Peter L. Bartlett

ORCID

Publication Activity (10 Years)

Years Active: 1991-2024
Publications (10 Years): 153

Top Venues

Publications

Saptarshi Chakraborty, Peter L. Bartlett
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data. ICLR (2024)
Saptarshi Chakraborty, Peter L. Bartlett
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data. CoRR (2024)
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? ICLR (2024)
Gautam Goel, Peter L. Bartlett
Can a transformer represent a Kalman filter? L4DC (2024)
Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu
Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency. CoRR (2024)
Peter L. Bartlett, Philip M. Long
Corrigendum to "Prediction, learning, uniform convergence, and scale-sensitive dimensions" [J. Comput. Syst. Sci. 56 (2) (1998) 174-190]. J. Comput. Syst. Sci. 140 (2024)
Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu
Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency. COLT (2024)
Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett
Contextual Bandits with Stage-wise Constraints. CoRR (2024)
Licong Lin, Jingfeng Wu, Sham M. Kakade, Peter L. Bartlett, Jason D. Lee
Scaling Laws in Linear Regression: Compute, Parameters, and Data. CoRR (2024)
Wenlong Mou, Nhat Ho, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
A Diffusion Process Perspective on Posterior Contraction Rates for Parameters. SIAM J. Math. Data Sci. 6 (2) (2024)
Yuhang Cai, Jingfeng Wu, Song Mei, Michael Lindsey, Peter L. Bartlett
Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization. CoRR (2024)
Ruiqi Zhang, Jingfeng Wu, Peter L. Bartlett
In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization. CoRR (2024)
Saptarshi Chakraborty, Peter L. Bartlett
On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension. CoRR (2024)
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu
Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. ICLR (2023)
Alexander Tsigler, Peter L. Bartlett
Benign overfitting in ridge regression. J. Mach. Learn. Res. 24 (2023)
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. CoRR (2023)
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro
Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization. COLT (2023)
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? CoRR (2023)
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nati Srebro
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. NeurIPS (2023)
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro
Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization. CoRR (2023)
Ruiqi Zhang, Spencer Frei, Peter L. Bartlett
Trained Transformers Learn Linear Models In-Context. CoRR (2023)
Peter L. Bartlett, Philip M. Long
Prediction, Learning, Uniform Convergence, and Scale-sensitive Dimensions. CoRR (2023)
Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. ALT (2023)
Philip M. Long, Peter L. Bartlett
Sharpness-Aware Minimization and the Edge of Stability. CoRR (2023)
Wenlong Mou, Ashwin Pananjady, Martin J. Wainwright, Peter L. Bartlett
Optimal and instance-dependent guarantees for Markovian linear stochastic approximation. COLT (2022)
Wenlong Mou, Martin J. Wainwright, Peter L. Bartlett
Off-policy estimation of linear functionals: Non-asymptotic theory for semi-parametric efficiency. CoRR (2022)
Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett
The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks. J. Mach. Learn. Res. 23 (2022)
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu
Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. CoRR (2022)
Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan
Optimal Mean Estimation without a Variance. COLT (2022)
Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data. COLT (2022)
Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data. CoRR (2022)
Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, Peter L. Bartlett
An Efficient Sampling Algorithm for Non-smooth Composite Potentials. J. Mach. Learn. Res. 23 (2022)
Juan C. Perdomo, Akshay Krishnamurthy, Peter L. Bartlett, Sham M. Kakade
A Sharp Characterization of Linear Estimators for Offline Policy Evaluation. CoRR (2022)
Wenlong Mou, Koulik Khamaru, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
Optimal variance-reduced stochastic approximation in Banach spaces. CoRR (2022)
Peter L. Bartlett, Philip M. Long, Olivier Bousquet
