ayoub Belhadji (Ecole Centrale de Lille, France)
• Abstract: hide / show
Determinantal Point Processes (DPP) are probabilistic models of negatively dependent random variables that arise in theoretical quantum optics and random matrix theory. We study quadrature rules, for smooth functions living in a reproducing kernel Hilbert space, using random nodes that follow the distribution of a DPP [1] or a mixture of DPPs [2]. The definition of these DPPs is tailored to the RKHS so that the corresponding quadratures converge at fast rates that depend on the eigenvalues of the corresponding integration operator. This unified analysis gives new insights on the experimental design of kernel-based quadrature rules.[1] A. Belhadji, R. Bardenet, P. Chainais Kernel quadrature using DPPs In Advances in Neural Information Processing Systems 32, pages 12927-12937, 2019. [2] A. Belhadji, R. Bardenet, P. Chainais Kernel interpolation with continuous volume sampling International Conference on Machine Learning, 2020.
9th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Parametric estimation via MMD optimization: robustness to outliers and dependence
• Speaker: Pierre Alquier (RIKEN Center for Advanced Intelligence Project, Japan)
• Abstract: hide / show
In this talk, I will study the properties of parametric estimators based on the Maximum Mean Discrepancy (MMD) defined by Briol et al. (2019). In a first time, I will show thatthese estimators are universal in the i.i.d setting: even in case of misspecification, they converge to the best approximation of the distribution of the data in the model, without ANY assumption on this model. This leads to very strong robustness properties. In a second time,I will show that these results remain valid when the data is not independent, but satisfy instead a weak-dependence condition. This condition is based on a new dependence coefficient, which is itself defined thanks to the MMD. I will show through examples that this new notion of dependence is actually quite general. This talk is based on published works,and works in progress, with Badr-Eddine Chérief Abdellatif (ENSAE Paris), Mathieu Gerber(University of Bristol), Jean-David Fermanian (ENSAE Paris) and Alexis Derumigny (Uni-versity of Twente):[1] http://arxiv.org/abs/1912.05737 [2] http://proceedings.mlr.press/v118/cherief-abdellatif20a.html [3] http://arxiv.org/abs/2006.00840
16th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Noise contrastive estimation: Asymptotic properties, formal comparison with MC-MLE
• Speaker: Lionel Riou-Durand (University of Warwick, UK)
• Abstract: hide / show
A statistical model is said to be un-normalised when its likelihood function involves an intractable normalising constant. Two popular methods for parameter inference for these models are MC-MLE (Monte Carlo maximum likelihood estimation), and NCE (noise contrastive estimation); both methods rely on simulating artificial data-points to approximate the normalising constant. While the asymptotics of MC-MLE have been established under general hypotheses (Geyer, 1994), this is not so for NCE. We establish consistency and asymptotic normality of NCE estimators under mild assumptions. We compare NCE and MC-MLE under several asymptotic regimes. In particular, we show that, when m→∞ while n is fixed (m and n being respectively the number of artificial data-points, and actual data-points), the two estimators are asymptotically equivalent. Conversely, we prove that, when the artificial data-points are IID, and when n→∞ while m/n converges to a positive constant, the asymptotic variance of a NCE estimator is always smaller than the asymptotic variance of the corresponding MC-MLE estimator. We illustrate the variance reduction brought by NCE through a numerical study.https://projecteuclid.org/euclid.ejs/1539741651
23th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Bayesian Ancestral Reconstruction for Bat Echolocation
• Speaker: Joe Meagher (University College Dublin, Ireland)
• Abstract: hide / show
Ancestral reconstruction can be understood as an interpolation between measured characteristics of existing populations to those of their common ancestors. Doing so provides an insight into the characteristics of organisms that lived millions of years ago. Such reconstructions are inherently uncertain, making this anideal application area for Bayesian statistics. As such, Gaussian processes serve as a basis for many probabilistic models for trait evolution, which assume that measured characteristics, or some transformation of those characteristics, are jointly Gaussian distributed. While these models do provide a theoretical basis for uncertainty quantification in ancestral reconstruction, practical approaches to their implementation have proven challenging. Here, I present a flexible Bayesian approach to ancestral reconstruction, applied to bat echolocation calls. This represents a fully Bayesian approach to inference within the Phylogenetic Gaussian Process Regression framework for Function-Valued Traits, producing an ancestral reconstruction for which any uncertainty in this model may be quantified. The framework is generalised to collections of discrete and continuous traits, based on an efficient approximate Bayesian inference scheme. This efficient approach is then applied to the reconstruction of batecholocation calls, providing new insights into the developmental pathways of this remarkable characteristic. It is the complexity of bat echolocation that motivates the proposed approach to evolutionary inference, however, the resulting statistical methods are broadly applicable within the field of Evolutionary Biology.
30th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: A Theoretical Study of Variational Inference
• Speaker: Badr-Eddine Chérief-Abdellatif (University of Oxford, UK)
• Abstract: hide / show
Bayesian inference provides an attractive learning framework to analyze and to sequentially update knowledge on streaming data, but is rarely computationally feasible in practice. In the recent years, variational inference (VI) has become more and more popular for approximating intractable posterior distributions in Bayesian statistics and machine learning. Nevertheless, despite promising results in real-life applications, only little attention has been put in the literature towards the theoretical properties of VI. In this talk, we aim to present some recent advances in theory of VI. We will show that VI is consistent under mild conditions and retains the same properties than exact Bayesian inference. We will finally illustrate these results with generalization bounds in sparse deep learning.
14th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Bayesian Probabilistic Numerical Integration with Tree-Based Models
• Speaker: Harrison Zhu (Imperial College London, UK)
• Abstract: hide / show
Bayesian quadrature (BQ) is a method for solving numerical integration problems in a Bayesian manner, which allows user to quantify their uncertainty about the solution. The standard approach to BQ is based on Gaussian process (GP) approximation of the integrand. As a result, BQ approach is inherently limited to cases where GP approximations can be done in an efficient manner, thus often prohibiting high-dimensional or non-smooth target functions. This paper proposes to tackle this issue with a new Bayesian numerical integration algorithm based on Bayesian Additive Regression Trees (BART) priors, which we call BART-Int. BART priors are easy to tune and well-suited for discontinuous functions. We demonstrate that they also lend themselves naturally to a sequential design setting and that explicit convergence rates can be obtained in a variety of settings. The advantages and disadvantages of this new methodology are highlighted on a set of benchmark tests including the Genz functions, and on a Bayesian survey design problem.
