| 2026 | COLT | Risk Comparisons in Linear Regression: Implicit Regularization Dominates Explicit Regularization (Extended Abstract). | Jingfeng Wu, Peter L. Bartlett, Sham M. Kakade, Jason D. Lee, Bin Yu |
| 2025 | AISTATS | Statistical Guarantees for Unpaired Image-to-Image Cross-Domain Analysis using GANs. | Saptarshi Chakraborty, Peter L. Bartlett |
| 2025 | AISTATS | Implicit Diffusion: Efficient optimization through stochastic sampling. | Pierre Marion, Anna Korba, Peter L. Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-Lpez, Courtney Paquette, Quentin Berthet |
| 2025 | ICML | Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks. | Yuhang Cai, Kangjie Zhou, Jingfeng Wu, Song Mei, Michael Lindsey, Peter L. Bartlett |
| 2025 | ICML | Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression. | Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu |
| 2025 | ICML | Gradient Descent Converges Arbitrarily Fast for Logistic Regression via Large and Adaptive Stepsizes. | Ruiqi Zhang, Jingfeng Wu, Peter L. Bartlett |
| 2024 | COLT | Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency. | Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu |
| 2024 | ICLR | A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data. | Saptarshi Chakraborty, Peter L. Bartlett |
| 2024 | ICLR | How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? | Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett |
| 2023 | ALT | An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. | Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan |
| 2023 | COLT | Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization. | Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro |
| 2023 | ICLR | Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. | Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu |
| 2022 | COLT | Generalization Bounds for Data-Driven Numerical Linear Algebra. | Peter L. Bartlett, Piotr Indyk, Tal Wagner |
| 2022 | COLT | Optimal Mean Estimation without a Variance. | Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan |
| 2022 | COLT | Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data. | Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett |
| 2022 | COLT | Optimal and instance-dependent guarantees for Markovian linear stochastic approximation. | Wenlong Mou, Ashwin Pananjady, Martin J. Wainwright, Peter L. Bartlett |
| 2021 | AISTATS | Stochastic Bandits with Linear Constraints. | Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang |
| 2021 | COLT | When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations? | Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett |
| 2021 | COLT | Towards a Dimension-Free Understanding of Adaptive Linear Control. | Juan C. Perdomo, Max Simchowitz, Alekh Agarwal, Peter L. Bartlett |
| 2021 | ICML | Dropout: Explicit Forms and Capacity Control. | Raman Arora, Peter L. Bartlett, Poorya Mianjy, Nathan Srebro |
| 2020 | AISTATS | Langevin Monte Carlo without smoothness. | Niladri S. Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter L. Bartlett |
| 2020 | AISTATS | OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits. | Niladri S. Chatterji, Vidya Muthukumar, Peter L. Bartlett |
| 2020 | COLT | 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 |
| 2020 | ICML | Stochastic Gradient and Langevin Processes. | Xiang Cheng, Dong Yin, Peter L. Bartlett, Michael I. Jordan |
| 2020 | ICML | Accelerated Message Passing for Entropy-Regularized MAP Inference. | Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan |
| 2020 | ICML | On Approximate Thompson Sampling with Langevin Algorithms. | Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett |
| 2020 | IJCAI | Greedy Convex Ensemble. | Thanh Tan Nguyen, Nan Ye, Peter L. Bartlett |
| 2019 | AISTATS | 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 |
| 2019 | AISTATS | Best of many worlds: Robust model selection for online supervised learning. | Vidya Muthukumar, Mitas Ray, Anant Sahai, Peter L. Bartlett |
| 2019 | ALT | A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption. | Peter L. Bartlett, Victor Gabillon, Michal Valko |
