Bartlett, Peter

52 publications

ICML 2025 Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression Jingfeng Wu, Peter Bartlett, Matus Telgarsky, Bin Yu

JMLR 2025 Contextual Bandits with Stage-Wise Constraints Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett

ICML 2025 Gradient Descent Converges Arbitrarily Fast for Logistic Regression via Large and Adaptive Stepsizes Ruiqi Zhang, Jingfeng Wu, Peter Bartlett

ICML 2025 Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks Yuhang Cai, Kangjie Zhou, Jingfeng Wu, Song Mei, Michael Lindsey, Peter Bartlett

AISTATS 2025 Implicit Diffusion: Efficient Optimization Through Stochastic Sampling Pierre Marion, Anna Korba, Peter Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, Quentin Berthet

NeurIPS 2025 Improved Scaling Laws in Linear Regression via Data Reuse Licong Lin, Jingfeng Wu, Peter Bartlett

NeurIPS 2025 Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression Jingfeng Wu, Pierre Marion, Peter Bartlett

AISTATS 2025 Statistical Guarantees for Unpaired Image-to-Image Cross-Domain Analysis Using GANs Saptarshi Chakraborty, Peter Bartlett

ICLR 2024 A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-Dimensional Data Saptarshi Chakraborty, Peter Bartlett

ICMLW 2024 Accelerating Best-of-N via Speculative Rejection Ruiqi Zhang, Momin Haider, Ming Yin, Jiahao Qiu, Mengdi Wang, Peter Bartlett, Andrea Zanette

ICMLW 2024 Accelerating Best-of-N via Speculative Rejection Ruiqi Zhang, Momin Haider, Ming Yin, Jiahao Qiu, Mengdi Wang, Peter Bartlett, Andrea Zanette

ICMLW 2024 Accelerating Best-of-N via Speculative Rejection Ruiqi Zhang, Momin Haider, Ming Yin, Jiahao Qiu, Mengdi Wang, Peter Bartlett, Andrea Zanette

L4DC 2024 Can a Transformer Represent a Kalman Filter? Gautam Goel, Peter Bartlett

ICLR 2024 How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter Bartlett

ICMLW 2024 Implicit Diffusion: Efficient Optimization Through Stochastic Sampling Pierre Marion, Anna Korba, Peter Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, Quentin Berthet

JMLR 2023 A Complete Characterization of Linear Estimators for Offline Policy Evaluation Juan C. Perdomo, Akshay Krishnamurthy, Peter Bartlett, Sham Kakade

ALT 2023 An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit Aldo Pacchiano, Peter Bartlett, Michael Jordan

COLT 2023 Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization Spencer Frei, Gal Vardi, Peter Bartlett, Nathan Srebro

ICLR 2023 Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data Spencer Frei, Gal Vardi, Peter Bartlett, Nathan Srebro, Wei Hu

NeurIPSW 2023 Trained Transformers Learn Linear Models In-Context Ruiqi Zhang, Spencer Frei, Peter Bartlett

COLT 2022 Benign Overfitting Without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data Spencer Frei, Niladri S Chatterji, Peter Bartlett

COLT 2022 Generalization Bounds for Data-Driven Numerical Linear Algebra Peter Bartlett, Piotr Indyk, Tal Wagner

COLT 2022 Optimal Mean Estimation Without a Variance Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter Bartlett, Michael Jordan

COLT 2022 Optimal and Instance-Dependent Guarantees for Markovian Linear Stochastic Approximation Wenlong Mou, Ashwin Pananjady, Martin Wainwright, Peter Bartlett

AISTATS 2021 Stochastic Bandits with Linear Constraints Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett, Heinrich Jiang

ICML 2021 Dropout: Explicit Forms and Capacity Control Raman Arora, Peter Bartlett, Poorya Mianjy, Nathan Srebro

COLT 2021 Towards a Dimension-Free Understanding of Adaptive Linear Control Juan C Perdomo, Max Simchowitz, Alekh Agarwal, Peter Bartlett

