Martin J. Wainwright

Fast Score-Based Sampling via Log-Concave Reductions COLT 2026 Instability, Computational Efficiency and Statistical Accuracy JMLR 2025 Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces NeurIPS 2024 A finite-sample analysis of multi-step temporal difference estimates L4DC 2023 Instance-Dependent Confidence and Early Stopping for Reinforcement Learning JMLR 2023 An Efficient Sampling Algorithm for Non-smooth Composite Potentials JMLR 2022 Bellman Residual Orthogonalization for Offline Reinforcement Learning NeurIPS 2022 High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm JMLR 2021 Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning NeurIPS 2021 Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems JMLR 2020 Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients JMLR 2020 FedSplit: an algorithmic framework for fast federated optimization NeurIPS 2020 Preference learning along multiple criteria: A game-theoretic perspective NeurIPS 2020 Convergence Guarantees for a Class of Non-convex and Non-smooth Optimization Problems JMLR 2019 Log-concave sampling: Metropolis-Hastings algorithms are fast JMLR 2019 Low Permutation-rank Matrices: Structural Properties and Noisy Completion JMLR 2019 L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data ICLR 2019 Breaking the 1/√n Barrier: Faster Rates for Permutation-based Models in Polynomial Time COLT 2018 Fast MCMC Sampling Algorithms on Polytopes JMLR 2018 Simple, Robust and Optimal Ranking from Pairwise Comparisons JMLR 2018 Theoretical guarantees for EM under misspecified Gaussian mixture models NeurIPS 2018 A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control NeurIPS 2017 Convexified Convolutional Neural Networks ICML 2017 Early stopping for kernel boosting algorithms: A general analysis with localized complexities NeurIPS 2017 Kernel Feature Selection via Conditional Covariance Minimization NeurIPS 2017 Online control of the false discovery rate with decaying memory NeurIPS 2017 Statistical and Computational Guarantees for the Baum-Welch Algorithm JMLR 2017 A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees JMLR 2016 Asymptotic behavior of \ell_p-based Laplacian regularization in semi-supervised learning COLT 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 NeurIPS 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 Belief Propagation for Continuous State Spaces: Stochastic Message-Passing with Quantitative Guarantees JMLR 2013 Communication-Efficient Algorithms for Statistical Optimization JMLR 2013 Information-theoretic lower bounds for distributed statistical estimation with communication constraints NeurIPS 2013 Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation NeurIPS 2013 Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima NeurIPS 2013 Communication-Efficient Algorithms for Statistical Optimization NeurIPS 2012 Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods NeurIPS 2012 Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming JMLR 2012 Privacy Aware Learning NeurIPS 2012 Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise JMLR 2012 Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions NeurIPS 2012 Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses NeurIPS 2012 A More Powerful Two-Sample Test in High Dimensions using Random Projection NeurIPS 2011 High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity NeurIPS 2011 Distributed Dual Averaging In Networks NeurIPS 2010 Fast global convergence rates of gradient methods for high-dimensional statistical recovery NeurIPS 2010 High-dimensional Variable Selection with Sparse Random Projections: Measurement Sparsity and Statistical Efficiency JMLR 2010 Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes JMLR 2010 Restricted Eigenvalue Properties for Correlated Gaussian Designs JMLR 2010 A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers NeurIPS 2009 Information-theoretic lower bounds on the oracle complexity of convex optimization NeurIPS 2009 Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness NeurIPS 2009 High-dimensional support union recovery in multivariate regression NeurIPS 2008 Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \boldmath$\ell_1$-regularized MLE NeurIPS 2008 Phase transitions for high-dimensional joint support recovery NeurIPS 2008 Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization NeurIPS 2007 Loop Series and Bethe Variational Bounds in Attractive Graphical Models NeurIPS 2007 Estimating the “Wrong” Graphical Model: Benefits in the Computation-Limited Setting JMLR 2006 High-Dimensional Graphical Model Selection Using ℓ_1-Regularized Logistic Regression NeurIPS 2006