21st Wednesday 15:00 - 16:00 @ Zoom (online)
• Topic: A hierarchical expected improvement method for Bayesian optimization
• Speaker: Simon Mak (Duke University, USA)
• Abstract: hide / show
The Expected Improvement (EI) method, proposed by Jones et al. (1998), is a widely-used Bayesian optimization method which makes use of a Gaussian process surrogate model. However, one drawback of EI is that it is overly greedy in exploiting the fitted model for optimization, which results in suboptimal solutions even for large sample sizes. To address this, we propose a new hierarchical EI (HEI) framework, which makes use of a hierarchical Gaussian process model. HEI preserves a closed-form acquisition function, and corrects the over-greediness of EI by encouraging exploration of the optimization space. Under certain prior specifications, we prove the global convergence of HEI over a broad function space, and derive global convergence rates under smoothness assumptions. We then introduce several hyperparameter estimation methods, which allow HEI to mimic a fully Bayesian optimization procedure while avoiding expensive Markov-chain Monte Carlo sampling. Numerical experiments show the improvement of HEI over existing Bayesian optimization methods, for synthetic functions as well as a semiconductor manufacturing optimization problem.
28th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Specifying Priors on Predictive Complexity
• Speaker: Eric Nalisnick (University of Amsterdam, Netherlands)
• Abstract: hide / show
Specifying a Bayesian prior is notoriously difficult for complex models such as neural networks. Reasoning about parameters is made challenging by the high-dimensionality and over-parameterization of the space. Priors that seem benign and uninformative can have unintuitive and detrimental effects on a model's predictions. For this reason, we propose predictive complexity priors: a functional prior that is defined by comparing the model's predictions to those of a reference model. Although originally defined on the model outputs, we transfer the prior to the model parameters via a change of variables. The traditional Bayesian workflow can then proceed as usual. We apply our predictive complexity prior to modern machine learning tasks such as reasoning over neural network depth and sharing of statistical strength for few-shot learning.
4th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks
• Speaker: Takuo Matsubara (Newcastle University / The Alan Turing Institute, UK)
• Abstract: hide / show
Bayesian neural networks attempt to combine the strong predictive performance of neural networks with formal quantification of uncertainty associated with the predictive output in the Bayesian framework. However, it remains unclear how to endow the parameters of the network with a prior distribution that is meaningful when lifted into the output space of the network. A possible solution is proposed that enables the user to posit an appropriate covariance function for the task at hand. Our approach constructs a prior distribution for the parameters of the network, called a ridgelet prior, that approximates the posited covariance structure in the output space of the network. The approach is rooted in the ridgelet transform and we establish both finite-sample-size error bounds and the consistency of the approximation of the covariance function in a limit where the number of hidden units is increased. Our experimental assessment is limited to a proof-of-concept, where we demonstrate that the ridgelet prior can out-perform an unstructured prior on regression problems for which an informative covariance function can be a priori provided.
18th Wednesday 14:00 - 15:00 @ Zoom (online)
• Topic: On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient Flow
• Speaker: Youssef Mroueh (IBM Research, USA)
• Abstract: hide / show
We consider the maximum mean discrepancy (MMD) GAN problem and propose a parametric kernelized gradient flow that mimics the minmax game in gradient regularized MMD GAN. We show that this flow provides a descent direction minimizing the MMD on a statistical manifold of probability distributions. We then derive an explicit condition which ensures that gradient descent on the parameter space of the generator in gradient regularized MMD GAN is globally convergent to the target distribution. Under this condition, we give non asymptoticconvergence results of gradient descent in MMD GAN. Another contribution of this paper is the introduction of a dynamic formulation of a regularization of MMD and demonstrating that the parametric kernelized descent for MMD is the gradient flow of this functional with respect to the new Riemannian structure. Our obtained theoretical result allows ones to treat gradient flows for quite general functionals and thus has potential applications to other types of variational inferences on a statistical manifold beyond GANs. Finally, numerical experiments suggest that our parametric kernelized gradient flow stabilizes GAN training and guarantees convergence.
24th Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Learning under Model Misspecification: Applications to Variational and Ensemble methods
• Speaker: Andrés Masegosa (University of Almería, Spain)
• Abstract: hide / show
Virtually any model we use in machine learning to make predictions does not perfectly represent reality. So, most of the learning happens under model misspeci- fication. In this work, we present a novel analysis of the generalization performance of Bayesian model averaging under model misspecification and i.i.d. data using a new family of second-order PAC-Bayes bounds. This analysis shows, in simple and intuitive terms, that Bayesian model averaging provides suboptimal generalization performance when the model is misspecified. In consequence, we provide strong theoretical arguments showing that Bayesian methods are not optimal for learning predictive models, unless the model class is perfectly specified. Using novel second- order PAC-Bayes bounds, we derive a new family of Bayesian-like algorithms, which can be implemented as variational and ensemble methods. The output of these algorithms is a new posterior distribution, different from the Bayesian pos- terior, which induces a posterior predictive distribution with better generalization performance. Experiments with Bayesian neural networks illustrate these findings.
2nd Wednesday 11:00 - 12:00 @ Zoom (online)
• Topic: Measure Transport with Kernel Stein Discrepancy
• Speaker: Matthew Fisher (Newcastle University, UK)
• Abstract: hide / show
Measure transport underpins several recent algorithms for posterior approximation in the Bayesian context, wherein a transport map is sought to minimise the Kullback Leibler divergence (KLD) from the posterior to the approximation. The KLD is a strong mode of convergence, requiring absolute continuity of measures and placing restrictions on which transport maps can be permitted. Here we propose to minimise a kernel Stein discrepancy (KSD) instead, requiring only that the set of transport maps is dense in an L2 sense and demonstrating how this condition can be validated. The consistency of the associated posterior approximation is established and empirical results suggest that KSD is competitive and more flexible alternative to KLD for measure transport.
All talk schedules for this year is finished. We hope to see you next year.
