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Bartlett, Peter L.

  1. RL2: Fast Reinforcement Learning via Slow Reinforcement Learning
    2016/11/09 by Yan Duan, John Schulman, Duan, Yan +9 · 1 voice · 89 citations
    Computer Science · #Reinforcement Learning in Robotics #Optimization and Search Problems #Advanced Neural Network Applications
  2. Trained Transformers Learn Linear Models In-Context
    2023/06/16 by Ruiqi Zhang, Spencer Frei, Zhang, Ruiqi +3 · 1 voice · 53 citations
    Computer Science · Mathematics · #Advanced Neural Network Applications #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Stochastic Gradient Optimization Techniques #cs.AI #cs.CL #cs.LG #stat.ML
  3. REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs
    2012/05/09 by Peter L. Bartlett, Bartlett, Peter L., Ambuj Tewari +1 · 30 citations
    Decision Sciences · Computer Science · #Advanced Bandit Algorithms Research #Reinforcement Learning in Robotics #Age of Information Optimization
  4. Deep learning: a statistical viewpoint
    2021/03/16 by Bartlett, Peter L., Montanari, Andrea, Rakhlin, Alexander · 21 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistics Theory (math.ST)
  5. Underdamped Langevin MCMC: A non-asymptotic analysis
    2017/07/12 by Xiang Cheng, Cheng, Xiang, Niladri S. Chatterji +5 · 18 citations
    Mathematics · Medicine · Physics and Astronomy · #Markov Chains and Monte Carlo Methods #Advanced Neuroimaging Techniques and Applications #Theoretical and Computational Physics
  6. Randomized Smoothing for Stochastic Optimization
    2011/03/22 by Duchi, John C., Bartlett, Peter L., Wainwright, Martin J. · 10 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Optimization and Control (math.OC)
  7. Recovery Guarantees for One-hidden-layer Neural Networks
    2017/06/10 by Kai Zhong, Zhong, Kai, Zhao Song +7 · 16 citations
    Computer Science · #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Neural Networks and Applications #Stochastic Gradient Optimization Techniques
  8. How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?
    2023/10/12 by Jingfeng Wu, Difan Zou, Wu, Jingfeng +9 · 18 citations
    Computer Science · #Domain Adaptation and Few-Shot Learning #Machine Learning and ELM #Advanced Neural Network Applications
  9. Sharp convergence rates for Langevin dynamics in the nonconvex setting
    2018/05/04 by Cheng, Xiang, Chatterji, Niladri S., Abbasi-Yadkori, Yasin +2 · 8 citations
    #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Probability (math.PR)
  10. On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration
    2020/04/09 by Mou, Wenlong, Li, Chris Junchi, Wainwright, Martin J. +2 · 9 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Statistics Theory (math.ST)
  11. Scaling Laws in Linear Regression: Compute, Parameters, and Data
    2024/06/12 by Licong Lin, Jingfeng Wu, Lin, Licong +7 · 15 citations
    Mathematics · #Advanced Statistical Methods and Models #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistics Theory (math.ST)
  12. Self-Distillation Amplifies Regularization in Hilbert Space
    2020/02/13 by Mobahi, Hossein, Farajtabar, Mehrdad, Bartlett, Peter L. · 7 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  13. The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima
    2022/10/04 by Bartlett, Peter L., Long, Philip M., Bousquet, Olivier · 8 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
  14. Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data
    2022/02/11 by Spencer Frei, Frei, Spencer, Niladri S. Chatterji +3 · 7 citations
    Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Mathematics #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Neural Networks and Applications #Statistics Theory (math.ST)
  15. Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems
    2018/12/20 by Malik, Dhruv, Pananjady, Ashwin, Bhatia, Kush +3 · 5 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
  16. Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity
    2019/07/25 by Mou, Wenlong, Flammarion, Nicolas, Wainwright, Martin J. +1 · 5 citations
    #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Probability (math.PR) #Statistics Theory (math.ST)
  17. Optimal and instance-dependent guarantees for Markovian linear stochastic approximation
