p7stat.ME

Category

stat.ME

232 papers

Augment-and-Conquer Negative Binomial Processes

Mingyuan Zhou, Lawrence Carin

1209.1119

Learning deep kernels for exponential family densities

Li Wenliang, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton

1811.08357

Show Your Work: Improved Reporting of Experimental Results

Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, Noah A. Smith

1909.03004

Scaling limit of the Stein variational gradient descent: the mean field regime

Jianfeng Lu, Yulong Lu, James Nolen

1805.04035

Hierarchical Implicit Models and Likelihood-Free Variational Inference

Dustin Tran, Rajesh Ranganath, David M. Blei

1702.08896

Gaussian Process Kernels for Pattern Discovery and Extrapolation

Andrew Gordon Wilson, Ryan Prescott Adams

1302.4245

A Statistical Perspective on Algorithmic Leveraging

Ping Ma, Michael W. Mahoney, Bin Yu

1306.5362

Stochastic Variational Deep Kernel Learning

Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing

1611.00336

Thoughts on Massively Scalable Gaussian Processes

Andrew Gordon Wilson, Christoph Dann, Hannes Nickisch

1511.01870

Deep Poisson gamma dynamical systems

Dandan Guo, Bo Chen, Hao Zhang, Mingyuan Zhou

1810.11209

f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

Sebastian Nowozin, Botond Cseke, Ryota Tomioka

1606.00709

Achieving Optimal Misclassification Proportion in Stochastic Block Model

Chao Gao, Zongming Ma, Anderson Y. Zhang, Harrison H. Zhou

1505.03772

Bayesian Regression Tree Ensembles that Adapt to Smoothness and Sparsity

Antonio Ricardo Linero, Yun Yang

1707.09461

Heteroscedastic BART Using Multiplicative Regression Trees

Matthew Pratola, Hugh Chipman, Edward George, Robert McCulloch

1709.07542

Density Estimation in Infinite Dimensional Exponential Families

Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar

1312.3516

Variational Inference with Normalizing Flows

Danilo Jimenez Rezende, Shakir Mohamed

1505.05770

Dropout Training as Adaptive Regularization

Stefan Wager, Sida Wang, Percy Liang

1307.1493

Enhancing Sparsity by Reweighted L1 Minimization

Emmanuel J. Candes, Michael B. Wakin, Stephen P. Boyd

0711.1612

Particle Filters for Partially Observed Diffusions

Paul Fearnhead, Omiros Papaspiliopoulos, Gareth Roberts

0710.4245

Measuring Sample Quality with Stein's Method

Jackson Gorham, Lester Mackey

1506.03039

Control functionals for Monte Carlo integration

Chris J. Oates, Mark Girolami, Nicolas Chopin

1410.2392

On the relation between Gaussian process quadratures and sigma-point methods

Simo Särkkä, Jouni Hartikainen, Lennart Svensson, Fredrik Sandblom

1504.05994

Dynamic Trees for Learning and Design

Matthew A. Taddy, Robert B. Gramacy, Nicholas G. Polson

0912.1586

Gamma Belief Networks

Mingyuan Zhou, Yulai Cong, Bo Chen

1512.03081

Particle learning of Gaussian process models for sequential design and optimization

Robert B. Gramacy, Nicholas G. Polson

0909.5262

BART: Bayesian additive regression trees

Hugh A. Chipman, Edward I. George, Robert E. McCulloch

0806.3286

Non-Stationary Spatial Modeling

Dave Higdon, Jenise Swall, John Kern

2212.08043

Stochastic Gradient Hamiltonian Monte Carlo

Tianqi Chen, Emily B. Fox, Carlos Guestrin

1402.4102

Nonparametric Bayesian Negative Binomial Factor Analysis

Mingyuan Zhou

1604.07464

The Poisson Gamma Belief Network

Mingyuan Zhou, Yulai Cong, Bo Chen

1511.02199

Negative Binomial Process Count and Mixture Modeling

Mingyuan Zhou, Lawrence Carin

1209.3442

Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression

David Mimno, Andrew McCallum

1206.3278

Assumption Lean Regression

Richard A. Berk, Andreas Buja, Lawrence Brown, Edward George, Arun Kumar Kuchibhotla, Weijie J. Su, Linda Zhao

