Faculty

Institution

Unit

Venue

Martin Wainwright

Professor

University of California, Berkeley

Affiliations

College of Computing, Data Science, and Society

Department of Electrical Engineering and Computer Sciences

Department of Statistics

College of Engineering

Electrical Engineering & Computer Sciences

Martin Wainwright is currently serving as a College Of Computing, Data Science, And Society Professor at University of California, Berkeley.

Honors

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Experiences

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Education

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Martin Wainwright

University of California, Berkeley

Professor

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machine learning computer science applied mathematics statistics information theory electrical engineering mathematical optimization machine learning research statistical inference applied statistics estimation theory signal processing high-dimensional statistics graph theory computer vision model selection statistical methodology computational statistics information technology graphical models high dimensions computational learning theory kernel methods pairwise comparisons bayesian analysis bibliometrics high-dimensional method message passing computational optimization neuroinformatics stochastic optimization mixture distribution information-theoretic limits mathematical programming computational intelligence image denoising learning theory statistical signal processing mixture analysis approximate inference surrogate loss functions numerical algorithms combinatorial optimization linear programming matrix analysis supervised learning markov chain monte carlo bayesian inference transaction processing graph analysis monte carlo methods monte carlo sampling convex optimization statistical learning theory wavelet domain local privacy dual averaging optimal rates minimax rates gradient methods natural images nonparametric regression error floors mixing time statistical efficiency automatic control discrete algorithms information fusion networked system design distributed systems logistic regression reproducing kernel methods educational theory error correction hardware emulation graph processing

Video Presentations

Notable Scholarly Works

Notable Federal Grants

Collaborators

Near-Optimal Inference in Adaptive Linear Regression. The Annals of Statistics (2025)

Koulik KhamaruYash DeshpandeLester MackeyMartin J. Wainwright

near-optimal inferenceadaptive linear regressionapplied mathematicsmachine learning

A Diffusion Process Perspective on Posterior Contraction Rates for Parameters. SIAM Journal on Mathematics of Data Science (2024)

Wenlong MouNhat HoMartin WainwrightPeter BartlettMichael Jordan

applied mathematicsdiffusion processdata scienceposterior contraction ratesdiffusionstatistics

Instance-Optimality in Optimal Value Estimation: Adaptivity via Variance-Reduced Q -Learning. IEEE Transactions on Information Theory (2024)

Eric XiaKoulik KhamaruMartin J. WainwrightMichael I. Jordan

optimal value estimationinformation theorycomputer sciencemachine learningdata scienceestimation theory

Noisy Recovery From Random Linear Observations: Sharp Minimax Rates Under Elliptical Constraints. The Annals of Statistics (2024)

Reese PathakMartin J. WainwrightLin Xiao

applied mathematicsmachine learningelliptical constraintsnoisy recoveryrandom linear observations

Optimal and Instance-Dependent Guarantees for Markovian Linear Stochastic Approximation. Mathematical Statistics and Learning (2024)

Wenlong MouAshwin PananjadyMartin J. WainwrightPeter L. Bartlett

approximation theorymathematical optimizationapplied mathematicsinstance-dependent guaranteesstochastic optimizationestimation theory

Optimal Policy Evaluation Using Kernel-Based Temporal Difference Methods. The Annals of Statistics (2024)

Yaqi DuanMengdi WangMartin J. Wainwright

systems engineeringapplied mathematicscontrol optimizationoptimal policy evaluationdecision sciencepolicy evaluationdecision theory

When is It Worthwhile to Jackknife? Breaking the Quadratic Barrier for Z-Estimators. arXiv (Cornell University) (2024)

Lin, LicongSu, FangzhouMou, WenlongDing, PengWainwright, Martin

theoretical analysisquadratic barrierapplied mathematicscomputer sciencehigh-dimensional statistics

A Decorrelation Method for General Regression Adjustment in Randomized Experiments. arXiv (Cornell University) (2023)

Su, FangzhouMou, WenlongDing, PengWainwright, Martin J.

randomized experimentsgeneral regression adjustmentapplied statisticsdecorrelation methodstatistics

Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing. SSRN Electronic Journal (2023)