6th February 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Stochastic Gradient Descent
• Speaker: Marina Riabiz
• Reference:
- Leon Bottou, Frank E. Curtis, Jorge Nocedal, Optimization Methods for Large-Scale Machine Learning, https://arxiv.org/pdf/1606.04838.pdf#page21
13th February 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Proof of convergence rate of Stochastic Gradient Descent
• Speaker: Ömer Deniz Akyıldız
• Reference:
20th February 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Proof of convergence rate of Stochastic Gradient Descent
• Speaker: Ömer Deniz Akyıldız
• Reference:
- Robert M. Gower, Convergence Theorems for Gradient Descent, https://perso.telecom-paristech.fr/rgower/pdf/M2_statistique_optimisation/grad_conv.pdf
- Ji Liu, Stochastic Gradient ‘Descent’ Algorithm, https://www.cs.rochester.edu/u/jliu/CSC-576/class-note-10.pdf
27th February 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Stochastic Gradient Langevin Dynamics
• Speaker: Andrew Duncan
• Reference:
- Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky, Non-Convex Learning via Stochastic Gradient Langevin Dynamics: A Nonasymptotic Analysis, https://arxiv.org/pdf/1702.03849.pdf
6th March 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Conjugate Gradient Methods
• Speaker: Taha Ceriti
• Reference:
- Chris Bishop, Neural Networks for Pattern Recognition, Chapter 7.
- J.R. Shewchuk, An Introduction to the Conjugate Gradient Method Without the Agonizing Pain, 1994, https://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf
(Extra Reference)
- S. Bubeck, Convex Optimization: Algorithms and Complexity. In Foundations and Trends in Machine Learning, Vol. 8: No. 3-4, pp 231-357, 2015. http://sbubeck.com/Bubeck15.pdf
- Nagapetyan et al., The True Cost of SGLD, https://arxiv.org/pdf/1706.02692.pdf
- Brosse at al, The promises and pitfalls of Stochastic Gradient Langevin Dynamics, https://arxiv.org/pdf/1811.10072.pdf
- Dalalyan and Karagulyan, User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient, https://arxiv.org/pdf/1710.00095.pdf
- Vollmer et al., Exploration of the (Non-)asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics, https://arxiv.org/pdf/1501.00438.pdf
13th March 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Hyperparameter estimation for Gaussian Processes
• Speaker: Alex Diaz
• Reference:
- Rassmussen, C.E., Gaussian Processes for Machine Learning (Ch2 and Ch 5) http://www.gaussianprocess.org/gpml/chapters/RW.pdf
- DiazDelaO, F.A. et al. (2017) Bayesian updating and model class selection with Subset Simulation https://www.sciencedirect.com/science/article/pii/S0045782516308283
- Garbuno-Inigo, A. et al. (2016) Gaussian process hyper-parameter estimation using Parallel Asymptotically Independent Markov Sampling https://www.sciencedirect.com/science/article/pii/S0167947316301311
- Garbuno-Inigo, A. et al. (2016) Transitional annealed adaptive slice sampling for Gaussian process hyper-parameter estimation http://www.dl.begellhouse.com/journals/52034eb04b657aea,55c0c92f02169163,2f3449c01b48b322.html
20th March 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Gaussian Interpolation/Regression Error Bounds
• Speaker: George Wynne
• Reference:
- Holger Wendland, Christien Rieger, Approximate Interpolation with Applications to Selecting Smoothing Parameters https://link.springer.com/article/10.1007/s00211-005-0637-y
27th March 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Structure of the Gaussian RKHS
• Speaker: Toni Karvonen
• Reference:
- Ha Quang Minh, Some Properties of Gaussian Reproducing Kernel Hilbert Spaces and Their Implications for Function Approximation and Learning Theory, https://link.springer.com/article/10.1007/s00365-009-9080-0
(Extra reference)
- Steinwart, I., Hush, D., & Scovel, C. (2006). An explicit description of the reproducing kernel Hilbert spaces of Gaussian RBF kernels. IEEE Transactions on Information Theory, 52(10), 4635–4643
03th April 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Bayesian synthetic likelihood
• Speaker: Leah South
• Reference:
- L. F. Price, C. C. Drovandi, A. Lee & D. J. Nott, Bayesian Synthetic Likelihood, https://www.tandfonline.com/doi/abs/10.1080/10618600.2017.1302882
12th April 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Deep Gaussian Processes
• Speaker: Kangrui Wang
• Reference:
- Damianou A, Lawrence N. Deep Gaussian processes http://proceedings.mlr.press/v31/damianou13a.pdf
- Dunlop M M, Girolami M A, Stuart A M, et al. How deep are deep Gaussian processes? http://www.jmlr.org/papers/volume19/18-015/18-015.pdf
- Bauer M, van der Wilk M, Rasmussen C E. Understanding probabilistic sparse Gaussian process approximationshttps://arxiv.org/abs/1606.04820
17th April 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Multi Level Monte Carlo
• Speaker: Alastair Gregory
• Reference:
- http://people.maths.ox.ac.uk/gilesm/files/cgst.pdf
- https://people.maths.ox.ac.uk/gilesm/files/OPRE_2008.pdf
24th April 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Adaptive Bayesian Quadrature
• Speaker: Matthew Fisher
• Reference:
01st May 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Controlling Convergence with Maximum Mean Discrepancy
• Speaker: Chris Oates
• Reference:
- Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B. and Smola, A.J., 2007. A kernel method for the two-sample-problem. http://papers.nips.cc/paper/3110-a-kernel-method-for-the-two-sample-problem.pdf
08th May 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Introduction to Stein’s method
• Speaker: FX Briol
• Reference:
- Talk will cover Chapter 2 of “Chen, L. H. Y., Goldstein, L., & Shao, Q.-M. (2011). Normal Approximation by Stein’s Method. Springer.”
- Gorham, J., Duncan, A., Mackey, L., & Vollmer, S. (2016). Measuring Sample Quality with Diffusions. ArXiv:1506.03039.
- Gorham, J., & Mackey, L. (2017). Measuring Sample Quality with Kernels. In Proceedings of the International Conference on Machine Learning (pp. 1292–1301).
