Peter L. Bartlett

On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension JMLR 2025 Scaling Laws in Linear Regression: Compute, Parameters, and Data NIPS 2024 Fast Best-of-N Decoding via Speculative Rejection NIPS 2024 In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization NIPS 2024 Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization NIPS 2024 Sharpness-Aware Minimization and the Edge of Stability JMLR 2024 Trained Transformers Learn Linear Models In-Context JMLR 2024 Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency COLT 2024 The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima JMLR 2023 The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks NIPS 2023 Random Feature Amplification: Feature Learning and Generalization in Neural Networks JMLR 2023 Benign overfitting in ridge regression JMLR 2023 An Efficient Sampling Algorithm for Non-smooth Composite Potentials JMLR 2022 The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks JMLR 2022 On the Theory of Reinforcement Learning with Once-per-Episode Feedback NIPS 2021 Failures of Model-dependent Generalization Bounds for Least-norm Interpolation JMLR 2021 When Does Gradient Descent with Logistic Loss Find Interpolating Two-Layer Networks? JMLR 2021 High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm JMLR 2021 Near Optimal Policy Optimization via REPS NIPS 2021 Adversarial Examples in Multi-Layer Random ReLU Networks NIPS 2021 Preference learning along multiple criteria: A game-theoretic perspective NIPS 2020 Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems JMLR 2020 Self-Distillation Amplifies Regularization in Hilbert Space NIPS 2020 Fast Mean Estimation with Sub-Gaussian Rates COLT 2019 A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption ALT 2019 Testing Symmetric Markov Chains Without Hitting COLT 2019 Nearly-tight VC-dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks JMLR 2019 Horizon-Independent Minimax Linear Regression NIPS 2018 Underdamped Langevin MCMC: A non-asymptotic analysis COLT 2018 Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation NIPS 2018 Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem NIPS 2017 Acceleration and Averaging in Stochastic Descent Dynamics NIPS 2017 Alternating minimization for dictionary learning with random initialization NIPS 2017 Recovery Guarantees for One-hidden-layer Neural Networks ICML 2017 Spectrally-normalized margin bounds for neural networks NIPS 2017 Adaptive Averaging in Accelerated Descent Dynamics NIPS 2016 A Fast and Reliable Policy Improvement Algorithm AISTATS 2016 Accelerated Mirror Descent in Continuous and Discrete Time NIPS 2015 Minimax Fixed-Design Linear Regression COLT 2015 Minimax Time Series Prediction NIPS 2015 Efficient Minimax Strategies for Square Loss Games NIPS 2014 Large-Margin Convex Polytope Machine NIPS 2014 How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal NIPS 2013 Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions NIPS 2013 The Optimality of Jeffreys Prior for Online Density Estimation and the Asymptotic Normality of Maximum Likelihood Estimators COLT 2012 Blackwell Approachability and No-Regret Learning are Equivalent COLT 2011 Oracle inequalities for computationally budgeted model selection COLT 2011 Information-theoretic lower bounds on the oracle complexity of convex optimization NIPS 2009 Classification with a Reject Option using a Hinge Loss JMLR 2008 Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks JMLR 2008 Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results JMLR 2007 Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs NIPS 2007 Adaptive Online Gradient Descent NIPS 2007 AdaBoost is Consistent JMLR 2007 On the Consistency of Multiclass Classification Methods JMLR 2007 AdaBoost is Consistent NIPS 2006 Sample Complexity of Policy Search with Known Dynamics NIPS 2006 Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds NIPS 2006 Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning JMLR 2004 Rademacher and Gaussian Complexities: Risk Bounds and Structural Results JMLR 2002