    2021/12/23 by Mou, Wenlong, Pananjady, Ashwin, Wainwright, Martin J. +1 · 5 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Probability (math.PR) #Statistics Theory (math.ST)
  18. Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization
    2010/09/03 by Agarwal, Alekh, Bartlett, Peter L., Ravikumar, Pradeep +1 · 2 citations
    #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering
  19. Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning
    2009/11/30 by Benjamin I. P. Rubinstein, Rubinstein, Benjamin I. P., Peter L. Bartlett +5 · 2 citations
    Computer Science · #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques #Machine Learning and Data Classification
  20. Gradient descent with identity initialization efficiently learns\n positive definite linear transformations by deep residual networks
    2018/02/16 by Peter L. Bartlett, Bartlett, Peter L., David P. Helmbold +3 · 2 citations
    Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Matrix Theory and Algorithms #Neural and Evolutionary Computing (cs.NE) #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Statistics Theory (math.ST) #Stochastic Gradient Optimization Techniques
  21. A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption
    2018/10/01 by Bartlett, Peter L., Gabillon, Victor, Valko, Michal · 2 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  22. An Efficient Sampling Algorithm for Non-smooth Composite Potentials
    2019/10/01 by Wenlong Mou, Mou, Wenlong, Nicolas Flammarion +5 · 3 citations
    Engineering · Mathematics · #Computation (stat.CO) #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference
  23. Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency
    2024/02/24 by Wu, Jingfeng, Bartlett, Peter L., Telgarsky, Matus +1 · 4 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  24. In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization
    2024/02/22 by Zhang, Ruiqi, Wu, Jingfeng, Bartlett, Peter L. · 4 citations
    #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  25. Optimal Mean Estimation without a Variance
    2020/11/24 by Yeshwanth Cherapanamjeri, Cherapanamjeri, Yeshwanth, Nilesh Tripuraneni +5 · 3 citations
    Mathematics · #Statistical Methods and Inference #Statistical Methods and Bayesian Inference #Advanced Statistical Methods and Models
  26. The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks
    2021/08/25 by Chatterji, Niladri S., Long, Philip M., Bartlett, Peter L. · 2 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistics Theory (math.ST)
  27. Sharpness-Aware Minimization and the Edge of Stability
    2023/09/21 by Long, Philip M., Bartlett, Peter L. · 3 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE)
  28. Random Feature Amplification: Feature Learning and Generalization in Neural Networks
    2022/02/15 by Spencer Frei, Frei, Spencer, Niladri S. Chatterji +3 · 2 citations
    Computer Science · #Neural Networks and Applications #Stochastic Gradient Optimization Techniques #Advanced Neural Network Applications
  29. Blackwell Approachability and Low-Regret Learning are Equivalent
    2010/11/08 by Abernethy, Jacob, Bartlett, Peter L., Hazan, Elad · 1 citation
    #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #Machine Learning (cs.LG)
  30. Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data
    2022/10/13 by Frei, Spencer, Vardi, Gal, Bartlett, Peter L. +2 · 2 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  31. Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization
    2024/06/12 by Yuhang Cai, Jingfeng Wu, Cai, Yuhang +7 · 3 citations
    Engineering · #Advanced Photonic Communication Systems #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Photonic and Optical Devices #Semiconductor Lasers and Optical Devices
  32. Linear Programming for Large-Scale Markov Decision Problems
    2014/02/27 by Abbasi-Yadkori, Yasin, Bartlett, Peter L., Malek, Alan · 1 citation
    #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Mathematics #Numerical Analysis (math.NA) #Optimization and Control (math.OC)
  33. Off-policy estimation of linear functionals: Non-asymptotic theory for semi-parametric efficiency
    2022/09/26 by Wenlong Mou, Martin J. Wainwright, Mou, Wenlong +3 · 2 citations
    Decision Sciences · Mathematics · #Advanced Bandit Algorithms Research #Advanced Causal Inference Techniques #FOS: Computer and information sciences #FOS: Mathematics #Information Theory (cs.IT) #Machine Learning (stat.ML) #Statistics Theory (math.ST)
