Bartlett, Peter L.

129 publications

JMLR 2025 On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension Saptarshi Chakraborty, Peter L. Bartlett

NeurIPS 2024 Fast Best-of-N Decoding via Speculative Rejection Hanshi Sun, Momin Haider, Ruiqi Zhang, Huitao Yang, Jiahao Qiu, Ming Yin, Mengdi Wang, Peter L. Bartlett, Andrea Zanette

NeurIPS 2024 In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization Ruiqi Zhang, Jingfeng Wu, Peter L. Bartlett

COLT 2024 Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu

NeurIPS 2024 Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization Yuhang Cai, Jingfeng Wu, Song Mei, Michael Lindsey, Peter L. Bartlett

NeurIPS 2024 Scaling Laws in Linear Regression: Compute, Parameters, and Data Licong Lin, Jingfeng Wu, Sham M. Kakade, Peter L. Bartlett, Jason D. Lee

JMLR 2024 Sharpness-Aware Minimization and the Edge of Stability Philip M. Long, Peter L. Bartlett

JMLR 2024 Trained Transformers Learn Linear Models In-Context Ruiqi Zhang, Spencer Frei, Peter L. Bartlett

JMLR 2023 Benign Overfitting in Ridge Regression Alexander Tsigler, Peter L. Bartlett

JMLR 2023 Random Feature Amplification: Feature Learning and Generalization in Neural Networks Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett

NeurIPS 2023 The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks Spencer Frei, Gal Vardi, Peter L. Bartlett, Nati Srebro

JMLR 2023 The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima Peter L. Bartlett, Philip M. Long, Olivier Bousquet

JMLR 2022 An Efficient Sampling Algorithm for Non-Smooth Composite Potentials Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, Peter L. Bartlett

JMLR 2022 The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett

NeurIPS 2021 Adversarial Examples in Multi-Layer Random ReLU Networks Peter L. Bartlett, Sebastien Bubeck, Yeshwanth Cherapanamjeri

JMLR 2021 Failures of Model-Dependent Generalization Bounds for Least-Norm Interpolation Peter L. Bartlett, Philip M. Long

JMLR 2021 High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan

NeurIPS 2021 Near Optimal Policy Optimization via REPS Aldo Pacchiano, Jonathan N Lee, Peter L. Bartlett, Ofir Nachum

NeurIPS 2021 On the Theory of Reinforcement Learning with Once-per-Episode Feedback Niladri Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan

JMLR 2021 When Does Gradient Descent with Logistic Loss Find Interpolating Two-Layer Networks? Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett

JMLR 2020 Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter L. Bartlett, Martin J. Wainwright

IJCAI 2020 Greedy Convex Ensemble Thanh Tan Nguyen, Nan Ye, Peter L. Bartlett

COLT 2020 On Linear Stochastic Approximation: Fine-Grained Polyak-Ruppert and Non-Asymptotic Concentration Wenlong Mou, Chris Junchi Li, Martin J Wainwright, Peter L Bartlett, Michael I Jordan

NeurIPS 2020 Preference Learning Along Multiple Criteria: A Game-Theoretic Perspective Kush Bhatia, Ashwin Pananjady, Peter L. Bartlett, Anca Dragan, Martin J. Wainwright

NeurIPS 2020 Self-Distillation Amplifies Regularization in Hilbert Space Hossein Mobahi, Mehrdad Farajtabar, Peter L. Bartlett

ALT 2019 A Simple Parameter-Free and Adaptive Approach to Optimization Under a Minimal Local Smoothness Assumption Peter L. Bartlett, Victor Gabillon, Michal Valko

COLT 2019 Fast Mean Estimation with Sub-Gaussian Rates Yeshwanth Cherapanamjeri, Nicolas Flammarion, Peter L. Bartlett

JMLR 2019 Nearly-Tight VC-Dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks Peter L. Bartlett, Nick Harvey, Christopher Liaw, Abbas Mehrabian

COLT 2019 Testing Symmetric Markov Chains Without Hitting Yeshwanth Cherapanamjeri, Peter L. Bartlett

COLT 2018 Best of Both Worlds: Stochastic & Adversarial Best-Arm Identification Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon, Alan Malek, Michal Valko

