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Peter L. Bartlett

60 papers · 2002–2025 · 6 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer πŸ—ΊοΈ Taxonomy Completionist (30) 🐝 Cross-Pollinator (9) 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (23) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (6) 🏠 Conference Loyalist (29) 🌟 Keyword Trendsetter Combo (6) 🌱 Topic Pioneer πŸ”¬ Deep Specialist (11) πŸ† Keyword Champion 🀝 Dynamic Duo (13) πŸ—ƒοΈ Keyword Collector (116) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (15) ⚑ Prolific Year (5) ❓ The Questioner πŸ’Ž Century Club (60)

Conferences

NIPS (29) JMLR (20) COLT (8) AISTATS (1) ALT (1) ICML (1)

Top co-authors

Peter L Bartlett (13) Philip M. Long (5) Martin J. Wainwright (5) Jingfeng Wu (4) Alan Malek (4) Ambuj Tewari (4) Niladri S. Chatterji (4) Michael I. Jordan (4) Spencer Frei (3) Kush Bhatia (3)

Keywords

gradient descent (9) online learning (8) regret bound (7) convex optimization (6) binary classification (5) minimax optimization (5) learning theory (4) minimax strategy (4) support vector machine (4) stochastic optimization (4) markov decision process (4) regret minimization (4) linear regression (4) neural network (4) neural network optimization (3) adversarial learning (3) mirror descent (3) wasserstein distance (3) reinforcement learning (3) sample complexity (3)

Papers

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