id stringlengths 9 16 | submitter stringlengths 2 51 ⌀ | title stringlengths 5 243 | categories stringlengths 5 69 | abstract stringlengths 23 3.66k | labels stringlengths 5 184 | domain stringclasses 9
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2311.18274 | Thomas Cook | Semiparametric Efficient Inference in Adaptive Experiments | stat.ML cs.LG stat.ME | We consider the problem of efficient inference of the Average Treatment
Effect in a sequential experiment where the policy governing the assignment of
subjects to treatment or control can change over time. We first provide a
central limit theorem for the Adaptive Augmented Inverse-Probability Weighted
estimator, whic... | Machine Learning, Machine Learning, Methodology | Statistics |
2103.05092 | Larry Wasserman | Forest Guided Smoothing | stat.ML cs.LG stat.ME | We use the output of a random forest to define a family of local smoothers
with spatially adaptive bandwidth matrices. The smoother inherits the
flexibility of the original forest but, since it is a simple, linear smoother,
it is very interpretable and it can be used for tasks that would be intractable
for the origin... | Machine Learning, Machine Learning, Methodology | Statistics |
2405.20039 | Jiacheng Miao | Task-Agnostic Machine Learning-Assisted Inference | stat.ML cs.LG stat.ME | Machine learning (ML) is playing an increasingly important role in scientific
research. In conjunction with classical statistical approaches, ML-assisted
analytical strategies have shown great promise in accelerating research
findings. This has also opened up a whole new field of methodological research
focusing on i... | Machine Learning, Machine Learning, Methodology | Statistics |
2301.02190 | Michel Van De Velden | A general framework for implementing distances for categorical variables | stat.ML cs.LG stat.ME | The degree to which subjects differ from each other with respect to certain
properties measured by a set of variables, plays an important role in many
statistical methods. For example, classification, clustering, and data
visualization methods all require a quantification of differences in the
observed values. We can... | Machine Learning, Machine Learning, Methodology | Statistics |
1312.4479 | Jean-Baptiste Durand | Parametric Modelling of Multivariate Count Data Using Probabilistic
Graphical Models | stat.ML cs.LG stat.ME | Multivariate count data are defined as the number of items of different
categories issued from sampling within a population, which individuals are
grouped into categories. The analysis of multivariate count data is a recurrent
and crucial issue in numerous modelling problems, particularly in the fields of
biology and... | Machine Learning, Machine Learning, Methodology | Statistics |
1805.05383 | Jeremias Knoblauch | Spatio-temporal Bayesian On-line Changepoint Detection with Model
Selection | stat.ML cs.LG stat.ME | Bayesian On-line Changepoint Detection is extended to on-line model selection
and non-stationary spatio-temporal processes. We propose spatially structured
Vector Autoregressions (VARs) for modelling the process between changepoints
(CPs) and give an upper bound on the approximation error of such models. The
resultin... | Machine Learning, Machine Learning, Methodology | Statistics |
2111.04597 | Ye Tian | Neyman-Pearson Multi-class Classification via Cost-sensitive Learning | stat.ML cs.LG stat.ME | Most existing classification methods aim to minimize the overall
misclassification error rate. However, in applications such as loan default
prediction, different types of errors can have varying consequences. To address
this asymmetry issue, two popular paradigms have been developed: the
Neyman-Pearson (NP) paradigm... | Machine Learning, Machine Learning, Methodology | Statistics |
2402.07868 | Sahel Iqbal | Nesting Particle Filters for Experimental Design in Dynamical Systems | stat.ML cs.LG stat.ME | In this paper, we propose a novel approach to Bayesian experimental design
for non-exchangeable data that formulates it as risk-sensitive policy
optimization. We develop the Inside-Out SMC$^2$ algorithm, a nested sequential
Monte Carlo technique to infer optimal designs, and embed it into a particle
Markov chain Mont... | Machine Learning, Machine Learning, Methodology | Statistics |
2005.00466 | Mike Laszkiewicz | Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph
Recovery | stat.ML cs.LG stat.ME | Many Machine Learning algorithms are formulated as regularized optimization
problems, but their performance hinges on a regularization parameter that needs
to be calibrated to each application at hand. In this paper, we propose a
general calibration scheme for regularized optimization problems and apply it
to the gra... | Machine Learning, Machine Learning, Methodology | Statistics |
1908.05287 | Mohsen Shahhosseini | Optimizing Ensemble Weights and Hyperparameters of Machine Learning
Models for Regression Problems | stat.ML cs.LG stat.ME | Aggregating multiple learners through an ensemble of models aim to make
better predictions by capturing the underlying distribution of the data more
accurately. Different ensembling methods, such as bagging, boosting, and
stacking/blending, have been studied and adopted extensively in research and
practice. While bag... | Machine Learning, Machine Learning, Methodology | Statistics |
2305.04086 | Gongbo Zhang | Efficient Learning for Selecting Top-m Context-Dependent Designs | stat.ML math.OC | We consider a simulation optimization problem for a context-dependent
decision-making, which aims to determine the top-m designs for all contexts.