15th May 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Bochner's Theorem and Maximum Mean Discrepancy
• Speaker: George Wynne
• Reference:
- Theorem 9 in http://www.jmlr.org/papers/volume11/sriperumbudur10a/sriperumbudur10a.pdf is the result of Bochner's theorem being used in MMD
- http://www.math.nus.edu.sg/~matsr/ProbI/Lecture7.pdf is a source for proof of Bochner's theorem
(Extra Reference)
- https://sites.google.com/site/steinsmethod/home
19th June 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Convergence Guarantees for Adaptive Bayesian Quadrature Methods
• Speaker: Motonobu Kanagawa
• Reference:
- Kanagawa, M. and Hennig, P, 2019. Convergence Guarantees for Adaptive Bayesian Quadrature Methods. https://arxiv.org/abs/1905.10271
26th June 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Using machine learning to predict and understand turbulence modelling uncertainties
• Speaker: Ashley Scillitoe
• Reference:
03rd July 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Polynomial approximations in uncertainty quantification
• Speaker: Pranay Seshadri
• Reference:
- Talk will cover Xiu D (2010) “Numerical methods for stochastic computations: a spectral method approach”. Princeton University Press.
10th July 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: A Kernel Stein Test for Comparing Latent Variable Models
• Speaker: Heishiro Kanagawa
• Reference:
- Kanagawa, H., Jitkrittum, W., Mackey, L., Fukumizu, K., Gretton, A. (2019) A Kernel Stein Test for Comparing Latent Variable Models. https://arxiv.org/abs/1907.00586
17th July 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Multi-resolution Multi-task Gaussian Processes
• Speaker: Oliver Hamelijnck
• Reference:
- Hamelijnck, O., Damoulas, T., Wang, K., Girolami, M. (2019) Multi-resolution Multi-task Gaussian Processes. https://arxiv.org/pdf/1906.08344.pdf
24th July 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: A Primer on PAC Bayesian Learning
• Speaker: Benjamin Guedj
• Reference:
- Talk will cover this tutorial from ICML 2019
31th July 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: An Introduction to Measure Transport
• Speaker: Chris Oates
• Reference:
06th August 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Gradient Flows for Statistical Computation
• Speaker: Marina Riabiz
• Reference:
- Daneri and Savare', Lecture Notes on Gradient Flows and Optimal Transport, https://arxiv.org/abs/1009.3737
- N. Garcia Trillos, D. Sanz-Alonso, The Bayesian update: variational formulations and gradient flows, https://arxiv.org/abs/1705.07382
- G.Peyre' and M. Cuturi, Computational Optimal Transport, Chapter 9.3 https://arxiv.org/pdf/1803.00567.pdf
- Carrillo, Craig, Patacchini, A blob method for diffusion, https://arxiv.org/abs/1709.09195
- Sides http://web.math.ucsb.edu/~kcraig/math/curriculum_vitae_files/NIPS_120917.pdf
14th August 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: The Mathematics of Gradient Flows
• Speaker: Andrew Duncan
• Reference:
14th August 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Displacement Convexity and Implications for Variational Inference
• Speaker: Andrew Duncan
• Reference:
23th August 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Comparing spatial models in the presence of spatial smoothing
• Speaker: Earl Duncan
• Reference:
28th August 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Fisher efficient inference of intractable models
• Speaker: Song Liu
• Reference:
- Liu, S., Kanamori, T., Jitkrittum, W., & Chen, Y. (2018). Fisher efficient inference of intractable models. https://arxiv.org/abs/1805.07454
04th September 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Statistical Inference for Generative Models with Maximum Mean Discrepancy
• Speaker: FX Briol
• Reference:
- Briol, F-X, Barp, A., Duncan, A. B., Girolami, M. (2019) Statistical inference for generative models with maximum mean discrepancy. https://arxiv.org/abs/1906.05944
11th September 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: A New Approach to Probabilistic Rounding Error Analysis
• Speaker: Jon Cockayne
• Reference:
- Talk will cover Higham, N., Mary, T. (2018) A new approach to probabilistic rounding analysis. http://eprints.maths.manchester.ac.uk/2673/1/paper.pdf
25th September 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Model Inference for Ordinary Differential Equations by Parametric Polynomial Kernel Regression
• Speaker: David Green
• Reference:
02nd October 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: The Ridgelet Transform and a Quadrature of Neural Networks
• Speaker: Takuo Matsubara
• Reference:
09th October 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Hierarchical and multivariate Gaussian processes for environmental and ecological applications
• Speaker: Jarno Vanhatalo
• Reference:
16th October 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: TBA
• Speaker: Omar Rivasplata
• Reference:
23th October 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Learning Laws of Stochastic Processes
• Speaker: Harald Oberhauser
• Reference:
- Chevyrev, I., & Oberhauser, H. (2018). Signature moments to characterize laws of stochastic processes. arXiv:1810.10971.
29th October 11:00 - 12:00 @ Mary Shelley Room at ATI
- 10:00 - 10:15. Chris Oates. Welcome, and Some Open Problems in Probabilistic Numerics.
- 10:15 - 10:30. Francois-Xavier Briol. Probabilistic Numerics via Transfer Learning.
- 10:30 - 10:45. Toni Karvonen. Uncertainty quantification with Gaussian processes and when to trust a PN method.
- 10:45 - 11:00. Simo Särkkä. Numerical Integration as a Finite Matrix Approximation to Multiplication Operator.
- 11:30 - 11:45. Motonobu Kanagawa. Open questions regarding adaptive quadrature methods.
- 11:45 - 12:00. George Wynne. Gaussian process error bounds from a sampling inequality.
- 14:00 - 14:15. Peter Hristov. Surrogate modelling with probabilistic numerics.
- 14:15 - 14:30. Filip Tronarp. On Gaussian Filtering/Smoothing for Solving ODEs.
- 14:30 - 14:45. Takuo Matsubara. Bayesian quadrature of neural networks based on the Ridgelet transform.
- 15:30 - 15:45. Alex Diaz. PN for eigenvalue problems.
- 15:45 - 16:00. Maren Mahsereci. Software for PN.
30th October 11:00 - 12:00 @ Mary Shelley Room at ATI
- 10:00 - 10:15. Alex Gessner. Acquisition functions for adaptive Bayesian quadrature.
- 10:15 - 10:30. Mark Girolami. Title TBC.
- 10:30 - 10:45. Takeru Matsuda. ODE parameter estimation with discretization error quantification.