Junhui CaiRan ChenMartin J. WainwrightLinda Zhao

computational social sciencesocial science researcheconomicsinterpretable modeljoint assortment-pricinghigh-dimensional contextual banditscomputer sciencemachine learningdecision sciencesocial sciences

Kernel-Based Off-Policy Estimation Without Overlap: Instance Optimality Beyond Semiparametric Efficiency. arXiv (Cornell University) (2023)

Mou, WenlongDing, PengWainwright, Martin J.Bartlett, Peter L.

kernel-based off-policy estimationinstance optimalitymachine learningdata sciencesemiparametric efficiencykernel methods

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Martin Wainwright

Professor

University of California, Berkeley

Affiliations

College of Computing, Data Science, and Society

Department of Electrical Engineering and Computer Sciences

Department of Statistics

College of Engineering

Electrical Engineering & Computer Sciences

Martin Wainwright is currently serving as a College Of Computing, Data Science, And Society Professor at University of California, Berkeley.

Honors

Create free account to unlock more features

Experiences

Create free account to unlock more features

Education

Create free account to unlock more features

Martin Wainwright

University of California, Berkeley

Professor

You can click a keyword to filter the displayed items

Video Presentations

Notable Scholarly Works

Notable Federal Grants

Collaborators

Near-Optimal Inference in Adaptive Linear Regression. The Annals of Statistics (2025)

Koulik KhamaruYash DeshpandeLester MackeyMartin J. Wainwright

near-optimal inferenceadaptive linear regressionapplied mathematicsmachine learning

A Diffusion Process Perspective on Posterior Contraction Rates for Parameters. SIAM Journal on Mathematics of Data Science (2024)

Wenlong MouNhat HoMartin WainwrightPeter BartlettMichael Jordan

applied mathematicsdiffusion processdata scienceposterior contraction ratesdiffusionstatistics

Instance-Optimality in Optimal Value Estimation: Adaptivity via Variance-Reduced Q -Learning. IEEE Transactions on Information Theory (2024)

Eric XiaKoulik KhamaruMartin J. WainwrightMichael I. Jordan

optimal value estimationinformation theorycomputer sciencemachine learningdata scienceestimation theory

Noisy Recovery From Random Linear Observations: Sharp Minimax Rates Under Elliptical Constraints. The Annals of Statistics (2024)

Reese PathakMartin J. WainwrightLin Xiao

applied mathematicsmachine learningelliptical constraintsnoisy recoveryrandom linear observations

Optimal and Instance-Dependent Guarantees for Markovian Linear Stochastic Approximation. Mathematical Statistics and Learning (2024)

Wenlong MouAshwin PananjadyMartin J. WainwrightPeter L. Bartlett

approximation theorymathematical optimizationapplied mathematicsinstance-dependent guaranteesstochastic optimizationestimation theory

Optimal Policy Evaluation Using Kernel-Based Temporal Difference Methods. The Annals of Statistics (2024)

Yaqi DuanMengdi WangMartin J. Wainwright

systems engineeringapplied mathematicscontrol optimizationoptimal policy evaluationdecision sciencepolicy evaluationdecision theory

When is It Worthwhile to Jackknife? Breaking the Quadratic Barrier for Z-Estimators. arXiv (Cornell University) (2024)

Lin, LicongSu, FangzhouMou, WenlongDing, PengWainwright, Martin

theoretical analysisquadratic barrierapplied mathematicscomputer sciencehigh-dimensional statistics

A Decorrelation Method for General Regression Adjustment in Randomized Experiments. arXiv (Cornell University) (2023)

Su, FangzhouMou, WenlongDing, PengWainwright, Martin J.

randomized experimentsgeneral regression adjustmentapplied statisticsdecorrelation methodstatistics

Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing. SSRN Electronic Journal (2023)

Junhui CaiRan ChenMartin J. WainwrightLinda Zhao

Kernel-Based Off-Policy Estimation Without Overlap: Instance Optimality Beyond Semiparametric Efficiency. arXiv (Cornell University) (2023)

Mou, WenlongDing, PengWainwright, Martin J.Bartlett, Peter L.

kernel-based off-policy estimationinstance optimalitymachine learningdata sciencesemiparametric efficiencykernel methods

1
2
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5
30
10 / page

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