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Martin J. Wainwright

63 papers · 2006–2025 · 6 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer πŸ—ΊοΈ Taxonomy Completionist (23) 🐝 Cross-Pollinator (13) 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (19) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (6) 🏠 Conference Loyalist (32) 🌟 Keyword Trendsetter Combo (10) 🌱 Topic Pioneer πŸ”¬ Deep Specialist (18) πŸ† Keyword Champion 🀝 Dynamic Duo (16) πŸ—ƒοΈ Keyword Collector (160) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (20) ⚑ Prolific Year (6) ❓ The Questioner πŸ’Ž Century Club (63)

Conferences

NIPS (32) JMLR (25) COLT (3) ICLR (1) ICML (1) L4DC (1)

Top co-authors

Michael I. Jordan (16) Bin Yu (10) Yuchen Zhang (6) Michael I Jordan (6) Peter L. Bartlett (5) Sahand Negahban (5) John C. Duchi (5) Alekh Agarwal (5) Garvesh Raskutti (5) Raaz Dwivedi (5)

Research topics

Privacy (2) Differential Privacy (1) Statistics (1) Probability (1)

Keywords

convergence rate (9) convex optimization (7) high-dimensional statistics (6) graphical model (5) markov random field (5) sparse optimization (5) mixing time (5) distributed optimization (4) statistical learning theory (4) statistical learning (4) sparse regression (4) markov chain monte carlo (4) sample complexity (4) information theory (4) kernel methods (4) belief propagation (3) stochastic optimization (3) structure learning (3) high-dimensional regression (3) hypothesis testing (3)

Papers

Instability, Computational Efficiency and Statistical Accuracy JMLR 2025
Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces NIPS 2024
Instance-Dependent Confidence and Early Stopping for Reinforcement Learning JMLR 2023
A finite-sample analysis of multi-step temporal difference estimates L4DC 2023
Bellman Residual Orthogonalization for Offline Reinforcement Learning NIPS 2022
An Efficient Sampling Algorithm for Non-smooth Composite Potentials JMLR 2022
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm JMLR 2021
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning NIPS 2021
Preference learning along multiple criteria: A game-theoretic perspective NIPS 2020
Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients JMLR 2020
Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems JMLR 2020
FedSplit: an algorithmic framework for fast federated optimization NIPS 2020
Low Permutation-rank Matrices: Structural Properties and Noisy Completion JMLR 2019
Log-concave sampling: Metropolis-Hastings algorithms are fast JMLR 2019
Convergence Guarantees for a Class of Non-convex and Non-smooth Optimization Problems JMLR 2019
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data ICLR 2019
Breaking the $1/\sqrt{n}$ Barrier: Faster Rates for Permutation-based Models in Polynomial Time COLT 2018
Simple, Robust and Optimal Ranking from Pairwise Comparisons JMLR 2018
Fast MCMC Sampling Algorithms on Polytopes JMLR 2018
Theoretical guarantees for EM under misspecified Gaussian mixture models NIPS 2018
Kernel Feature Selection via Conditional Covariance Minimization NIPS 2017
Online control of the false discovery rate with decaying memory NIPS 2017
Early stopping for kernel boosting algorithms: A general analysis with localized complexities NIPS 2017
A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control NIPS 2017
Convexified Convolutional Neural Networks ICML 2017
Statistical and Computational Guarantees for the Baum-Welch Algorithm JMLR 2017
Asymptotic behavior of \ell_p-based Laplacian regularization in semi-supervised learning COLT 2016
A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees JMLR 2016
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence JMLR 2016
Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares JMLR 2016
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences NIPS 2016
Regularized M-estimators with Nonconvexity: Statistical and Algorithmic Theory for Local Optima JMLR 2015
Early Stopping and Non-parametric Regression: An Optimal Data-dependent Stopping Rule JMLR 2014
Lower Bounds on the Performance of Polynomial-time Algorithms for Sparse Linear Regression COLT 2014
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima NIPS 2013
Information-theoretic lower bounds for distributed statistical estimation with communication constraints NIPS 2013
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation NIPS 2013
Belief Propagation for Continuous State Spaces: Stochastic Message-Passing with Quantitative Guarantees JMLR 2013
Communication-Efficient Algorithms for Statistical Optimization JMLR 2013
Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions NIPS 2012
Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods NIPS 2012
Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming JMLR 2012
Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise JMLR 2012
Communication-Efficient Algorithms for Statistical Optimization NIPS 2012
Privacy Aware Learning NIPS 2012
Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses NIPS 2012
A More Powerful Two-Sample Test in High Dimensions using Random Projection NIPS 2011
High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity NIPS 2011
Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes JMLR 2010
High-dimensional Variable Selection with Sparse Random Projections: Measurement Sparsity and Statistical Efficiency JMLR 2010
Distributed Dual Averaging In Networks NIPS 2010
Fast global convergence rates of gradient methods for high-dimensional statistical recovery NIPS 2010
Restricted Eigenvalue Properties for Correlated Gaussian Designs JMLR 2010
Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness NIPS 2009
Information-theoretic lower bounds on the oracle complexity of convex optimization NIPS 2009
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers NIPS 2009
High-dimensional support union recovery in multivariate regression NIPS 2008
Phase transitions for high-dimensional joint support recovery NIPS 2008
Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \boldmath$\ell_1$-regularized MLE NIPS 2008
Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization NIPS 2007
Loop Series and Bethe Variational Bounds in Attractive Graphical Models NIPS 2007
Estimating the β€œWrong” Graphical Model: Benefits in the Computation-Limited Setting JMLR 2006
High-Dimensional Graphical Model Selection Using $\ell_1$-Regularized Logistic Regression NIPS 2006