- 11:30 - 11:45. Matthew Fisher. Locally Adaptive Bayesian Cubature.
- 11:45 - 12:00. Daniel Tait. Constrained VI for inverse problems.
- 14:00 - 14:15. Peter Hristov. A cross-platform implementation of BayesCG (live demo).
- 14:15 - 14:30. Jon Cockayne. Probabilistic local sensitivity analysis.
- 14:30 - 14:45. O. Deniz Akyildiz. Proximal methods from a probabilistic perspective.
- 15:30 - 15:45. Filip de Roos. Probabilistic linear algebra (active/adaptive or noisy).
- 15:45 - 16:00. Jonathan Wenger. Session Introduction: Ideas for a Probabilistic Numerics Framework.
13th November 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds
• Speaker: Hisham Husain
• Reference:
- https://arxiv.org/pdf/1902.00985
20th November 11:00 - 12:00 @ Mary Shelley Room at ATI
• Topic: Kernelized Wasserstein Natural Gradient
• Speaker: Michael Arbel
• Reference:


This site requires JavaScript to function.
Enable JavaScript.
Called It!
A service intended to prove you knew something was going to happen before it happened.
Probably using some hashing algorithm nobody including me understands.
Coming soon™.


| Current Healthcare Providers | |||||
|---|---|---|---|---|---|
| Name/Facility | Specialty | NPI | Contact Number | Address | |
|
|
lastName+firstName | specialty | NPI | ||
|
|
OfficeName | specialty | NPI |


new Interval(func, delay, lifetime)
Interval.js
Parameters:
| Name | Type | Description |
|---|---|---|
func |
function | The function you want to be called repeatly |
delay |
int | milliseconds that should wait before each call |
lifetime |
int | if set, clearInterval will be called after `lifetime` milliseconds |
- Source:
Methods
-
once()
-
Run func once
- Source:
-
pause()
-
clearInterval without touching lifetime
- Source:
-
resume()
-
setInterval if not set
- Source:
-
start(delay, lifetime)
-
setInterval & setTimeout for clearInterval if lifetime set
Parameters:
Name Type Description delay
int milliseconds that should wait before each call, defaults to this.delay lifetime
int if set, clearInterval will be called after `lifetime` milliseconds, defaults to this.lifetime - Source:
-
stop()
-
clearInterval and clearTimeout for lifetime
- Source:


Snookes1516.github.io
Using Words Phrases to Signal Spatial Temporal. Hibernate Search - Dashboard - JBoss.org Documentation. Space is the boundless three-dimensional extent in which objects and events have relative position and direction. Physical space is often conceived in three linear. Building descriptive essay using spatial order Essay about childhood memories vaccinations collection research paper with outline examples, essay about creative. Art Define Art at Dictionary.com.
Descriptive essay using spatial order. In the Beginning was the Command Line - Inria. The fine arts collectively, often excluding architecture: art and architecture. any field using the skills or techniques of art: advertising art; industrial. Spatial order is one of the organization tools that helps you describe details in your writing. It is considered very logical and helps the reader understand a scene. Problem solving using order of operations - Rapid. The Purdue University Online Writing Lab serves writers from around the world and the Purdue University Writing Lab helps writers on Purdue's campus. A Glorious Mess - New Orleans Magazine Understanding Spatial Order and How to Implement It's the same with writing. With any given group of ideas and details, you might use any of a number of principles of organization, and any one of them would
An essay that takes the reader through a town, stopping at significant points along the way, would be organized using A. chronological order. B. spatial order. Essay Using Spatial Order - buyworkwriteessay.org. MLA Formatting and Style Guide // Purdue Writing.
Principles of Organization - CommNet. Essay topic website my school economics and me essay reflective Critical essay historical higher english example Essay of american revolution flashcards quizlet title. Esri Training - Esri: GIS Mapping Software, Spatial.
What Is Spatial Organization in Writing? Reference.com. Good writers are always looking for ways to improve their writing. One way you can do this is by adding words and phrases that connect your ideas. Looking for a list of interesting narrative essay topics? Look no further! Here is a list of 101 thought provoking essay or short story topics. You'll Oracle Locator vs Oracle Spatial: A Reflection on Oracle. This is our selected set of topics on spatial order. It is also backed up with a spatial order essay example written to give you a better idea of how this subject. Essay using spatial order - Creating odata service using. Research Paper Examples - Free Sample Research Papers. Self-regulating Learning: Impact of Attention, Memory. Bayes' Theorem for the curious and bewildered; an excruciatingly gentle introduction.
But it is their product. Time and competition will change this (PostGIS and SQL Server both provide aggregates for free). He argues that critical theories
Learn the latest GIS technology through free live training seminars, self-paced courses, or classes taught by Esri experts. Resources are available for professionals. Descriptive essay using spatial order . But we will teach students struggle with exponents. Well, addition and division, and solving math problem solving Lpi sat essay help Essay Using Spatial Order help me with my essay government research papers. Hibernate Search, full text search for your entities - Reference Documentation. Twenty sections of 250 study guides and 100 exercises for learners, middle school through returning adult, in 39 languages. PATTERNS OF ORGANIZATION - University of Washington. Spatial Order Essay Example and Topics CustomWritings. By Michael Nielsen. One day in the mid-1920s, a Moscow newspaper reporter named Solomon Shereshevsky entered the laboratory of the psychologist Alexander Luria.
101 Persuasive Essay and Speech Topics By: Mr. Morton. Whether you are a student in need of a persuasive essay topic, or a teacher looking to assign a persuasive.
"In the Beginning was the Command Line" By Neal Stephenson. cryptonomicon.com/beginning.html. About twenty years ago Jobs and Wozniak, the founders.
A reflection on Locator versus Spatial licensing and some suggestions as to how to use “spatial” processing with Locator. The Spatial Companion For Oracle. The Meaning of Spatial Order Explained With Perfect Examples. From the beginning, both sociology and photography worked on a variety of projects. Among these, for both, was the exploration of society.