COLT 2021 When Does Gradient Descent with Logistic Loss Interpolate Using Deep Networks with Smoothed ReLU Activations? Niladri S. Chatterji, Philip M. Long, Peter Bartlett

ICML 2020 Accelerated Message Passing for Entropy-Regularized MAP Inference Jonathan Lee, Aldo Pacchiano, Peter Bartlett, Michael Jordan

ICLR 2020 Dropout: Explicit Forms and Capacity Control Raman Arora, Peter Bartlett, Poorya Mianjy, Nathan Srebro

AISTATS 2020 Langevin Monte Carlo Without Smoothness Niladri Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter Bartlett

AISTATS 2020 OSOM: A Simultaneously Optimal Algorithm for Multi-Armed and Linear Contextual Bandits Niladri Chatterji, Vidya Muthukumar, Peter Bartlett

ICML 2020 On Approximate Thompson Sampling with Langevin Algorithms Eric Mazumdar, Aldo Pacchiano, Yian Ma, Michael Jordan, Peter Bartlett

ICML 2020 Stochastic Gradient and Langevin Processes Xiang Cheng, Dong Yin, Peter Bartlett, Michael Jordan

AISTATS 2019 Best of Many Worlds: Robust Model Selection for Online Supervised Learning Vidya Muthukumar, Mitas Ray, Anant Sahai, Peter Bartlett

ICML 2019 Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning Dong Yin, Yudong Chen, Ramchandran Kannan, Peter Bartlett

AISTATS 2019 Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter Bartlett, Martin Wainwright

ICML 2019 Online Learning with Kernel Losses Niladri Chatterji, Aldo Pacchiano, Peter Bartlett

ICML 2019 POLITEX: Regret Bounds for Policy Iteration Using Expert Prediction Yasin Abbasi-Yadkori, Peter Bartlett, Kush Bhatia, Nevena Lazic, Csaba Szepesvari, Gellert Weisz

ICML 2019 Rademacher Complexity for Adversarially Robust Generalization Dong Yin, Ramchandran Kannan, Peter Bartlett

ICML 2019 Scale-Free Adaptive Planning for Deterministic Dynamics & Discounted Rewards Peter Bartlett, Victor Gabillon, Jennifer Healey, Michal Valko

ICML 2018 Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates Dong Yin, Yudong Chen, Ramchandran Kannan, Peter Bartlett

ALT 2018 Convergence of Langevin MCMC in KL-Divergence Xiang Cheng, Peter Bartlett

ICML 2018 Gradient Descent with Identity Initialization Efficiently Learns Positive Definite Linear Transformations by Deep Residual Networks Peter Bartlett, Dave Helmbold, Philip Long

ICML 2018 On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo Niladri Chatterji, Nicolas Flammarion, Yian Ma, Peter Bartlett, Michael Jordan

ICML 2015 Large-Scale Markov Decision Problems with KL Control Cost and Its Application to Crowdsourcing Yasin Abbasi-Yadkori, Peter Bartlett, Xi Chen, Alan Malek

ICML 2014 Linear Programming for Large-Scale Markov Decision Problems Alan Malek, Yasin Abbasi-Yadkori, Peter Bartlett

ICML 2014 Prediction with Limited Advice and Multiarmed Bandits with Paid Observations Yevgeny Seldin, Peter Bartlett, Koby Crammer, Yasin Abbasi-Yadkori

ICML 2014 Tracking Adversarial Targets Yasin Abbasi-Yadkori, Peter Bartlett, Varun Kanade

AISTATS 2012 Exchangeability Characterizes Optimality of Sequential Normalized Maximum Likelihood and Bayesian Prediction with Jeffreys Prior Fares Hedayati, Peter Bartlett

AISTATS 2010 Optimal Allocation Strategies for the Dark Pool Problem Alekh Agarwal, Peter Bartlett, Max Dama

JMLR 2004 Learning the Kernel Matrix with Semidefinite Programming Gert R.G. Lanckriet, Nello Cristianini, Peter Bartlett, Laurent El Ghaoui, Michael I. Jordan