| 2019 | COLT | Testing Symmetric Markov Chains Without Hitting. | Yeshwanth Cherapanamjeri, Peter L. Bartlett |
| 2019 | COLT | Fast Mean Estimation with Sub-Gaussian Rates. | Yeshwanth Cherapanamjeri, Nicolas Flammarion, Peter L. Bartlett |
| 2019 | ICML | Scale-free adaptive planning for deterministic dynamics & discounted rewards. | Peter L. Bartlett, Victor Gabillon, Jennifer Healey, Michal Valko |
| 2019 | ICML | Online learning with kernel losses. | Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett |
| 2019 | ICML | Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning. | Dong Yin, Yudong Chen, Kannan Ramchandran, Peter L. Bartlett |
| 2019 | ICML | Rademacher Complexity for Adversarially Robust Generalization. | Dong Yin, Kannan Ramchandran, Peter L. Bartlett |
| 2018 | AISTATS | FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods. | Xiang Cheng, Fred (Farbod) Roosta, Stefan Palombo, Peter L. Bartlett, Michael W. Mahoney |
| 2018 | AISTATS | Gradient Diversity: a Key Ingredient for Scalable Distributed Learning. | Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett |
| 2018 | ALT | Convergence of Langevin MCMC in KL-divergence. | Xiang Cheng, Peter L. Bartlett |
| 2018 | COLT | Best of both worlds: Stochastic & adversarial best-arm identification. | Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon, Alan Malek, Michal Valko |
| 2018 | COLT | Underdamped Langevin MCMC: A non-asymptotic analysis. | Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, Michael I. Jordan |
| 2018 | ICML | Gradient descent with identity initialization efficiently learns positive definite linear transformations. | Peter L. Bartlett, David P. Helmbold, Philip M. Long |
| 2018 | ICML | On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. | Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan |
| 2018 | ICML | Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates. | Dong Yin, Yudong Chen, Kannan Ramchandran, Peter L. Bartlett |
| 2017 | AAAI | Fast-Tracking Stationary MOMDPs for Adaptive Management Problems. | Martin Pron, Kai Helge Becker, Peter L. Bartlett, Iadine Chades |
| 2017 | AISTATS | Hit-and-Run for Sampling and Planning in Non-Convex Spaces. | Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon, Alan Malek |
| 2017 | ICML | Recovery Guarantees for One-hidden-layer Neural Networks. | Kai Zhong, Zhao Song, Prateek Jain, Peter L. Bartlett, Inderjit S. Dhillon |
| 2016 | AISTATS | A Fast and Reliable Policy Improvement Algorithm. | Yasin Abbasi-Yadkori, Peter L. Bartlett, Stephen J. Wright |
| 2016 | AISTATS | Improved Learning Complexity in Combinatorial Pure Exploration Bandits. | Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Ronald Ortner, Peter L. Bartlett |
| 2015 | COLT | Minimax Fixed-Design Linear Regression. | Peter L. Bartlett, Wouter M. Koolen, Alan Malek, Eiji Takimoto, Manfred K. Warmuth |
| 2015 | ICML | Large-Scale Markov Decision Problems with KL Control Cost and its Application to Crowdsourcing. | Yasin Abbasi-Yadkori, Peter L. Bartlett, Xi Chen, Alan Malek |
| 2014 | ICML | Tracking Adversarial Targets. | Yasin Abbasi-Yadkori, Peter L. Bartlett, Varun Kanade |
| 2014 | ICML | Linear Programming for Large-Scale Markov Decision Problems. | Alan Malek, Yasin Abbasi-Yadkori, Peter L. Bartlett |
| 2014 | ICML | Prediction with Limited Advice and Multiarmed Bandits with Paid Observations. | Yevgeny Seldin, Peter L. Bartlett, Koby Crammer, Yasin Abbasi-Yadkori |
| 2013 | COLT | Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families. | Peter L. Bartlett, Peter Grnwald, Peter Harremos, Fares Hedayati, Wojciech Kotlowski |
| 2013 | COLT | Open Problem: Adversarial Multiarmed Bandits with Limited Advice. | Yevgeny Seldin, Koby Crammer, Peter L. Bartlett |
| 2012 | COLT | The Optimality of Jeffreys Prior for Online Density Estimation and the Asymptotic Normality of Maximum Likelihood Estimators. | Fares Hedayati, Peter L. Bartlett |
| 2011 | UAI | Learning with Missing Features. | Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett |
| 2010 | ALT | A Regularization Approach to Metrical Task Systems. | Jacob D. Abernethy, Peter L. Bartlett, Niv Buchbinder, Isabelle Stanton |
| 2010 | ALT | Optimal Online Prediction in Adversarial Environments. | Peter L. Bartlett |
