Peter Bartlett

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berkeley college

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

Alekh Agarwal

Mohammad Ghavamzadeh

Yudong Chen

Nhat Ho

Kannan Ramchandran

Raman Arora

Dylan J. Foster

Matus Telgarsky

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