Faculty

Institution

Unit

Venue

Peter Bartlett

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

Peter Bartlett 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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Peter Bartlett

University of California, Berkeley

Professor

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machine learning computer science applied mathematics statistics reinforcement learning deep learning artificial intelligence machine learning research learning theory mathematical optimization neuroscience neural computation pattern recognition supervised learning information theory probability theory computational intelligence computational learning theory estimation theory classification method online learning adversarial machine learning neural networks distributed learning gradient descent machine intelligence convex analysis computational optimization neuroinformatics stochastic optimization support vector machines statistical learning theory kernel methods oracle inequalities sample complexity intelligent computing incremental learning convex optimization linear programming sparse representations function theory risk bounds fault diagnostics scale-sensitive dimensions one-inclusion mistake bounds knowledge discovery mathematical programming bayesian analysis adaptive learning adaptive optimization algorithmic learning educational theory point clouds regularization (mathematics) bayesian inference bibliometrics theoretical foundations efficient agnostic learning optimal strategies rademacher complexity nesterov acceleration identity initialization expert advice combined classifiers scientometrics reliability engineering decision science information technology scientific computing machine vision combinatorial optimization decision analysis semidefinite programming gaussian analysis gaussian process cost-sensitive machine learning data science learning classifier system

Video Presentations

Notable Scholarly Works

Notable Federal Grants

Collaborators

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

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

Unveiling the Family of Optimal Accelerations in Minimax Optimization and Fixed-Point Algorithms. bioRxiv (Cold Spring Harbor Laboratory) (2024)

Peter BartlettChris Junchi LiJingfeng WuBin Yu

optimal accelerationscomputational optimizationmachine learningminimax optimizationfixed-point algorithms

Bayesian Robustness: A Nonasymptotic Viewpoint. Journal of the American Statistical Association (2023)

Kush BhatiaYi-An MaAnca D. DraganPeter L. BartlettMichael I. Jordan

bayesian inferencebayesian analysisprobability theorystatistical theorystatistical inferencesamplingapplied statisticsestimation theorybayesian robustnessstatistics

Corrigendum to “Prediction, Learning, Uniform Convergence, and Scale-Sensitive Dimensions” [J. Comput. Syst. Sci. 56 (2) (1998) 174–190]. Journal of Computer and System Sciences (2023)

Peter L. BartlettPhilip M. Long

uniform convergencescale-sensitive dimensionscomputer sciencemachine learning

Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates Without Convexity. Bernoulli (2022)

Wenlong MouNicolas FlammarionMartin J. WainwrightPeter L. Bartlett

improved boundslangevin diffusionscomputer sciencemachine learningnear-optimal rates

Oracle Lower Bounds for Stochastic Gradient Sampling Algorithms. Bernoulli (2022)

Niladri S. ChatterjiPeter L. BartlettPhilip M. Long

theory of computationmathematical optimizationapplied mathematicsoracle lower bounds

Adversarial Examples in Multi-Layer Random Relu Networks. arXiv (Cornell University) (2021)

Peter L. BartlettSébastien BubeckYeshwanth Cherapanamjeri

adversarial machine learningcomputer scienceadversarial examplesmachine learning

Agnostic Learning With Unknown Utilities. arXiv (Cornell University) (2021)

Kush BhatiaPeter L. BartlettAnca D. DraganJacob Steinhardt

unknown utilitiesagnostic learninglearning theorymachine learningcognitive science

An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. arXiv (Cornell University) (2021)

Aldo PacchianoPeter L. BartlettMichael I. Jordan

behavioral sciencesreinforcement learninginstance-dependent analysismachine learning

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Peter Bartlett

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

Peter Bartlett 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

Peter Bartlett

University of California, Berkeley

Professor

You can click a keyword to filter the displayed items

Video Presentations

Notable Scholarly Works

Notable Federal Grants

Collaborators

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

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

Unveiling the Family of Optimal Accelerations in Minimax Optimization and Fixed-Point Algorithms. bioRxiv (Cold Spring Harbor Laboratory) (2024)

Peter BartlettChris Junchi LiJingfeng WuBin Yu

optimal accelerationscomputational optimizationmachine learningminimax optimizationfixed-point algorithms

Bayesian Robustness: A Nonasymptotic Viewpoint. Journal of the American Statistical Association (2023)

Kush BhatiaYi-An MaAnca D. DraganPeter L. BartlettMichael I. Jordan

bayesian inferencebayesian analysisprobability theorystatistical theorystatistical inferencesamplingapplied statisticsestimation theorybayesian robustnessstatistics

Corrigendum to “Prediction, Learning, Uniform Convergence, and Scale-Sensitive Dimensions” [J. Comput. Syst. Sci. 56 (2) (1998) 174–190]. Journal of Computer and System Sciences (2023)

Peter L. BartlettPhilip M. Long

uniform convergencescale-sensitive dimensionscomputer sciencemachine learning

Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates Without Convexity. Bernoulli (2022)

Wenlong MouNicolas FlammarionMartin J. WainwrightPeter L. Bartlett

improved boundslangevin diffusionscomputer sciencemachine learningnear-optimal rates

Oracle Lower Bounds for Stochastic Gradient Sampling Algorithms. Bernoulli (2022)

Niladri S. ChatterjiPeter L. BartlettPhilip M. Long

theory of computationmathematical optimizationapplied mathematicsoracle lower bounds

Adversarial Examples in Multi-Layer Random Relu Networks. arXiv (Cornell University) (2021)

Peter L. BartlettSébastien BubeckYeshwanth Cherapanamjeri

adversarial machine learningcomputer scienceadversarial examplesmachine learning

Agnostic Learning With Unknown Utilities. arXiv (Cornell University) (2021)

Kush BhatiaPeter L. BartlettAnca D. DraganJacob Steinhardt

unknown utilitiesagnostic learninglearning theorymachine learningcognitive science

An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. arXiv (Cornell University) (2021)

Aldo PacchianoPeter L. BartlettMichael I. Jordan

behavioral sciencesreinforcement learninginstance-dependent analysismachine learning

1
2
3
4
5
27
10 / page

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multidisciplinary ai

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