1806.09014

Black Box Variational Inference

Rajesh Ranganath, Sean Gerrish, David M. Blei

1401.0118

Gaussian Process Regression Networks

Andrew Gordon Wilson, David A. Knowles, Zoubin Ghahramani

1110.4411

Equivalence of distance-based and RKHS-based statistics in hypothesis testing

Dino Sejdinovic, Bharath Sriperumbudur, Arthur Gretton, Kenji Fukumizu

1207.6076

Assumption violations in causal discovery and the robustness of score matching

Francesco Montagna, Atalanti A. Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello

2310.13387

Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers

Abraham J. Wyner, Matthew Olson, Justin Bleich, David Mease

1504.07676

Priors for Random Count Matrices Derived from a Family of Negative Binomial Processes

Mingyuan Zhou, Oscar Hernan Madrid Padilla, James G. Scott

1404.3331

Sparse CCA: Adaptive Estimation and Computational Barriers

Chao Gao, Zongming Ma, Harrison H. Zhou

1409.8565

Sylvester Normalizing Flows for Variational Inference

Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling

1803.05649

Quantum Computation and Quantum Information

Yazhen Wang

1210.0736

Optimizing The Integrator Step Size for Hamiltonian Monte Carlo

M. J. Betancourt, Simon Byrne, Mark Girolami

1411.6669

A Constrained L1 Minimization Approach to Sparse Precision Matrix Estimation

Tony Cai, Weidong Liu, Xi Luo

1102.2233

Empirical risk minimization for heavy-tailed losses

Christian Brownlees, Emilien Joly, Gábor Lugosi

1406.2462

Adaptive Thresholding for Sparse Covariance Matrix Estimation

Tony Cai, Weidong Liu

1102.2237

The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling

Andreas Mayr, Harald Binder, Olaf Gefeller, Matthias Schmid

1403.1452

A Scalable Bootstrap for Massive Data

Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar, Michael I. Jordan

1112.5016

Covariances, Robustness, and Variational Bayes

Ryan Giordano, Tamara Broderick, Michael I. Jordan

1709.02536

Stochastic Variational Inference

Matt Hoffman, David M. Blei, Chong Wang, John Paisley

1206.7051

Hierarchical Variational Models

Rajesh Ranganath, Dustin Tran, David M. Blei

1511.02386

Green AI

Roy Schwartz, Jesse Dodge, Noah A. Smith, Oren Etzioni

1907.10597

Adaptive design and analysis of supercomputer experiments

Robert B. Gramacy, Herbert K. H. Lee

0805.4359

Alternating Projection, Ptychographic Imaging and Phase Synchronization

Stefano Marchesini, Yu-Chao Tu, Hau-tieng Wu

1402.0550

Doubly Robust Off-policy Value Evaluation for Reinforcement Learning

Nan Jiang, Lihong Li

1511.03722

Doubly Robust Policy Evaluation and Optimization

Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li

1503.02834

Particle Learning and Smoothing

Carlos M. Carvalho, Michael S. Johannes, Hedibert F. Lopes, Nicholas G. Polson

1011.1098

High-recall causal discovery for autocorrelated time series with latent confounders

Andreas Gerhardus, Jakob Runge

2007.01884

A Complete Recipe for Stochastic Gradient MCMC

Yi-An Ma, Tianqi Chen, Emily B. Fox

1506.04696

Stochastic Backpropagation and Approximate Inference in Deep Generative Models

Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra

1401.4082

Variational Sequential Monte Carlo

Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei

1705.11140

Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects

P. Richard Hahn, Jared S. Murray, Carlos Carvalho

1706.09523

Learning Sparse Nonparametric DAGs

Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing

1909.13189

Kernel-based independence tests for causal structure learning on functional data

Felix Laumann, Julius von Kügelgen, Junhyung Park, Bernhard Schölkopf, Mauricio Barahona

2311.08743

Structure Discovery in Nonparametric Regression through Compositional Kernel Search

David Duvenaud, James Robert Lloyd, Roger Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani

1302.4922

Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning

Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson

1902.03932

Nonparametric Bayesian Factor Analysis for Dynamic Count Matrices

Ayan Acharya, Joydeep Ghosh, Mingyuan Zhou

1512.08996

Combinatorial clustering and the beta negative binomial process

Tamara Broderick, Lester Mackey, John Paisley, Michael I. Jordan

1111.1802

Log-Linear Bayesian Additive Regression Trees for Multinomial Logistic and Count Regression Models