| 2010 | DIS | Optimal Online Prediction in Adversarial Environments. | Peter L. Bartlett |
| 2010 | FC | A Learning-Based Approach to Reactive Security. | Adam Barth, Benjamin I. P. Rubinstein, Mukund Sundararajan, John C. Mitchell, Dawn Song, Peter L. Bartlett |
| 2010 | ICML | Implicit Online Learning. | Brian Kulis, Peter L. Bartlett |
| 2009 | COLT | A Stochastic View of Optimal Regret through Minimax Duality. | Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin |
| 2009 | UAI | REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs. | Peter L. Bartlett, Ambuj Tewari |
| 2008 | CCS | Open problems in the security of learning. | Marco Barreno, Peter L. Bartlett, Fuching Jack Chi, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, Udam Saini, J. Doug Tygar |
| 2008 | COLT | Optimal Stragies and Minimax Lower Bounds for Online Convex Games. | Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin, Ambuj Tewari |
| 2008 | COLT | High-Probability Regret Bounds for Bandit Online Linear Optimization. | Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, Sham M. Kakade, Alexander Rakhlin, Ambuj Tewari |
| 2007 | COLT | Multitask Learning with Expert Advice. | Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin |
| 2007 | COLT | Bounded Parameter Markov Decision Processes with Average Reward Criterion. | Ambuj Tewari, Peter L. Bartlett |
| 2007 | ICML | Online discovery of similarity mappings. | Alexander Rakhlin, Jacob D. Abernethy, Peter L. Bartlett |
| 2005 | COLT | On the Consistency of Multiclass Classification Methods. | Ambuj Tewari, Peter L. Bartlett |
| 2004 | COLT | Local Complexities for Empirical Risk Minimization. | Peter L. Bartlett, Shahar Mendelson, Petra Philips |
| 2004 | COLT | Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results. | Peter L. Bartlett, Ambuj Tewari |
| 2002 | COLT | Localized Rademacher Complexities. | Peter L. Bartlett, Olivier Bousquet, Shahar Mendelson |
| 2002 | ICML | Learning the Kernel Matrix with Semi-Definite Programming. | Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan |
| 2001 | COLT | Rademacher and Gaussian Complexities: Risk Bounds and Structural Results. | Peter L. Bartlett, Shahar Mendelson |
| 2000 | COLT | Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. | Peter L. Bartlett, Jonathan Baxter |
| 2000 | COLT | Model Selection and Error Estimation. | Peter L. Bartlett, Stphane Boucheron, Gbor Lugosi |
| 2000 | ICML | Reinforcement Learning in POMDP's via Direct Gradient Ascent. | Jonathan Baxter, Peter L. Bartlett |
| 2000 | ISCAS | Direct gradient-based reinforcement learning. | Jonathan Baxter, Peter L. Bartlett |
| 1999 | COLT | Covering Numbers for Support Vector Machines. | Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson |
| 1996 | COLT | Learning Changing Concepts by Exploiting the Structure of Change. | Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni |
| 1996 | COLT | The Importance of Convexity in Learning with Squared Loss. | Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson |
| 1996 | COLT | A Framework for Structural Risk Minimisation. | John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony |
| 1995 | COLT | More Theorems about Scale-sensitive Dimensions and Learning. | Peter L. Bartlett, Philip M. Long |
| 1995 | COLT | On Efficient Agnostic Learning of Linear Combinations of Basis Functions. | Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson |
| 1994 | COLT | Exploiting Random Walks for Learning. | Peter L. Bartlett, Paul Fischer, Klaus-Uwe Hffgen |
| 1994 | COLT | Fat-Shattering and the Learnability of Real-Valued Functions. | Peter L. Bartlett, Philip M. Long, Robert C. Williamson |
| 1994 | COLT | Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes. | Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson |
| 1993 | COLT | Lower Bounds on the Vapnik-Chervonenkis Dimension of Multi-Layer Threshold Networks. | Peter L. Bartlett |
| 1992 | COLT | Learning With a Slowly Changing Distribution. | Peter L. Bartlett |
| 1991 | COLT | Investigating the Distribution Assumptions in the Pac Learning Model. | Peter L. Bartlett, Robert C. Williamson |