AISTATS 2018 FLAG N' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods Xiang Cheng, Fred (Farbod) Roosta, Stefan Palombo, Peter L. Bartlett, Michael W. Mahoney

NeurIPS 2018 Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L Bartlett, Michael I Jordan

AISTATS 2018 Gradient Diversity: A Key Ingredient for Scalable Distributed Learning Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett

NeurIPS 2018 Horizon-Independent Minimax Linear Regression Alan Malek, Peter L Bartlett

COLT 2018 Underdamped Langevin MCMC: A Non-Asymptotic Analysis Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, Michael I. Jordan

NeurIPS 2017 Acceleration and Averaging in Stochastic Descent Dynamics Walid Krichene, Peter L Bartlett

NeurIPS 2017 Alternating Minimization for Dictionary Learning with Random Initialization Niladri Chatterji, Peter L Bartlett

AAAI 2017 Fast-Tracking Stationary MOMDPs for Adaptive Management Problems Martin Péron, Kai Helge Becker, Peter L. Bartlett, Iadine Chades

AISTATS 2017 Hit-and-Run for Sampling and Planning in Non-Convex Spaces Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon, Alan Malek

NeurIPS 2017 Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem Yasin Abbasi Yadkori, Peter L Bartlett, Victor Gabillon

ICML 2017 Recovery Guarantees for One-Hidden-Layer Neural Networks Kai Zhong, Zhao Song, Prateek Jain, Peter L. Bartlett, Inderjit S. Dhillon

NeurIPS 2017 Spectrally-Normalized Margin Bounds for Neural Networks Peter L Bartlett, Dylan J Foster, Matus J Telgarsky

AISTATS 2016 A Fast and Reliable Policy Improvement Algorithm Yasin Abbasi-Yadkori, Peter L. Bartlett, Stephen J. Wright

NeurIPS 2016 Adaptive Averaging in Accelerated Descent Dynamics Walid Krichene, Alexandre Bayen, Peter L Bartlett

AISTATS 2016 Improved Learning Complexity in Combinatorial Pure Exploration Bandits Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Ronald Ortner, Peter L. Bartlett

NeurIPS 2015 Accelerated Mirror Descent in Continuous and Discrete Time Walid Krichene, Alexandre Bayen, Peter L Bartlett

COLT 2015 Minimax Fixed-Design Linear Regression Peter L. Bartlett, Wouter M. Koolen, Alan Malek, Eiji Takimoto, Manfred K. Warmuth

NeurIPS 2015 Minimax Time Series Prediction Wouter M. Koolen, Alan Malek, Peter L Bartlett, Yasin Abbasi Yadkori

NeurIPS 2014 Efficient Minimax Strategies for Square Loss Games Wouter M. Koolen, Alan Malek, Peter L Bartlett

NeurIPS 2014 Large-Margin Convex Polytope Machine Alex Kantchelian, Michael C Tschantz, Ling Huang, Peter L Bartlett, Anthony D Joseph, J. D. Tygar

COLT 2013 Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families Peter L. Bartlett, Peter Grünwald, Peter Harremoës, Fares Hedayati, Wojciech Kotlowski

NeurIPS 2013 How to Hedge an Option Against an Adversary: Black-Scholes Pricing Is Minimax Optimal Jacob Abernethy, Peter L Bartlett, Rafael Frongillo, Andre Wibisono

NeurIPS 2013 Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions Yasin Abbasi Yadkori, Peter L Bartlett, Varun Kanade, Yevgeny Seldin, Csaba Szepesvari

COLT 2013 Open Problem: Adversarial Multiarmed Bandits with Limited Advice Yevgeny Seldin, Koby Crammer, Peter L. Bartlett

COLT 2012 The Optimality of Jeffreys Prior for Online Density Estimation and the Asymptotic Normality of Maximum Likelihood Estimators Fares Hedayati, Peter L. Bartlett

COLT 2011 Blackwell Approachability and No-Regret Learning Are Equivalent Jacob Abernethy, Peter L. Bartlett, Elad Hazan

UAI 2011 Learning with Missing Features Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett

COLT 2011 Oracle Inequalities for Computationally Budgeted Model Selection Alekh Agarwal, John C. Duchi, Peter L. Bartlett, Clement Levrard

ALT 2010 A Regularization Approach to Metrical Task Systems Jacob D. Abernethy, Peter L. Bartlett, Niv Buchbinder, Isabelle Stanton