Jared S. Murray

1701.01503

Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms

Ahmed M. Alaa, Mihaela van der Schaar

1712.08914

Likelihood-free inference via classification

Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski, Jukka Corander

1407.4981

Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition

Vincent Dorie, Jennifer Hill, Uri Shalit, Marc Scott, Dan Cervone

1707.02641

Operator Variational Inference

Rajesh Ranganath, Jaan Altosaar, Dustin Tran, David M. Blei

1610.09033

Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithm

Alain Durmus, Eric Moulines

1507.05021

Noisy Monte Carlo: Convergence of Markov chains with approximate transition kernels

P. Alquier, N. Friel, R. Everitt, A. Boland

1403.5496

Lognormal and Gamma Mixed Negative Binomial Regression

Mingyuan Zhou, Lingbo Li, David Dunson, Lawrence Carin

1206.6456

A Kernel Test for Three-Variable Interactions

Dino Sejdinovic, Arthur Gretton, Wicher Bergsma

1306.2281

Supersparse Linear Integer Models for Optimized Medical Scoring Systems

Berk Ustun, Cynthia Rudin

1502.04269

Optimal selection of reduced rank estimators of high-dimensional matrices

Florentina Bunea, Yiyuan She, Marten H. Wegkamp

1004.2995

The frontier of simulation-based inference

Kyle Cranmer, Johann Brehmer, Gilles Louppe

1911.01429

Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms

Christian A. Naesseth, Francisco J. R. Ruiz, Scott W. Linderman, David M. Blei

1610.05683

Omnigrok: Grokking Beyond Algorithmic Data

Ziming Liu, Eric J. Michaud, Max Tegmark

2210.01117

Copula Processes

Andrew Gordon Wilson, Zoubin Ghahramani

1006.1350

Beta-Negative Binomial Process and Poisson Factor Analysis

Mingyuan Zhou, Lauren Hannah, David Dunson, Lawrence Carin

1112.3605

Positive Definite $\ell_1$ Penalized Estimation of Large Covariance Matrices

Lingzhou Xue, Shiqian Ma, Hui Zou

1208.5702

Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems

Thomas Bengtsson, Peter Bickel, Bo Li

0805.3034

Nested Sequential Monte Carlo Methods

Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön

1502.02536

Deep Reinforcement Learning at the Edge of the Statistical Precipice

Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville, Marc G. Bellemare

2108.13264

Boosting the concordance index for survival data - a unified framework to derive and evaluate biomarker combinations