Under a Bayesian framework, we formulate the optimal dynamic sampling decision
as a stochastic dynamic programming problem, and develop a sequential sampling
policy to eff... | Machine Learning, Optimization and Control | Statistics |
1203.0565 | Taiji Suzuki | Fast learning rate of multiple kernel learning: Trade-off between
sparsity and smoothness | stat.ML math.ST stat.TH | We investigate the learning rate of multiple kernel learning (MKL) with
$\ell_1$ and elastic-net regularizations. The elastic-net regularization is a
composition of an $\ell_1$-regularizer for inducing the sparsity and an
$\ell_2$-regularizer for controlling the smoothness. We focus on a sparse
setting where the tota... | Machine Learning, Statistics Theory, Statistics Theory | Statistics |
1204.4154 | Nathan Lay | The Artificial Regression Market | stat.ML math.ST stat.TH | The Artificial Prediction Market is a recent machine learning technique for
multi-class classification, inspired from the financial markets. It involves a
number of trained market participants that bet on the possible outcomes and are
rewarded if they predict correctly. This paper generalizes the scope of the
Artific... | Machine Learning, Statistics Theory, Statistics Theory | Statistics |
1401.0871 | Sakellarios Zairis | Stylistic Clusters and the Syrian/South Syrian Tradition of
First-Millennium BCE Levantine Ivory Carving: A Machine Learning Approach | stat.ML stat.AP | Thousands of first-millennium BCE ivory carvings have been excavated from
Neo-Assyrian sites in Mesopotamia (primarily Nimrud, Khorsabad, and Arslan
Tash) hundreds of miles from their Levantine production contexts. At present,
their specific manufacture dates and workshop localities are unknown. Relying
on subjective... | Machine Learning, Applications | Statistics |
1405.5576 | Sam Davanloo | On the Theoretical Guarantees for Parameter Estimation of Gaussian
Random Field Models: A Sparse Precision Matrix Approach | stat.ML stat.CO | Iterative methods for fitting a Gaussian Random Field (GRF) model via maximum
likelihood (ML) estimation requires solving a nonconvex optimization problem.
The problem is aggravated for anisotropic GRFs where the number of covariance
function parameters increases with the dimension. Even evaluation of the
likelihood ... | Machine Learning, Computation | Statistics |
0901.2730 | Jun Zhu | Maximum Entropy Discrimination Markov Networks | stat.ML stat.ME | In this paper, we present a novel and general framework called {\it Maximum
Entropy Discrimination Markov Networks} (MaxEnDNet), which integrates the
max-margin structured learning and Bayesian-style estimation and combines and
extends their merits. Major innovations of this model include: 1) It
generalizes the extan... | Machine Learning, Methodology | Statistics |
1802.03127 | Takayuki Kawashima | Robust and Sparse Regression in GLM by Stochastic Optimization | stat.ML stat.ME | The generalized linear model (GLM) plays a key role in regression analyses.