ECML-PKDD 2010 A Unifying View of Multiple Kernel Learning Marius Kloft, Ulrich Rückert, Peter L. Bartlett

ICML 2010 Implicit Online Learning Brian Kulis, Peter L. Bartlett

ALT 2010 Optimal Online Prediction in Adversarial Environments Peter L. Bartlett

COLT 2009 A Stochastic View of Optimal Regret Through Minimax Duality Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin

NeurIPS 2009 Information-Theoretic Lower Bounds on the Oracle Complexity of Convex Optimization Alekh Agarwal, Martin J. Wainwright, Peter L. Bartlett, Pradeep K. Ravikumar

UAI 2009 REGAL: A Regularization Based Algorithm for Reinforcement Learning in Weakly Communicating MDPs Peter L. Bartlett, Ambuj Tewari

JMLR 2008 Classification with a Reject Option Using a Hinge Loss Peter L. Bartlett, Marten H. Wegkamp

JMLR 2008 Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks Michael Collins, Amir Globerson, Terry Koo, Xavier Carreras, Peter L. Bartlett

COLT 2008 High-Probability Regret Bounds for Bandit Online Linear Optimization Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, Sham M. Kakade, Alexander Rakhlin, Ambuj Tewari

COLT 2008 Optimal Stragies and Minimax Lower Bounds for Online Convex Games Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin, Ambuj Tewari

JMLR 2007 AdaBoost Is Consistent Peter L. Bartlett, Mikhail Traskin

NeurIPS 2007 Adaptive Online Gradient Descent Peter L. Bartlett, Elad Hazan, Alexander Rakhlin

COLT 2007 Bounded Parameter Markov Decision Processes with Average Reward Criterion Ambuj Tewari, Peter L. Bartlett

COLT 2007 Multitask Learning with Expert Advice Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin

JMLR 2007 On the Consistency of Multiclass Classification Methods Ambuj Tewari, Peter L. Bartlett

ICML 2007 Online Discovery of Similarity Mappings Alexander Rakhlin, Jacob D. Abernethy, Peter L. Bartlett

NeurIPS 2007 Optimistic Linear Programming Gives Logarithmic Regret for Irreducible MDPs Ambuj Tewari, Peter L. Bartlett

JMLR 2007 Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results Peter L. Bartlett, Ambuj Tewari

AISTATS 2007 The Rademacher Complexity of Co-Regularized Kernel Classes David S. Rosenberg, Peter L. Bartlett

NeurIPS 2006 AdaBoost Is Consistent Peter L. Bartlett, Mikhail Traskin

NeurIPS 2006 Sample Complexity of Policy Search with Known Dynamics Peter L. Bartlett, Ambuj Tewari

NeurIPS 2006 Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds Benjamin I. Rubinstein, Peter L. Bartlett, J. H. Rubinstein

COLT 2005 On the Consistency of Multiclass Classification Methods Ambuj Tewari, Peter L. Bartlett

NeurIPS 2004 Exponentiated Gradient Algorithms for Large-Margin Structured Classification Peter L. Bartlett, Michael Collins, Ben Taskar, David A. McAllester

COLT 2004 Local Complexities for Empirical Risk Minimization Peter L. Bartlett, Shahar Mendelson, Petra Philips

COLT 2004 Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results Peter L. Bartlett, Ambuj Tewari

JMLR 2004 Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning Evan Greensmith, Peter L. Bartlett, Jonathan Baxter

NeurIPS 2003 Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates Peter L. Bartlett, Michael I. Jordan, Jon D. Mcauliffe

ICML 2002 Learning the Kernel Matrix with Semi-Definite Programming Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan

COLT 2002 Localized Rademacher Complexities Peter L. Bartlett, Olivier Bousquet, Shahar Mendelson

MLJ 2002 Model Selection and Error Estimation Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi

JMLR 2002 Rademacher and Gaussian Complexities: Risk Bounds and Structural Results Peter L. Bartlett, Shahar Mendelson

JAIR 2001 Experiments with Infinite-Horizon, Policy-Gradient Estimation Jonathan Baxter, Peter L. Bartlett, Lex Weaver

JAIR 2001 Infinite-Horizon Policy-Gradient Estimation Jonathan Baxter, Peter L. Bartlett