Andreas Mayr, Matthias Schmid

1307.6417

Sequential Monte Carlo for Graphical Models

Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön

1402.0330

Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism

Ery Arias-Castro, Emmanuel J. Candès, Yaniv Plan

1007.1434

Community extraction for social networks

Yunpeng Zhao, Elizaveta Levina, Ji Zhu

1005.3265

Maximum likelihood estimation of a multidimensional log-concave density

Madeleine Cule, Richard Samworth, Michael Stewart

0804.3989

Langevin diffusions and the Metropolis-adjusted Langevin algorithm

Tatiana Xifara, Chris Sherlock, Samuel Livingstone, Simon Byrne, Mark Girolami

1309.2983

On Nesting Monte Carlo Estimators

Tom Rainforth, Robert Cornish, Hongseok Yang, Andrew Warrington, Frank Wood

1709.06181

Excursion and contour uncertainty regions for latent Gaussian models

David Bolin, Finn Lindgren

1211.3946

Robust Estimation and Generative Adversarial Nets

Chao Gao, Jiyi Liu, Yuan Yao, Weizhi Zhu

1810.02030

Information Theoretical Estimators Toolbox

Zoltan Szabo

1405.2106

Sensitivity analysis via the proportion of unmeasured confounding

Matteo Bonvini, Edward H Kennedy

1912.02793

Gaussian Graphical Model Estimation with False Discovery Rate Control

Weidong Liu

1306.0976

Tests in adaptive regression via the Kac-Rice formula

Jonathan Taylor, Joshua Loftus, Ryan Tibshirani

1308.3020

Stochastic blockmodels with growing number of classes

David S. Choi, Patrick J. Wolfe, Edoardo M. Airoldi

1011.4644

Deep Kernel Learning

Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing

1511.02222

Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets

Jakob Runge

2003.03685

On Estimating Many Means, Selection Bias, and the Bootstrap

Noah Simon, Richard Simon

1311.3709

Topological Data Analysis

Larry Wasserman

1609.08227

De-biasing "bias" measurement

Kristian Lum, Yunfeng Zhang, Amanda Bower

2205.05770

Beta processes, stick-breaking, and power laws

Tamara Broderick, Michael I. Jordan, Jim Pitman

1106.0539

Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game

Alexander G. Reisach, Christof Seiler, Sebastian Weichwald

2102.13647

Inference and Modeling with Log-concave Distributions

Guenther Walther

1010.0305

Bayesian treed Gaussian process models with an application to computer modeling

Robert B. Gramacy, Herbert K. H. Lee

0710.4536

A Selective Overview of Deep Learning

Jianqing Fan, Cong Ma, Yiqiao Zhong

1904.05526

Why do tree-based models still outperform deep learning on tabular data?

Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux

2207.08815

Bayesian nonparametric Plackett-Luce models for the analysis of preferences for college degree programmes

François Caron, Yee Whye Teh, Thomas Brendan Murphy

1211.5037

Sparse principal component analysis and iterative thresholding

Zongming Ma

1112.2432

Causal discovery in heavy-tailed models

Nicola Gnecco, Nicolai Meinshausen, Jonas Peters, Sebastian Engelke

1908.05097

On overfitting and post-selection uncertainty assessments

Liang Hong, Todd A. Kuffner, Ryan Martin

1712.02379

Preconditioning Kernel Matrices

Kurt Cutajar, Michael A. Osborne, John P. Cunningham, Maurizio Filippone

1602.06693

A Unified Framework for High-Dimensional Analysis of M-Estimators with Decomposable Regularizers

Sahand N. Negahban, Pradeep Ravikumar, Martin J. Wainwright, Bin Yu

1010.2731

Bayesian nonparametric models for ranked data

Francois Caron, Yee Whye Teh

1211.4321

Causal Structure Learning: a Combinatorial Perspective

Chandler Squires, Caroline Uhler

2206.01152

Optimal Inference After Model Selection

William Fithian, Dennis Sun, Jonathan Taylor

1410.2597

Variable selection for BART: An application to gene regulation

Justin Bleich, Adam Kapelner, Edward I. George, Shane T. Jensen

1310.4887

Statistical Inference for Cluster Trees

Jisu Kim, Yen-Chi Chen, Sivaraman Balakrishnan, Alessandro Rinaldo, Larry Wasserman