In high-dimensional data, the sparse GLM has been used but it is not robust
against outliers. Recently, the robust methods have been proposed for the
specific example of the sparse GLM. Among them, we focus on the robust and
sparse linear reg... | Machine Learning, Methodology | Statistics |
1905.08876 | Andrew Gelman | Many perspectives on Deborah Mayo's "Statistical Inference as Severe
Testing: How to Get Beyond the Statistics Wars" | stat.OT | The new book by philosopher Deborah Mayo is relevant to data science for
topical reasons, as she takes various controversial positions regarding
hypothesis testing and statistical practice, and also as an entry point to
thinking about the philosophy of statistics. The present article is a slightly
expanded version of... | Other Statistics | Statistics |
1811.06980 | Antonio Irpino PhD | Batch Self Organizing maps for distributional data using adaptive
distances | stat.OT | The paper deals with a Batch Self Organizing Map algorithm (DBSOM) for data
described by distributional-valued variables. This kind of variables is
characterized to take as values one-dimensional probability or frequency
distributions on a numeric support. The objective function optimized in the
algorithm depends on ... | Other Statistics | Statistics |
2007.12210 | Roger Peng | Reproducible Research: A Retrospective | stat.OT | Rapid advances in computing technology over the past few decades have spurred
two extraordinary phenomena in science: large-scale and high-throughput data
collection coupled with the creation and implementation of complex statistical
algorithms for data analysis. Together, these two phenomena have brought about
treme... | Other Statistics | Statistics |
1903.08880 | John Galati | Three issues impeding communication of statistical methodology for
incomplete data | stat.OT | We identify three issues permeating the literature on statistical methodology
for incomplete data written for non-specialist statisticians and other
investigators. The first is a mathematical defect in the notation Yobs, Ymis
used to partition the data into observed and missing components. The second are
issues conce... | Other Statistics | Statistics |
1209.4019 | Giles Hooker | Experimental design for Partially Observed Markov Decision Processes | stat.OT | This paper deals with the question of how to most effectively conduct
experiments in Partially Observed Markov Decision Processes so as to provide
data that is most informative about a parameter of interest. Methods from
Markov decision processes, especially dynamic programming, are introduced and
then used in an alg... | Other Statistics | Statistics |
1911.00535 | Alex Reinhart | Think-aloud interviews: A tool for exploring student statistical
reasoning | stat.OT | Think-aloud interviews have been a valuable but underused tool in statistics
education research. Think-alouds, in which students narrate their reasoning in
real time while solving problems, differ in important ways from other types of
cognitive interviews and related education research methods. Beyond the uses
alread... | Other Statistics | Statistics |
1905.10209 | {\L}ukasz Rajkowski | A score function for Bayesian cluster analysis | stat.OT | We propose a score function for Bayesian clustering. The function is
parameter free and captures the interplay between the within cluster variance
and the between cluster entropy of a clustering. It can be used to choose the
number of clusters in well-established clustering methods such as hierarchical
clustering or ... | Other Statistics | Statistics |
2401.11000 | Jing (Janet) Lin | Human-Centric and Integrative Lighting Asset Management in Public
Libraries: Qualitative Insights and Challenges from a Swedish Field Study | stat.OT | Traditional lighting source reliability evaluations, often covering just half
of a lamp's volume, can misrepresent real-world performance. To overcome these
limitations,adopting advanced asset management strategies for a more holistic
evaluation is crucial. This paper investigates human-centric and integrative
lighti... | Other Statistics | Statistics |
2009.02099 | Yudi Pawitan | Defending the P-value | stat.OT stat.AP | Attacks on the P-value are nothing new, but the recent attacks are
increasingly more serious. They come from more mainstream sources, with
widening targets such as a call to retire the significance testing altogether.
While well meaning, I believe these attacks are nevertheless misdirected:
Blaming the P-value for th... | Other Statistics, Applications | Statistics |
1910.06964 | Charles Gray | \texttt{code::proof}: Prepare for \emph{most} weather conditions | stat.OT stat.ME | Computational tools for data analysis are being released daily on
repositories such as the Comprehensive R Archive Network. How we integrate
these tools to solve a problem in research is increasingly complex and
requiring frequent updates. To mitigate these \emph{Kafkaesque} computational
challenges in research, this... | Other Statistics, Methodology | Statistics |
supr-con/9502001 | Mark Jarrell | Anomalous Normal-State Properties of High-T$_c$ Superconductors --
Intrinsic Properties of Strongly Correlated Electron Systems? | supr-con cond-mat.supr-con | A systematic study of optical and transport properties of the Hubbard model,
based on Metzner and Vollhardt's dynamical mean-field approximation, is
reviewed. This model shows interesting anomalous properties that are, in our
opinion, ubiquitous to single-band strongly correlated systems (for all spatial
dimensions g... | Superconductivity | Physics |
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