Peter Bartlett

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berkeley college

Parallelizing Contextual Linear Bandits

Standard approaches to decision-making under uncertainty focus on sequen...
05/21/2021 ∙ by Jeffrey Chan, et al. ∙ 9 ∙ share

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Towards a Dimension-Free Understanding of Adaptive Linear Control

We study the problem of adaptive control of the linear quadratic regulat...
03/19/2021 ∙ by Juan C. Perdomo, et al. ∙ 0 ∙ share

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Near Optimal Policy Optimization via REPS

Since its introduction a decade ago, relative entropy policy search (REP...
03/17/2021 ∙ by Aldo Pacchiano, et al. ∙ 0 ∙ share

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Regret Bound Balancing and Elimination for Model Selection in Bandits and RL

We propose a simple model selection approach for algorithms in stochasti...
12/24/2020 ∙ by Aldo Pacchiano, et al. ∙ 0 ∙ share

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Accelerated Message Passing for Entropy-Regularized MAP Inference

Maximum a posteriori (MAP) inference in discrete-valued Markov random fi...
07/01/2020 ∙ by Jonathan N. Lee, et al. ∙ 11 ∙ share

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Stochastic Bandits with Linear Constraints

We study a constrained contextual linear bandit setting, where the goal ...
06/17/2020 ∙ by Aldo Pacchiano, et al. ∙ 0 ∙ share

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Dropout: Explicit Forms and Capacity Control

We investigate the capacity control provided by dropout in various machi...
03/06/2020 ∙ by Raman Arora, et al. ∙ 0 ∙ share

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A Diffusion Process Perspective on Posterior Contraction Rates for Parameters

We show that diffusion processes can be exploited to study the posterior...
09/03/2019 ∙ by Wenlong Mou, et al. ∙ 0 ∙ share

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Is There an Analog of Nesterov Acceleration for MCMC?

We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as ...
02/04/2019 ∙ by Yi-an Ma, et al. ∙ 4 ∙ share

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Rademacher Complexity for Adversarially Robust Generalization

Many machine learning models are vulnerable to adversarial attacks. It h...
10/29/2018 ∙ by Dong Yin, et al. ∙ 0 ∙ share

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Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning

In this paper, we study robust large-scale distributed learning in the p...
06/14/2018 ∙ by Dong Yin, et al. ∙ 0 ∙ share

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Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

In large-scale distributed learning, security issues have become increas...
03/05/2018 ∙ by Dong Yin, et al. ∙ 0 ∙ share

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Spectrally-normalized margin bounds for neural networks

This paper presents a margin-based multiclass generalization bound for n...
06/26/2017 ∙ by Peter Bartlett, et al. ∙ 0 ∙ share

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Convergence of Langevin MCMC in KL-divergence

Langevin diffusion is a commonly used tool for sampling from a given dis...
05/25/2017 ∙ by Xiang Cheng, et al. ∙ 0 ∙ share

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Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families

We study online learning under logarithmic loss with regular parametric ...
05/19/2013 ∙ by Peter Bartlett, et al. ∙ 0 ∙ share

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Advice-Efficient Prediction with Expert Advice

Advice-efficient prediction with expert advice (in analogy to label-effi...
04/12/2013 ∙ by Yevgeny Seldin, et al. ∙ 0 ∙ share

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Online and Batch Learning Algorithms for Data with Missing Features

We introduce new online and batch algorithms that are robust to data wit...
04/05/2011 ∙ by Afshin Rostamizadeh, et al. ∙ 0 ∙ share

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Peter Bartlett

Featured Co-authors

Michael I. Jordan

Martin J. Wainwright

Nathan Srebro

Mohammad Ghavamzadeh

Alekh Agarwal

Kannan Ramchandran

Nhat Ho

Yudong Chen

Ofir Nachum

Aldo Pacchiano

Max Simchowitz

Parallelizing Contextual Linear Bandits

Towards a Dimension-Free Understanding of Adaptive Linear Control

Near Optimal Policy Optimization via REPS

Regret Bound Balancing and Elimination for Model Selection in Bandits and RL

Accelerated Message Passing for Entropy-Regularized MAP Inference

Stochastic Bandits with Linear Constraints

Dropout: Explicit Forms and Capacity Control

A Diffusion Process Perspective on Posterior Contraction Rates for Parameters

Is There an Analog of Nesterov Acceleration for MCMC?

Rademacher Complexity for Adversarially Robust Generalization

Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning

Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

Spectrally-normalized margin bounds for neural networks

Convergence of Langevin MCMC in KL-divergence

Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families

Advice-Efficient Prediction with Expert Advice

Online and Batch Learning Algorithms for Data with Missing Features