1605.06416

A Bayesian Approach to Constraint Based Causal Inference

Tom Claassen, Tom Heskes

1210.4866

The Rank of the Covariance Matrix of an Evanescent Field

M. Kliger, J. M. Francos

0803.0783

Innovated higher criticism for detecting sparse signals in correlated noise

Peter Hall, Jiashun Jin

0902.3837

Information-geometric Markov Chain Monte Carlo methods using Diffusions

Samuel Livingstone, Mark Girolami

1403.7957

MCMC for Normalized Random Measure Mixture Models

Stefano Favaro, Yee Whye Teh

1310.0595

Proximal Markov chain Monte Carlo algorithms

Marcelo Pereyra

1306.0187

Bayesian inference for logistic models using Polya-Gamma latent variables

Nicholas G. Polson, James G. Scott, Jesse Windle

1205.0310

Confidence Intervals and Hypothesis Testing for High-Dimensional Regression

Adel Javanmard, Andrea Montanari

1306.3171

Multilinear tensor regression for longitudinal relational data

Peter D. Hoff

1412.0048

Deep Survival Analysis

Rajesh Ranganath, Adler Perotte, Noémie Elhadad, David Blei

1608.02158

A Conceptual Introduction to Hamiltonian Monte Carlo

Michael Betancourt

1701.02434

Generalised Wishart Processes

Andrew Gordon Wilson, Zoubin Ghahramani

1101.0240

Statistical Modeling of Spatial Extremes

A. C. Davison, S. A. Padoan, M. Ribatet

1208.3378

Can You Trust This Prediction? Auditing Pointwise Reliability After Learning

Peter Schulam, Suchi Saria

1901.00403

Estimating an Extreme Bayesian Network via Scalings

Claudia Klüppelberg, Mario Krali

1912.03968

Counterfactual Risk Assessments, Evaluation, and Fairness

Amanda Coston, Alan Mishler, Edward H. Kennedy, Alexandra Chouldechova

1909.00066

Importance sampling squared for Bayesian inference in latent variable models

Minh-Ngoc Tran, Marcel Scharth, Michael K. Pitt, Robert Kohn

1309.3339

Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

Stefan Wager, Susan Athey

1510.04342

Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations

Patrick R. Conrad, Youssef M. Marzouk, Natesh S. Pillai, Aaron Smith

1402.1694

Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems

Tina Toni, David Welch, Natalja Strelkowa, Andreas Ipsen, Michael P. H. Stumpf

0901.1925

SLOPE - Adaptive variable selection via convex optimization

Małgorzata Bogdan, Ewout van den Berg, Chiara Sabatti, Weijie Su, Emmanuel J. Candès

1407.3824

An introduction to sampling via measure transport

Youssef Marzouk, Tarek Moselhy, Matthew Parno, Alessio Spantini

1602.05023

Higher-dimensional spatial extremes via single-site conditioning

Jennifer L. Wadsworth, Jonathan Tawn

1912.06560

A New Perspective on Robust $M$-Estimation: Finite Sample Theory and Applications to Dependence-Adjusted Multiple Testing

Wen-Xin Zhou, Koushiki Bose, Jianqing Fan, Han Liu

1711.05381

Efficient Bayesian Inference for Generalized Bradley-Terry Models

Francois Caron, Arnaud Doucet

1011.1761

Exact Post Model Selection Inference for Marginal Screening

Jason D Lee, Jonathan E Taylor

1402.5596

False (and Missed) Discoveries in Financial Economics

Campbell R. Harvey, Yan Liu

2006.04269

Bayesian Indirect Inference Using a Parametric Auxiliary Model

Christopher C. Drovandi, Anthony N. Pettitt, Anthony Lee

1505.03372

Optimal whitening and decorrelation

Agnan Kessy, Alex Lewin, Korbinian Strimmer

1512.00809

Diagnosing Suboptimal Cotangent Disintegrations in Hamiltonian Monte Carlo

Michael Betancourt

1604.00695

KFAS: Exponential Family State Space Models in R

Jouni Helske

1612.01907

Data-Driven Model Reduction for the Bayesian Solution of Inverse Problems

Tiangang Cui, Youssef M. Marzouk, Karen E. Willcox

1403.4290

Density Level Sets: Asymptotics, Inference, and Visualization

Yen-Chi Chen, Christopher R. Genovese, Larry Wasserman

1504.05438

A mixed effects model for longitudinal relational and network data, with applications to international trade and conflict

