← Authors

Peter Bartlett

52 papers · 2004–2025 · 8 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (21) πŸ—ΊοΈ Taxonomy Completionist (14) 🐝 Cross-Pollinator (6) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (8) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸ”¬ Deep Specialist (12) πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (191) ⚑ Prolific Year (7) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ’Ž Century Club (52) πŸ”₯ Unstoppable (14) ❓ The Questioner (3)

Conferences

ICML (19) AISTATS (13) COLT (10) ICLR (3) JMLR (3) ALT (2) IJCAI (1) L4DC (1)

Top co-authors

Yasin Abbasi-Yadkori (7) Aldo Pacchiano (6) Michael Jordan (6) Dong Yin (5) Niladri Chatterji (4) Alan Malek (4) Jingfeng Wu (4) Victor Gabillon (4) Spencer Frei (3) Xiang Cheng (3)

Research topics

Statistics (1)

Keywords

regret bound (9) multi-armed bandit (9) gradient descent (4) generalization bound (3) convergence rate (3) stochastic optimization (3) non-convex optimization (3) markov chain monte carlo (3) online learning (3) reinforcement learning (2) model selection (2) logarithmic loss (2) convex optimization (2) upper confidence bound (2) markov decision process (2) rademacher complexity (2) policy optimization (2) kl divergence (2) mixing time (2) benign overfitting (2)

Papers

Statistical Guarantees for Unpaired Image-to-Image Cross-Domain Analysis using GANs AISTATS 2025
Contextual Bandits with Stage-wise Constraints JMLR 2025
Gradient Descent Converges Arbitrarily Fast for Logistic Regression via Large and Adaptive Stepsizes ICML 2025
Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression ICML 2025
Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks ICML 2025
Implicit Diffusion: Efficient optimization through stochastic sampling AISTATS 2025
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? ICLR 2024
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data ICLR 2024
Can a transformer represent a Kalman filter? L4DC 2024
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit ALT 2023
A Complete Characterization of Linear Estimators for Offline Policy Evaluation JMLR 2023
Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data ICLR 2023
Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization COLT 2023
Optimal Mean Estimation without a Variance COLT 2022
Generalization Bounds for Data-Driven Numerical Linear Algebra COLT 2022
Optimal and instance-dependent guarantees for Markovian linear stochastic approximation COLT 2022
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data COLT 2022
Dropout: Explicit Forms and Capacity Control ICML 2021
Towards a Dimension-Free Understanding of Adaptive Linear Control COLT 2021
When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations? COLT 2021
Stochastic Bandits with Linear Constraints AISTATS 2021
OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits AISTATS 2020
On Approximate Thompson Sampling with Langevin Algorithms ICML 2020
Greedy Convex Ensemble IJCAI 2020
Langevin Monte Carlo without smoothness AISTATS 2020
Accelerated Message Passing for Entropy-Regularized MAP Inference ICML 2020
Stochastic Gradient and Langevin Processes ICML 2020
Best of many worlds: Robust model selection for online supervised learning AISTATS 2019
Online learning with kernel losses ICML 2019
Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems AISTATS 2019
Scale-free adaptive planning for deterministic dynamics & discounted rewards ICML 2019
POLITEX: Regret Bounds for Policy Iteration using Expert Prediction ICML 2019
Rademacher Complexity for Adversarially Robust Generalization ICML 2019
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning ICML 2019
Best of both worlds: Stochastic & adversarial best-arm identification COLT 2018
Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks ICML 2018
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo ICML 2018
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates ICML 2018
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning AISTATS 2018
FLAG n’ FLARE: Fast Linearly-Coupled Adaptive Gradient Methods AISTATS 2018
Convergence of Langevin MCMC in KL-divergence ALT 2018
Hit-and-Run for Sampling and Planning in Non-Convex Spaces AISTATS 2017
Improved Learning Complexity in Combinatorial Pure Exploration Bandits AISTATS 2016
Large-Scale Markov Decision Problems with KL Control Cost and its Application to Crowdsourcing ICML 2015
Prediction with Limited Advice and Multiarmed Bandits with Paid Observations ICML 2014
Linear Programming for Large-Scale Markov Decision Problems ICML 2014
Tracking Adversarial Targets ICML 2014
Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families COLT 2013
Open Problem: Adversarial Multiarmed Bandits with Limited Advice COLT 2013
Exchangeability Characterizes Optimality of Sequential Normalized Maximum Likelihood and Bayesian Prediction with Jeffreys Prior AISTATS 2012
Optimal Allocation Strategies for the Dark Pool Problem AISTATS 2010
Learning the Kernel Matrix with Semidefinite Programming JMLR 2004