American Ethnography Quasimonthly. Patterns of Organization. Chronological Patterns ; Sequential Patterns; Spatial Patterns; Compare-Contrast Patterns; Advantages- Disadvantages Patterns. We allow for a certain level of ambiguity when we speak of geographical regions. References to “the South,” “the West” and “the Midwest,” for example. 101 Narrative Essay Topics and Short Story Ideas. Spatial organization is one of the principles of organization in writing that arranges items according to their relationships or physical position. Strictly speaking, the word abstract means to separate or withdraw something from something else. The term can be applied to art that is based an object. Building descriptive essay using spatial order - Šahovski.
Private Eye Field Reports and Lesson Ideas. Persuasive Essay and Speech Topics - Ereading Worksheets. The Private Eye is an interdisciplinary hands-on curriculum using a jeweler's loupe and inquiry method to accelerate creativity, literacy, scientific literacy. Research paper examples are of great value for students who want to complete their assignments timely and efficiently. Our collection of research paper examples. Self-regulating Learning: Impact of Attention, Memory, Language, Spatial and Sequential Ordering, Higher Order Cognition, and Social Cognition.
An essay that takes the reader through a town, stopping. A List of Transition Words — With Examples on how to use these transitional devices in writing to connect one idea with another. Spatial order is important when writing an academic essay. Understanding spatial ordering and simple tips on how to use it in your write. The following overview should help you better understand how to cite sources using MLA eighth edition, including the list of works cited and in-text citations.


python_pachyderm.pfs_client module
Module variables
var BUFFER_SIZE
Classes
class PfsClient
Methods
def __init__(
self, host=None, port=None, auth_token=None)
Creates a client to connect to PFS.
Params:
* host: The pachd host. Default is 'localhost', which is used with
pachctl port-forward
.
* port: The port to connect to. Default is 30650.
* auth_token: The authentication token; used if authentication is
enabled on the cluster. Default to None
.
def commit(
*args, **kwds)
A context manager for running operations within a commit.
Params:
* repo_name: A string specifying the name of the repo.
* branch: A string specifying the branch name. This is a more
convenient way to build linear chains of commits. When a commit is
started with a non-empty branch the value of branch becomes an alias
for the created Commit. This enables a more intuitive access pattern.
When the commit is started on a branch the previous head of the branch
is used as the parent of the commit.
* parent: An optional Commit
object specifying the parent commit.
Upon creation the new commit will appear identical to the parent
commit, data can safely be added to the new commit without affecting
the contents of the parent commit.
* description: An optional string describing the commit.
def copy_file(
self, source_commit, source_path, dest_commit, dest_path, overwrite=None)
Efficiently copies files already in PFS. Note that the destination repo cannot be an output repo, or the copy operation will (as of 1.9.0) silently fail.
Params:
* sourcecommit: A tuple, string, or Commit
object representing the
commit for the source file.
* sourcepath: A string specifying the path of the source file.
* destcommit: A tuple, string, or Commit
object representing the
commit for the destination file.
* destpath: A string specifying the path of the destination file.
* overwrite: Am optional bool specifying whether to overwrite the
destination file if it already exists.
def create_branch(
self, repo_name, branch_name, commit=None, provenance=None)
Creates a new branch.
Params:
* reponame: A string specifying the name of the repo.
* branchname: A string specifying the new branch name.
* commit: An optional tuple, string, or Commit
object representing
the head commit of the branch.
* provenance: An optional iterable of Branch
objects representing
the branch provenance.
def create_repo(
self, repo_name, description=None, update=None)
Creates a new Repo
object in PFS with the given name. Repos are the
top level data object in PFS and should be used to store data of a
similar type. For example rather than having a single Repo
for an
entire project you might have separate Repo
s for logs, metrics,
database dumps etc.
Params: * repo_name: Name of the repo. * description: An optional string describing the repo. * update: Whether to update if the repo already exists.
def delete_all(
self)
Deletes everything.
def delete_all_repos(
self, force=None)
Deletes all repos.
Params: * force: If set to true, the repo will be removed regardless of errors. This argument should be used with care.
def delete_branch(
self, repo_name, branch_name, force=None)
Deletes a branch, but leaves the commits themselves intact. In other words, those commits can still be accessed via commit IDs and other branches they happen to be on.
Params: * reponame: A string specifying the repo name. * branchname: A string specifying the name of the branch to delete. * force: A bool specifying whether to force the branch deletion.
def delete_commit(
self, commit)
Deletes a commit.
Params:
* commit: A tuple, string, or Commit
object representing the commit.
def delete_file(
self, commit, path)
Deletes a file from a Commit. DeleteFile leaves a tombstone in the Commit, assuming the file isn't written to later attempting to get the file from the finished commit will result in not found error. The file will of course remain intact in the Commit's parent.
Params:
* commit: A tuple, string, or Commit
object representing the commit.
* path: The path to the file.
def delete_repo(
self, repo_name, force=None)
Deletes a repo and reclaims the storage space it was using.
Params: * repo_name: The name of the repo. * force: If set to true, the repo will be removed regardless of errors. This argument should be used with care.
def finish_commit(
self, commit, description=None, tree_object_hashes=None, datum_object_hash=None, size_bytes=None, empty=None)
Ends the process of committing data to a Repo and persists the Commit. Once a Commit is finished the data becomes immutable and future attempts to write to it with PutFile will error.
Params:
* commit: A tuple, string, or Commit
object representing the commit.
* description: An optional string describing this commit.
* treeobjecthashes: A list of zero or more strings specifying object
hashes.
* datumobjecthash: An optional string specifying an object hash.
* size_bytes: An optional int.
* empty: An optional bool. If set, the commit will be closed (its
finished
field will be set to the current time) but its tree
will
be left nil.
def flush_commit(
self, commits, repos=None)
Blocks until all of the commits which have a set of commits as provenance have finished. For commits to be considered they must have all of the specified commits as provenance. This in effect waits for all of the jobs that are triggered by a set of commits to complete. It returns an error if any of the commits it's waiting on are cancelled due to one of the jobs encountering an error during runtime. Note that it's never necessary to call FlushCommit to run jobs, they'll run no matter what, FlushCommit just allows you to wait for them to complete and see their output once they do. This returns an iterator of CommitInfo objects.
Yields CommitInfo
objects.
Params:
* commits: A list of tuples, strings, or Commit
objects representing
the commits to flush.
* repos: An optional list of strings specifying repo names. If
specified, only commits within these repos will be flushed.
def get_file(
self, commit, path, offset_bytes=None, size_bytes=None)
Returns an iterator of the contents of a file at a specific commit.