Anton H. Westveld, Peter D. Hoff

1009.1436

A Separable Model for Dynamic Networks

Pavel N. Krivitsky, Mark S. Handcock

1011.1937

Robust Bayesian inference via coarsening

Jeffrey W. Miller, David B. Dunson

1506.06101

On the Geometric Ergodicity of Hamiltonian Monte Carlo

Samuel Livingstone, Michael Betancourt, Simon Byrne, Mark Girolami

1601.08057

Controlling the false discovery rate via knockoffs

Rina Foygel Barber, Emmanuel J. Candès

1404.5609

Advances in Statistical Modeling of Spatial Extremes

Raphaël Huser, Jennifer L. Wadsworth

2007.00774

Scalable Bayes via Barycenter in Wasserstein Space

Sanvesh Srivastava, Cheng Li, David B. Dunson

1508.05880

Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors

Chandler Squires, Annie Yun, Eshaan Nichani, Raj Agrawal, Caroline Uhler

2207.01237

Extremes on river networks

Peiman Asadi, Anthony C. Davison, Sebastian Engelke

1501.02663

Fairness Evaluation in Presence of Biased Noisy Labels

Riccardo Fogliato, Max G'Sell, Alexandra Chouldechova

2003.13808

Local Gaussian process approximation for large computer experiments

Robert B. Gramacy, Daniel W. Apley

1303.0383

High-Dimensional Inference: Confidence Intervals, $p$-Values and R-Software hdi

Ruben Dezeure, Peter Bühlmann, Lukas Meier, Nicolai Meinshausen

1408.4026

A significance test for forward stepwise model selection

Joshua R. Loftus, Jonathan E. Taylor

1405.3920

Adaptive Image Denoising by Targeted Databases

Enming Luo, Stanley H. Chan, Truong Q. Nguyen

1407.5055

Simulation-based Regularized Logistic Regression

Robert B. Gramacy, Nicholas G. Polson

1005.3430

Bayesian computation via empirical likelihood

K. L. Mengersen, P. Pudlo, C. P. Robert

1205.5658

Multivariate Rank-based Distribution-free Nonparametric Testing using Measure Transportation

Nabarun Deb, Bodhisattva Sen

1909.08733

Mixed membership stochastic blockmodels

Edoardo M Airoldi, David M Blei, Stephen E Fienberg, Eric P Xing

0705.4485

Approximation by log-concave distributions, with applications to regression

Lutz Duembgen, Richard Samworth, Dominic Schuhmacher

1002.3448

Evolutionary Stochastic Search for Bayesian model exploration

Leonardo Bottolo, Sylvia Richardson

1002.2706

Identifying the Optimal Integration Time in Hamiltonian Monte Carlo

Michael Betancourt

1601.00225

Outlier-Robust Optimal Transport

Debarghya Mukherjee, Aritra Guha, Justin Solomon, Yuekai Sun, Mikhail Yurochkin

2012.07363

Semiparametric doubly robust targeted double machine learning: a review

Edward H. Kennedy

2203.06469

Structure and Sensitivity in Differential Privacy: Comparing K-Norm Mechanisms

Jordan Awan, Aleksandra Slavkovic

1801.09236

Hierarchical Transformed Scale Mixtures for Flexible Modeling of Spatial Extremes on Datasets with Many Locations

Likun Zhang, Benjamin A. Shaby, Jennifer L. Wadsworth

1907.09617

Time-uniform, nonparametric, nonasymptotic confidence sequences

Steven R. Howard, Aaditya Ramdas, Jon McAuliffe, Jasjeet Sekhon

1810.08240

Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models

Michael U. Gutmann, Jukka Corander

1501.03291

Lifting -- A nonreversible Markov chain Monte Carlo Algorithm

Marija Vucelja

1412.8762

A continuous updating weighted least squares estimator of tail dependence in high dimensions

John H. J. Einmahl, Anna Kiriliouk, Johan Segers

1601.04826

Variance components and generalized Sobol' indices

Art B. Owen

1205.1774

Hamiltonian Monte Carlo for Hierarchical Models

M. J. Betancourt, Mark Girolami

1312.0906

Exact post-selection inference, with application to the lasso

Jason D. Lee, Dennis L. Sun, Yuekai Sun, Jonathan E. Taylor

1311.6238

$k$-means clustering of extremes

Anja Janßen, Phyllis Wan

1904.02970

DAGs with NO TEARS: Continuous Optimization for Structure Learning

Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing

1803.01422

Bayesian linear regression with sparse priors

Ismaël Castillo, Johannes Schmidt-Hieber, Aad van der Vaart

1403.0735

Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains

Joris Bierkens, Alexandre Bouchard-Côté, Arnaud Doucet, Andrew B. Duncan, Paul Fearnhead, Thibaut Lienart, Gareth Roberts, Sebastian J. Vollmer

1701.04244

Unbiased Markov chain Monte Carlo with couplings

Pierre E. Jacob, John O'Leary, Yves F. Atchadé

1708.03625

Statistical properties of sketching algorithms

Daniel Ahfock, William J. Astle, Sylvia Richardson

1706.03665

Iteration Complexity Analysis of Block Coordinate Descent Methods

Mingyi Hong, Xiangfeng Wang, Meisam Razaviyayn, Zhi-Quan Luo

1310.6957

Monotonic convergence of a general algorithm for computing optimal designs

Yaming Yu

0905.2646

Variational inference for large-scale models of discrete choice

Michael Braun, Jon McAuliffe

0712.2526

Augmented sparse principal component analysis for high dimensional data

Debashis Paul, Iain M. Johnstone

1202.1242

Unbiased estimation of log normalizing constants with applications to Bayesian cross-validation