The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima. CoRR (2022)
Peter L. Bartlett, Piotr Indyk, Tal Wagner
Generalization Bounds for Data-Driven Numerical Linear Algebra. CoRR (2022)
Aldo Pacchiano, Ofir Nachum, Nilesh Tripuraneni, Peter L. Bartlett
Joint Representation Training in Sequential Tasks with Shared Structure. CoRR (2022)
Peter L. Bartlett, Piotr Indyk, Tal Wagner
Generalization Bounds for Data-Driven Numerical Linear Algebra. COLT (2022)
Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett
Random Feature Amplification: Feature Learning and Generalization in Neural Networks. CoRR (2022)
Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum
Near Optimal Policy Optimization via REPS. NeurIPS (2021)
Peter L. Bartlett, Philip M. Long
Failures of Model-dependent Generalization Bounds for Least-norm Interpolation. J. Mach. Learn. Res. 22 (2021)
Jeffrey Chan, Aldo Pacchiano, Nilesh Tripuraneni, Yun S. Song, Peter L. Bartlett, Michael I. Jordan
Parallelizing Contextual Linear Bandits. CoRR (2021)
Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett
When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations? CoRR (2021)
Kush Bhatia, Ashwin Pananjady, Peter L. Bartlett, Anca D. Dragan, Martin J. Wainwright
Preference learning along multiple criteria: A game-theoretic perspective. CoRR (2021)
Kush Bhatia, Peter L. Bartlett, Anca D. Dragan, Jacob Steinhardt
Agnostic learning with unknown utilities. CoRR (2021)
Peter L. Bartlett, Andrea Montanari, Alexander Rakhlin
Deep learning: a statistical viewpoint. CoRR (2021)
Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang
Stochastic Bandits with Linear Constraints. AISTATS (2021)
Kush Bhatia, Peter L. Bartlett, Anca D. Dragan, Jacob Steinhardt
Agnostic Learning with Unknown Utilities. ITCS (2021)
Peter L. Bartlett, Sébastien Bubeck, Yeshwanth Cherapanamjeri
Adversarial Examples in Multi-Layer Random ReLU Networks. NeurIPS (2021)
Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
On the Theory of Reinforcement Learning with Once-per-Episode Feedback. CoRR (2021)
Juan C. Perdomo, Max Simchowitz, Alekh Agarwal, Peter L. Bartlett
Towards a Dimension-Free Understanding of Adaptive Linear Control. CoRR (2021)
Peter L. Bartlett, Sébastien Bubeck, Yeshwanth Cherapanamjeri
Adversarial Examples in Multi-Layer Random ReLU Networks. CoRR (2021)
Wenlong Mou, Ashwin Pananjady, Martin J. Wainwright, Peter L. Bartlett
Optimal and instance-dependent guarantees for Markovian linear stochastic approximation. CoRR (2021)
Peter L. Bartlett, Andrea Montanari, Alexander Rakhlin
Deep learning: a statistical viewpoint. Acta Numer. 30 (2021)
Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. CoRR (2021)
Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum
Near Optimal Policy Optimization via REPS. CoRR (2021)
Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett
The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks. CoRR (2021)
Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett
When Does Gradient Descent with Logistic Loss Find Interpolating Two-Layer Networks? J. Mach. Learn. Res. 22 (2021)
Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett
When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations? COLT (2021)
Lin Chen, Bruno Scherrer, Peter L. Bartlett
Infinite-Horizon Offline Reinforcement Learning with Linear Function Approximation: Curse of Dimensionality and Algorithm. CoRR (2021)
Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
On the Theory of Reinforcement Learning with Once-per-Episode Feedback. NeurIPS (2021)
Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm. J. Mach. Learn. Res. 22 (2021)
Juan C. Perdomo, Max Simchowitz, Alekh Agarwal, Peter L. Bartlett
Towards a Dimension-Free Understanding of Adaptive Linear Control. COLT (2021)
Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett
When does gradient descent with logistic loss find interpolating two-layer networks? CoRR (2020)
Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
Accelerated Message Passing for Entropy-Regularized MAP Inference. CoRR (2020)
Kush Bhatia, Ashwin Pananjady, Peter L. Bartlett, Anca D. Dragan, Martin J. Wainwright
Preference learning along multiple criteria: A game-theoretic perspective. NeurIPS (2020)
Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan
On Thompson Sampling with Langevin Algorithms. CoRR (2020)
Yeshwanth Cherapanamjeri, Efe Aras, Nilesh Tripuraneni, Michael I. Jordan, Nicolas Flammarion, Peter L. Bartlett
Optimal Robust Linear Regression in Nearly Linear Time. CoRR (2020)
Thanh Tan Nguyen, Nan Ye, Peter L. Bartlett
Greedy Convex Ensemble. IJCAI (2020)
Niladri S. Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter L. Bartlett
Langevin Monte Carlo without smoothness. AISTATS (2020)
Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang
Stochastic Bandits with Linear Constraints. CoRR (2020)
Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan
Optimal Mean Estimation without a Variance. CoRR (2020)
Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
Accelerated Message Passing for Entropy-Regularized MAP Inference. ICML (2020)
Xiang Cheng, Dong Yin, Peter L. Bartlett, Michael I. Jordan
Stochastic Gradient and Langevin Processes. ICML (2020)
Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett
On Approximate Thompson Sampling with Langevin Algorithms. ICML (2020)
Hossein Mobahi, Mehrdad Farajtabar, Peter L. Bartlett
Self-Distillation Amplifies Regularization in Hilbert Space. NeurIPS (2020)
Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration. CoRR (2020)
Niladri S. Chatterji, Peter L. Bartlett, Philip M. Long
Oracle lower bounds for stochastic gradient sampling algorithms. CoRR (2020)
Peter L. Bartlett, Philip M. Long
Failures of model-dependent generalization bounds for least-norm interpolation. CoRR (2020)
Niladri S. Chatterji, Vidya Muthukumar, Peter L. Bartlett
OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits. AISTATS (2020)
Hossein Mobahi, Mehrdad Farajtabar, Peter L. Bartlett
Self-Distillation Amplifies Regularization in Hilbert Space. CoRR (2020)
Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter L. Bartlett, Martin J. Wainwright
Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems. J. Mach. Learn. Res. 21 (2020)
Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration. COLT (2020)
Aldo Pacchiano, Christoph Dann, Claudio Gentile, Peter L. Bartlett
Regret Bound Balancing and Elimination for Model Selection in Bandits and RL. CoRR (2020)
Yeshwanth Cherapanamjeri, Peter L. Bartlett
Testing Markov Chains without Hitting. CoRR (2019)
Niladri S. Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter L. Bartlett
Langevin Monte Carlo without Smoothness. CoRR (2019)
Yeshwanth Cherapanamjeri, Peter L. Bartlett
Testing Symmetric Markov Chains Without Hitting. COLT (2019)
Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, Peter L. Bartlett
An Efficient Sampling Algorithm for Non-smooth Composite Potentials. CoRR (2019)
Xiang Cheng, Peter L. Bartlett, Michael I. Jordan
Quantitative Central Limit Theorems for Discrete Stochastic Processes. CoRR (2019)
Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm. CoRR (2019)
Peter L. Bartlett, Victor Gabillon, Jennifer Healey, Michal Valko
Scale-free adaptive planning for deterministic dynamics & discounted rewards. ICML (2019)
Xiang Cheng, Dong Yin, Peter L. Bartlett, Michael I. Jordan
Convergence of Langevin-Like Stochastic Processes with Non-Convex Potential State-Dependent Noise. CoRR (2019)
Kush Bhatia, Yi-An Ma, Anca D. Dragan, Peter L. Bartlett, Michael I. Jordan
Bayesian Robustness: A Nonasymptotic Viewpoint. CoRR (2019)
Peter L. Bartlett, Victor Gabillon, Michal Valko
A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption. ALT (2019)
Yeshwanth Cherapanamjeri, Nicolas Flammarion, Peter L. Bartlett
Fast Mean Estimation with Sub-Gaussian Rates. CoRR (2019)
Wenlong Mou, Nhat Ho, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
Sampling for Bayesian Mixture Models: MCMC with Polynomial-Time Mixing. CoRR (2019)
Dong Yin, Kannan Ramchandran, Peter L. Bartlett
Rademacher Complexity for Adversarially Robust Generalization. ICML (2019)
Dong Yin, Yudong Chen, Kannan Ramchandran, Peter L. Bartlett
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning. ICML (2019)
Niladri S. Chatterji, Vidya Muthukumar, Peter L. Bartlett
OSOM: A Simultaneously Optimal Algorithm for Multi-Armed and Linear Contextual Bandits. CoRR (2019)
Yi-An Ma, Niladri S. Chatterji, Xiang Cheng, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan
Is There an Analog of Nesterov Acceleration for MCMC? CoRR (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Peter L. Bartlett