  34. Alternating minimization for dictionary learning: Local Convergence Guarantees
    2017/11/09 by Niladri S. Chatterji, Chatterji, Niladri S., Peter L. Bartlett +1 · 1 citation
    Computer Science · Engineering · #Blind Source Separation Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Microwave Imaging and Scattering Analysis #Sparse and Compressive Sensing Techniques
  35. Online learning with kernel losses
    2018/02/27 by Aldo Pacchiano, Pacchiano, Aldo, Niladri S. Chatterji +3 · 1 citation
    Decision Sciences · Computer Science · Engineering · #Advanced Bandit Algorithms Research #Machine Learning and Algorithms #Sparse and Compressive Sensing Techniques
  36. Fast Mean Estimation with Sub-Gaussian Rates
    2019/02/06 by Yeshwanth Cherapanamjeri, Cherapanamjeri, Yeshwanth, Nicolas Flammarion +3 · 1 citation
    Computer Science · Mathematics · #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Markov Chains and Monte Carlo Methods #Statistics Theory (math.ST)
  37. Oracle Lower Bounds for Stochastic Gradient Sampling Algorithms
    2020/02/01 by Chatterji, Niladri S., Bartlett, Peter L., Long, Philip M. · 1 citation
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistics Theory (math.ST)
  38. Langevin Monte Carlo without smoothness
    2019/05/30 by Niladri S. Chatterji, Chatterji, Niladri S., Jelena Diakonikolas +5 · 1 citation
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Statistical Methods and Inference
  39. High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm
    2019/08/28 by Mou, Wenlong, Ma, Yi-An, Wainwright, Martin J. +2 · 1 citation
    #Computation (stat.CO) #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
  40. Sampling for Bayesian Mixture Models: MCMC with Polynomial-Time Mixing
    2019/12/11 by Mou, Wenlong, Ho, Nhat, Wainwright, Martin J. +2 · 1 citation
    #Computation (stat.CO) #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Probability (math.PR)
  41. On Thompson Sampling with Langevin Algorithms
    2020/02/23 by Mazumdar, Eric, Pacchiano, Aldo, Ma, Yi-an +2 · 1 citation
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  42. Optimal Robust Linear Regression in Nearly Linear Time
    2020/07/16 by Cherapanamjeri, Yeshwanth, Aras, Efe, Tripuraneni, Nilesh +3 · 1 citation
    #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  43. Preference learning along multiple criteria: A game-theoretic perspective
    2021/05/05 by Bhatia, Kush, Pananjady, Ashwin, Bartlett, Peter L. +2 · 1 citation
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  44. On the Theory of Reinforcement Learning with Once-per-Episode Feedback
    2021/05/29 by Chatterji, Niladri S., Pacchiano, Aldo, Bartlett, Peter L. +1 · 1 citation
    #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  45. Optimal variance-reduced stochastic approximation in Banach spaces
    2022/01/21 by Wenlong Mou, Mou, Wenlong, Koulik Khamaru +7 · 1 citation
    Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Age of Information Optimization #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Reinforcement Learning in Robotics #Statistics Theory (math.ST)
  46. Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization
    2023/03/02 by Spencer Frei, Frei, Spencer, Gal Vardi +5 · 1 citation
    Computer Science · #Bayesian Modeling and Causal Inference #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  47. On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension
    2024/01/28 by Saptarshi Chakraborty, Chakraborty, Saptarshi, Peter L. Bartlett +1 · 1 citation
    Computer Science · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #Generative Adversarial Networks and Image Synthesis
  48. Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks
    2025/02/22 by Yuhang Cai, Kangjie Zhou, Cai, Yuhang +9 · 1 citation
    Computer Science · #FOS: Computer and information sciences #FOS: Mathematics #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Optimization and Control (math.OC) #Stochastic Gradient Optimization Techniques
  49. Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes
    2025/04/05 by Ruiqi Zhang, Jingfeng Wu, Zhang, Ruiqi +5 · 1 citation
    Computer Science · Mathematics · #Stochastic Gradient Optimization Techniques #Privacy-Preserving Technologies in Data #Statistical Methods and Inference