Params:
* commit: A tuple, string, or Commit
object representing the commit.
* path: A string specifying the path of the file.
* offsetbytes: An optional int. Specifies a number of bytes that
should be skipped in the beginning of the file.
* sizebytes: An optional int. limits the total amount of data
returned, note you will get fewer bytes than size if you pass a value
larger than the size of the file. If size is set to 0 then all of the
data will be returned.
def glob_file(
self, commit, pattern)
Lists files that match a glob pattern. Yields FileInfo
objects.
Params:
* commit: A tuple, string, or Commit
object representing the commit.
* pattern: A string representing a glob pattern.
def inspect_branch(
self, repo_name, branch_name)
Inspects a branch. Returns a BranchInfo
object.
def inspect_commit(
self, commit, block_state=None)
Inspects a commit. Returns a CommitInfo
object.
 Returns a FileInfo
object.
Params:
* commit: A tuple, string, or Commit
object representing the commit.
* path: A string specifying the path to the file.
def inspect_repo(
self, repo_name)
Returns info about a specific repo. Returns a RepoInfo
object.
Params: * repo_name: Name of the repo.
def list_branch(
self, repo_name)
Lists the active branch objects on a repo. Returns a list of
BranchInfo
objects.
Params: * repo_name: A string specifying the repo name.
def list_commit(
self, repo_name, to_commit=None, from_commit=None, number=None)
Lists commits. Yields CommitInfo
objects.
Params:
* reponame: If only repo_name
is given, all commits in the repo are
returned.
* tocommit: Optional. Only the ancestors of to
, including to
itself, are considered.
* from_commit: Optional. Only the descendants of from
, including
from
itself, are considered.
* number: Optional. Determines how many commits are returned. If
number
is 0, all commits that match the aforementioned criteria are
returned.
def list_file(
self, commit, path, history=None, include_contents=None)
Lists the files in a directory.
Params:
* commit: A tuple, string, or Commit
object representing the commit.
* path: The path to the directory.
* history: An optional int that indicates to return jobs from
historical versions of pipelines. Semantics are:
0: Return jobs from the current version of the pipeline or pipelines.
1: Return the above and jobs from the next most recent version
2: etc.
-1: Return jobs from all historical versions.
* include_contents: An optional bool. If True
, file contents are
included.
def list_repo(
self)
Returns info about all repos, as a list of RepoInfo
objects.
def put_file_bytes(
self, commit, path, value, delimiter=None, target_file_datums=None, target_file_bytes=None, overwrite_index=None)
Uploads a binary bytes array as file(s) in a certain path.
Params:
* commit: A tuple, string, or Commit
object representing the commit.
* path: A string specifying the path in the repo the file(s) will be
written to.
* value: The file contents as bytes, represented as a file-like
object, bytestring, or iterator of bytestrings.
* delimiter: Optional. causes data to be broken up into separate files
with path
as a prefix.
* targetfiledatums: An optional int. Specifies the target number of
datums in each written file. It may be lower if data does not split
evenly, but will never be higher, unless the value is 0.
* targetfilebytes: An optional int. Specifies the target number of
bytes in each written file, files may have more or fewer bytes than
the target.
* overwrite_index: An optional OverwriteIndex
object. This is the
object index where the write starts from. All existing objects
starting from the index are deleted.
def put_file_url(
self, commit, path, url, recursive=None)
Puts a file using the content found at a URL. The URL is sent to the server which performs the request. Note that this is not a standard PFS function.
Params:
* commit: A tuple, string, or Commit
object representing the commit.
* path: A string specifying the path to the file.
* url: A string specifying the url of the file to put.
* recursive: allow for recursive scraping of some types URLs, for
example on s3:// URLs.
def start_commit(
self, repo_name, branch=None, parent=None, description=None, provenance=None)
Begins the process of committing data to a Repo. Once started you can write to the Commit with PutFile and when all the data has been written you must finish the Commit with FinishCommit. NOTE, data is not persisted until FinishCommit is called. A Commit object is returned.
Params:
* repo_name: A string specifying the name of the repo.
* branch: A string specifying the branch name. This is a more
convenient way to build linear chains of commits. When a commit is
started with a non-empty branch the value of branch becomes an alias
for the created Commit. This enables a more intuitive access pattern.
When the commit is started on a branch the previous head of the branch
is used as the parent of the commit.
* parent: An optional Commit
object specifying the parent commit.
Upon creation the new commit will appear identical to the parent
commit, data can safely be added to the new commit without affecting
the contents of the parent commit.
* description: An optional string describing the commit.
* provenance: An optional iterable of CommitProvenance
objects
specifying the commit provenance.
def subscribe_commit(
self, repo_name, branch, from_commit_id=None, state=None)
Yields CommitInfo
objects as commits occur.
Params: * reponame: A string specifying the name of the repo. * branch: A string specifying branch to subscribe to. * fromcommit_id: An optional string specifying the commit ID.  Returns a generator of
FileInfo
objects.
Params:
* commit: A tuple, string, or Commit
object representing the commit.
* path: The path to the directory.


Try out our Premium Member services: Virtual Legal Assistant, Query Alert Service and an ad-free experience. Free for one month and pay only if you like it.
IN THE HIGH COURT OF JHARKHAND, RANCHI --- Cr.M.P. No. 750 of 2014 --- 1.Parwej Khan 2.Satyapal Rawat .... .... ..... ........ ...Petitioner(s) -- Versus -- State of Jharkhand & Anr. ......... ..................Opposite Parties --- CORAM :- HON'BLE MR. JUSTICE P.P. BHATT --- For the Petitioner(s) :- Mr. Nilesh Kumar, Advocate For the State :- A.P.P. --- 03/ 18.11.2014
The present application is filed under Section 482 of the Cr. P.C., 1973, for quashing of the F.I.R. of Sikidiri P.S.Case No.32 of 2013, corresponding to G.R. No.4852 of 2013, for the offence under Rules 54 & 67 of the Jharkhand Mines and Minerals Concession Rules, 2004, and Sections 21A, 31A and 37 of Air (Prevention of Control of Pollution) Act, 1981, now pending in the Court of learned Judicial Magistrate, Ranchi.