Maxime Rischard, Pierre E. Jacob, Natesh Pillai

1810.01382

A survey of statistical network models

Anna Goldenberg, Alice X Zheng, Stephen E Fienberg, Edoardo M Airoldi

0912.5410

Principal Fitted Components for Dimension Reduction in Regression

R. Dennis Cook, Liliana Forzani

0906.3953

Lasso-type recovery of sparse representations for high-dimensional data

Nicolai Meinshausen, Bin Yu

0806.0145

Fisher Lecture: Dimension Reduction in Regression

R. Dennis Cook

0708.3774

Sequential Selection Procedures and False Discovery Rate Control

Max Grazier G'Sell, Stefan Wager, Alexandra Chouldechova, Robert Tibshirani

1309.5352

A Study of Error Variance Estimation in Lasso Regression

Stephen Reid, Robert Tibshirani, Jerome Friedman

1311.5274

Exact Post-Selection Inference for Sequential Regression Procedures

Ryan J. Tibshirani, Jonathan Taylor, Richard Lockhart, Robert Tibshirani

1401.3889

Asymptotic normality and optimalities in estimation of large Gaussian graphical models

Zhao Ren, Tingni Sun, Cun-Hui Zhang, Harrison H. Zhou

1309.6024

Selecting the number of principal components: estimation of the true rank of a noisy matrix

Yunjin Choi, Jonathan Taylor, Robert Tibshirani

1410.8260

Post-selection point and interval estimation of signal sizes in Gaussian samples

Stephen Reid, Jonathan Taylor, Robert Tibshirani

1405.3340

A significance test for the lasso

Richard Lockhart, Jonathan Taylor, Ryan J. Tibshirani, Robert Tibshirani

1301.7161

L1-Penalization for Mixture Regression Models

Nicolas Städler, Peter Bühlmann, Sara van de Geer

1202.6046

Outlier Detection Using Nonconvex Penalized Regression

Yiyuan She, Art B. Owen

1006.2592

Statistical significance in high-dimensional linear models

Peter Bühlmann

1202.1377

Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory

Adel Javanmard, Andrea Montanari

1301.4240

Wavelet methods in statistics: Some recent developments and their applications

Anestis Antoniadis

0712.0283

Learning Optimized Risk Scores

Berk Ustun, Cynthia Rudin

1610.00168

Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes

Yichi Zhang, Eric B. Laber, Anastasios Tsiatis, Marie Davidian

1504.07715

Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap

Qingyuan Zhao, Dylan S. Small, Bhaswar B. Bhattacharya

1711.11286

Revisiting Differentially Private Hypothesis Tests for Categorical Data

Yue Wang, Jaewoo Lee, Daniel Kifer

1511.03376

On Markov chain Monte Carlo methods for tall data

Rémi Bardenet, Arnaud Doucet, Chris Holmes

1505.02827

The Geometric Foundations of Hamiltonian Monte Carlo

M. J. Betancourt, Simon Byrne, Samuel Livingstone, Mark Girolami

1410.5110

Metropolis Sampling

Luca Martino, Victor Elvira

1704.04629

Better together? Statistical learning in models made of modules

Pierre E. Jacob, Lawrence M. Murray, Chris C. Holmes, Christian P. Robert

1708.08719

Rapid Mixing of Hamiltonian Monte Carlo on Strongly Log-Concave Distributions

Oren Mangoubi, Aaron Smith

1708.07114

The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method

Alexandre Bouchard-Côté, Sebastian J. Vollmer, Arnaud Doucet

1510.02451

Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlo

Paul Fearnhead, Joris Bierkens, Murray Pollock, Gareth O Roberts

1611.07873

Differentially Private Model Selection with Penalized and Constrained Likelihood

Jing Lei, Anne-Sophie Charest, Aleksandra Slavkovic, Adam Smith, Stephen Fienberg

1607.04204

Statistical Inference in Mean-Field Variational Bayes

Wei Han, Yun Yang

1911.01525

A Discrete Bouncy Particle Sampler

Chris Sherlock, Alexandre H. Thiery

1707.05200

Consistency of Variational Bayes Inference for Estimation and Model Selection in Mixtures

Badr-Eddine Chérief-Abdellatif, Pierre Alquier

1805.05054

Quasi-concave density estimation

Roger Koenker, Ivan Mizera

1007.4013