COLT 2001 Rademacher and Gaussian Complexities: Risk Bounds and Structural Results Peter L. Bartlett, Shahar Mendelson

NeurIPS 2001 Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning Evan Greensmith, Peter L. Bartlett, Jonathan Baxter

COLT 2000 Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning Peter L. Bartlett, Jonathan Baxter

MLJ 2000 Improved Generalization Through Explicit Optimization of Margins Llew Mason, Peter L. Bartlett, Jonathan Baxter

MLJ 2000 Learning Changing Concepts by Exploiting the Structure of Change Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni

COLT 2000 Model Selection and Error Estimation Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi

NeCo 2000 New Support Vector Algorithms Bernhard Schölkopf, Alexander J. Smola, Robert C. Williamson, Peter L. Bartlett

ICML 2000 Reinforcement Learning in POMDP's via Direct Gradient Ascent Jonathan Baxter, Peter L. Bartlett

NeurIPS 2000 Sparse Greedy Gaussian Process Regression Alex J. Smola, Peter L. Bartlett

NeurIPS 1999 Boosting Algorithms as Gradient Descent Llew Mason, Jonathan Baxter, Peter L. Bartlett, Marcus R. Frean

COLT 1999 Covering Numbers for Support Vector Machines Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson

NeurIPS 1998 Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks Peter L. Bartlett, Vitaly Maiorov, Ron Meir

NeCo 1998 Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks Peter L. Bartlett, Vitaly Maiorov, Ron Meir

NeurIPS 1998 Direct Optimization of Margins Improves Generalization in Combined Classifiers Llew Mason, Peter L. Bartlett, Jonathan Baxter

COLT 1998 Proceedings of the Eleventh Annual Conference on Computational Learning Theory, COLT 1998, Madison, Wisconsin, USA, July 24-26, 1998 Peter L. Bartlett, Yishay Mansour

NeurIPS 1998 Shrinking the Tube: A New Support Vector Regression Algorithm Bernhard Schölkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson

NeCo 1997 Correction to 'Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes' Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson

NeurIPS 1997 Generalization in Decision Trees and DNF: Does Size Matter? Mostefa Golea, Peter L. Bartlett, Wee Sun Lee, Llew Mason

NeurIPS 1997 The Canonical Distortion Measure in Feature Space and 1-NN Classification Jonathan Baxter, Peter L. Bartlett

COLT 1996 A Framework for Structural Risk Minimisation John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony

NeurIPS 1996 For Valid Generalization the Size of the Weights Is More Important than the Size of the Network Peter L. Bartlett

COLT 1996 Learning Changing Concepts by Exploiting the Structure of Change Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni

COLT 1996 The Importance of Convexity in Learning with Squared Loss Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson

NeCo 1996 The VC Dimension and Pseudodimension of Two-Layer Neural Networks with Discrete Inputs Peter L. Bartlett, Robert C. Williamson

NeurIPS 1995 Examples of Learning Curves from a Modified VC-Formalism Adam Kowalczyk, Jacek Szymanski, Peter L. Bartlett, Robert C. Williamson

NeCo 1995 Lower Bounds on the VC Dimension of Smoothly Parameterized Function Classes Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson

COLT 1995 More Theorems About Scale-Sensitive Dimensions and Learning Peter L. Bartlett, Philip M. Long

COLT 1995 On Efficient Agnostic Learning of Linear Combinations of Basis Functions Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson

COLT 1994 Exploiting Random Walks for Learning Peter L. Bartlett, Paul Fischer, Klaus-Uwe Höffgen

COLT 1994 Fat-Shattering and the Learnability of Real-Valued Functions Peter L. Bartlett, Philip M. Long, Robert C. Williamson

COLT 1994 Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson

COLT 1993 Lower Bounds on the Vapnik-Chervonenkis Dimension of Multi-Layer Threshold Networks Peter L. Bartlett

NeCo 1993 Vapnik-Chervonenkis Dimension Bounds for Two- and Three-Layer Networks Peter L. Bartlett

COLT 1992 Learning with a Slowly Changing Distribution Peter L. Bartlett

COLT 1991 Investigating the Distribution Assumptions in the Pac Learning Model Peter L. Bartlett, Robert C. Williamson

NeurIPS 1991 Splines, Rational Functions and Neural Networks Robert C. Williamson, Peter L. Bartlett