Learned counsel for the petitioners makes the following submissions :
(i) that, according to the learned counsel for the petitioners, in view of Section 43(1)(b) of the Air (Prevention & Control of Pollution) Act, 1981 (hereinafter referred to as 'the Act'), the informant ought to have filed complaint instead of lodging the First Information Report;
(ii) that, in view of Section 43 (1)(b) of the Act, the informant ought to give notice to the person who was to be prosecuted 60 days prior in the manner prescribed, but, no such notice was given in the instant case;
(iii) that, in view of Section 37-A of the Act, the Board ought to have at first give notice with direction to close or prohibit or regulation of Industry operation or process;
(iv) that, in view of Section 19 of the Act, the area has not been declared as Air Pollution Control Area;
(v) that, in view of Section 24 of the Act, the criteria of person has been defined for entry and inspection, and
(vi) that, the learned counsel for the petitioners has submitted that in the instant case, Sri Yogendra Baraik (informant) is not the authorized person.
Learned A.P.P. appearing for the State has prayed for four weeks time to take instructions and file counter affidavit and also submitted that let notice be issued upon the opposite party no.2- Assistant Mining Officer, Ranchi.
In view of the above submissions, let notice be issued upon the opposite party no.2, by registered post with A/D as well as under ordinary process, for which, requisites etc., must be filed on or before 21st Nov., 2014.
Notice is made returnable on 8th Jan., 2015. Till 8th Jan.,2015, there shall not be any coercive steps against the present petitioners in connection with Sikidiri P.S. Case No.32 of 2013, corresponding to G.R. No.4852 of 2013, pending in the court of learned Judicial Magistrate, Ranchi.
Put up this case on 8th January, 2015.
(P.P.Bhatt,J) SI


|
Here
you can insert your new or an other text it's your choice...
|
|
Here
you can insert a text or a pub of a partner or you write what you want!!!
|
| Here
you just have to write your text. But first you must read this text. I'm
going to give you some informations. ...First, for replace "My organisation" and "www.mondomaine.com" you just have to open "titre.psp" and "domaine.psp" files. When you have modified these files, you must record them in "titre.psp" and "domaine.psp" first and after in "titre.gif" and "domaine.gif". It's the last format who appeers in your web page. ...Now if you have to move menus on vertical you will have to move layers with the text of buttons to. . Good luck for your website. |
|
|
©vous web site designed
by www.E-template.fr.st
|


| Country | Per shipment | Per item |
|---|---|---|
| United States | $4.99 | $1.49 |
| Canada | $6.99 | $1.99 |
| Latin America | $3.99 | $2.99 |
| Atlantic Caribbean | $6.99 | $3.99 |
| Country | Per shipment | Per item |
|---|---|---|
| Europe | $3.99 | $2.49 |
| Israel | $6.99 | $2.99 |
| Middle East | $4.99 | $3.99 |
| Africa | $6.99 | $4.99 |
| Russian Federation | $12.99 | $4.99 |
| Country | Per shipment | Per item |
|---|---|---|
| Japan | $4.99 | $2.99 |
| india | $3.99 | $2.99 |
| Other asian and pacific | $13.99 | $6.99 |
We want you to be 100% happy with your purchases, so we aim to make any returns as easy as possible!
30 DAY RETURN GUARANTEE
You can return your item for an EXCHANGE, REFUND or STORE CREDIT
- Items must be returned in the original condition - product tags attached, original packaging, unworn, unused.
- Please keep the item clean from animal fur, stains & cigarette smoke. We reserve the right to reject any items that are returned in unsaleable condition.
- The customer is responsible for the return postage costs, unless there was an error with the order. In this case we will cover all costs to ensure you are not out of pocket. Exchange Postage costs - $7.95 Regular Post or $9.95 Express Post.
- All returns will be processed within 2 days of arriving back to the warehouse [there may be some slight delays on processing returns during busy times]
- Please note : Items purchased from our Sale section can be returned for a store credit only. This does NOT include items that were purchased during a sale event, eg. 15% OFF Sale, these can be returned for a full refund as normal.
FAULTY ITEMS : If you have received a faulty item we are terribly sorry! Please do not submit it through our Returns process, but instead email us directly at<EMAIL_ADDRESS>with a photo of the item so we can arrange a solution for you right away!
PAYPAL REFUNDED RETURNS : If you paid for your order with Paypal, you can receive a refund on the return postage costs through your Paypal account.
Simply activate Refunded Returns on your PayPal account & follow the instructions in your PayPal account.
We hold customer service very highly and want all our customers to walk away from their experience happy with the service they have been provided and loving their new purchases! If you have any questions about a product or the return process, please contact us as we are happy to help in any way we can!


Is there a numbering range that will be used for Open Energy Monitor devices communicating via RF going forward?
I've noticed that in the new EMONPI image the EMONHUB has allocation for 8 x EMONTH's which overlay the number range I was using to feed into EMONHUB via MQTT from Openhab. Simply deleting the duplication works until I update the image when they return so I was wondering what numbering range is safe to use.
Thanks
Kevin
Re: Is there a numbering range for RFM Devices?
Hello Kevin, we're having a bit of an discussion about this, whether emonhub.conf should ship pre-configured or not. You're right that many of the id's are now used up and reflect firmware changes on the emonth and emontx. I see the issue where the emonpiupdate will keep adding the nodes even if you already have it defined.
This is being done by the search here: https://github.com/openenergymonitor/emonpi/blob/master/emoncmsupdate#L57 I will look into how this can be changed to check for nodeid's so that it only appends a node if no node is already described on [[8]] etc
Re: Is there a numbering range for RFM Devices?
I've changed the implementation of this to search for nodeid's rather than the firmware name and add nodes individually. Running update now should not overwrite any existing nodes on the same nodeid. It will only add node decoders for nodes not described - which could in itself still be a problem if the nodes are not being described at all?
Re: Is there a numbering range for RFM Devices?
Thanks for the replies Trystan, is an entry such as
[[4]]
nodename = Office_Temp
sufficient to prevent overwriting or does it need extra information in there? I'm guessing you changed something in the last few days to prevent a full overwrite?
Re: Is there a numbering range for RFM Devices?
Yes that will be sufficient
Re: Is there a numbering range for RFM Devices?
Thanks, I did an update and nothing got overwritten