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Peter L. Bartlett

  1. RL2: Fast Reinforcement Learning via Slow Reinforcement Learning
    2016/11/09 by Yan Duan, John Schulman, Duan, Yan +9 · 1 voice · 138 citations
    Computer Science · #Reinforcement Learning in Robotics #Optimization and Search Problems #Advanced Neural Network Applications
  2. Can a Transformer Represent a Kalman Filter?
    2023/12/12 by Gautam Goel, Peter Bartlett, Peter L. Bartlett +2 · 2 voices · 7 citations
    Computer Science · #Target Tracking and Data Fusion in Sensor Networks #Distributed Sensor Networks and Detection Algorithms #Gaussian Processes and Bayesian Inference
  3. Spectrally-normalized margin bounds for neural networks
    2017/06/26 by Peter L. Bartlett, Dylan J. Foster, Bartlett, Peter +3 · 115 citations
    Computer Science · #Neural Networks and Applications #Machine Learning and ELM #Face and Expression Recognition
  4. Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
    2018/03/05 by Dong Yin, Yudong Chen, Yin, Dong +5 · 105 citations
    Computer Science · #Bayesian Modeling and Causal Inference #Cryptography and Security (cs.CR) #Distributed #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Parallel #and Cluster Computing (cs.DC)
  5. Trained Transformers Learn Linear Models In-Context
    2023/06/16 by Ruiqi Zhang, Zhang, Ruiqi, Spencer Frei +3 · 1 voice · 68 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
  6. REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs
    2012/05/09 by Peter L. Bartlett, Ambuj Tewari, Bartlett, Peter L. +1 · 38 citations
    Decision Sciences · Computer Science · #Advanced Bandit Algorithms Research #Reinforcement Learning in Robotics #Age of Information Optimization
  7. Underdamped Langevin MCMC: A non-asymptotic analysis
    2017/07/12 by Xiang Cheng, Niladri S. Chatterji, Cheng, Xiang +5 · 32 citations
    Mathematics · Medicine · Physics and Astronomy · #Markov Chains and Monte Carlo Methods #Advanced Neuroimaging Techniques and Applications #Theoretical and Computational Physics
  8. Fast Best-of-N Decoding via Speculative Rejection
    2024/10/26 by Hanshi Sun, Momin Haider, Sun, Hanshi +13 · 52 citations
    Biochemistry, Genetics and Molecular Biology · Computer Science · #Cellular Automata and Applications #Computation and Language (cs.CL) #DNA and Biological Computing #Error Correcting Code Techniques #FOS: Computer and information sciences
  9. Recovery Guarantees for One-hidden-layer Neural Networks
    2017/06/10 by Kai Zhong, Zhao Song, Zhong, Kai +7 · 25 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
  10. Benign overfitting in ridge regression
    2020/09/29 by Alexander Tsigler, Tsigler, A., Peter L. Bartlett +1 · 23 citations
    Engineering · Computer Science · Mathematics · #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques #Statistical Methods and Inference
  11. How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?
    2023/10/12 by Jingfeng Wu, Difan Zou, Wu, Jingfeng +9 · 23 citations
    Computer Science · #Domain Adaptation and Few-Shot Learning #Machine Learning and ELM #Advanced Neural Network Applications
  12. Convergence of Langevin MCMC in KL-divergence
    2017/05/25 by Xiang Cheng, Peter L. Bartlett, Cheng, Xiang +1 · 14 citations
    Mathematics · #Markov Chains and Monte Carlo Methods #Statistical Methods and Inference #Stochastic processes and statistical mechanics
  13. Rademacher Complexity for Adversarially Robust Generalization
    2018/10/29 by Dong Yin, Kannan Ramchandran, Yin, Dong +3 · 16 citations
    Computer Science · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #Cryptography and Security (cs.CR) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE)
  14. Self-Distillation Amplifies Regularization in Hilbert Space
    2020/02/13 by Hossein Mobahi, Mobahi, Hossein, Mehrdad Farajtabar +3 · 13 citations
    Computer Science · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Stochastic Gradient Optimization Techniques
  15. Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity
    2019/07/25 by Wenlong Mou, Mou, Wenlong, Nicolas Flammarion +5 · 9 citations
    Computer Science · Engineering · Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Probability (math.PR) #Sparse and Compressive Sensing Techniques #Statistics Theory (math.ST)
  16. Scaling Laws in Linear Regression: Compute, Parameters, and Data
    2024/06/12 by Licong Lin, Lin, Licong, Jingfeng Wu +7 · 16 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)
  17. 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)
  18. Stochastic Bandits with Linear Constraints
    2020/06/17 by Aldo Pacchiano, Pacchiano, Aldo, Mohammad Ghavamzadeh +5 · 6 citations
    Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Search Problems #Smart Grid Energy Management
  19. Linear Programming for Large-Scale Markov Decision Problems
    2014/02/27 by Yasin Abbasi-Yadkori, Abbasi-Yadkori, Yasin, Peter L. Bartlett +3 · 5 citations
    Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning and Algorithms #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Reinforcement Learning in Robotics
  20. Dropout: Explicit Forms and Capacity Control
    2020/03/06 by Raman Arora, Arora, Raman, Peter L. Bartlett +5 · 8 citations
    Computer Science · #Advanced Neural Network Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Stochastic Gradient Optimization Techniques
  21. Gradient Diversity: a Key Ingredient for Scalable Distributed Learning
    2017/06/18 by Dong Yin, Yin, Dong, Ashwin Pananjady +11 · 1 voice · 2 citations
    Computer Science · Mathematics · #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Markov Chains and Monte Carlo Methods #Parallel #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques #and Cluster Computing (cs.DC) #cs.DC #cs.LG
  22. Optimal and instance-dependent guarantees for Markovian linear stochastic approximation
    2021/12/23 by Wenlong Mou, Mou, Wenlong, Ashwin Pananjady +5 · 5 citations
    Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Optimization and Control (math.OC) #Probability (math.PR) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics Theory (math.ST)
  23. Gradient descent with identity initialization efficiently learns\n positive definite linear transformations by deep residual networks
    2018/02/16 by Peter L. Bartlett, David P. Helmbold, Bartlett, Peter L. +3 · 3 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
  24. An Efficient Sampling Algorithm for Non-smooth Composite Potentials
    2019/10/01 by Wenlong Mou, Nicolas Flammarion, Mou, Wenlong +5 · 5 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
  25. OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits
    2019/05/24 by Niladri S. Chatterji, Chatterji, Niladri S., Vidya Muthukumar +4 · 5 citations
    Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #Age of Information Optimization #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Smart Grid Energy Management
  26. High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm
    2019/08/28 by Wenlong Mou, Mou, Wenlong, Yi-An Ma +7 · 3 citations
    Computer Science · Mathematics · Medicine · #Advanced Neuroimaging Techniques and Applications #Computation (stat.CO) #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) #Markov Chains and Monte Carlo Methods #Optimization and Control (math.OC)
  27. Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency
    2024/02/24 by Jingfeng Wu, Wu, Jingfeng, Peter L. Bartlett +5 · 6 citations
    Decision Sciences · Engineering · #Auction Theory and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Scheduling and Optimization Algorithms
  28. 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 · 3 citations
    Computer Science · #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques #Machine Learning and Data Classification
  29. Langevin Monte Carlo without smoothness
    2019/05/30 by Niladri S. Chatterji, Jelena Diakonikolas, Chatterji, Niladri S. +5 · 3 citations
    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
  30. Random Feature Amplification: Feature Learning and Generalization in Neural Networks
    2022/02/15 by Spencer Frei, Frei, Spencer, Niladri S. Chatterji +3 · 3 citations
    Computer Science · #Neural Networks and Applications #Stochastic Gradient Optimization Techniques #Advanced Neural Network Applications
  31. Online learning with kernel losses
    2018/02/27 by Aldo Pacchiano, Pacchiano, Aldo, Niladri S. Chatterji +3 · 3 citations
    Decision Sciences · Computer Science · Engineering · #Advanced Bandit Algorithms Research #Machine Learning and Algorithms #Sparse and Compressive Sensing Techniques
  32. Optimal Mean Estimation without a Variance
    2020/11/24 by Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Cherapanamjeri, Yeshwanth +5 · 3 citations
    Mathematics · #Statistical Methods and Inference #Statistical Methods and Bayesian Inference #Advanced Statistical Methods and Models
  33. On the Theory of Reinforcement Learning with Once-per-Episode Feedback
    2021/05/29 by Niladri S. Chatterji, Chatterji, Niladri S., Aldo Pacchiano +5 · 2 citations
    Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Reinforcement Learning in Robotics #Smart Grid Energy Management
  34. 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 · 4 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
  35. Optimal variance-reduced stochastic approximation in Banach spaces
    2022/01/21 by Wenlong Mou, Mou, Wenlong, Koulik Khamaru +7 · 2 citations
    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)
  36. Implicit Diffusion: Efficient Optimization through Stochastic Sampling
    2024/02/08 by Pierre Marion, Anna Korba, Marion, Pierre +16 · 2 voices · 2 citations
    Computer Science · #Neural Networks and Applications
  37. Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data
    2022/10/13 by Spencer Frei, Gal Vardi, Frei, Spencer +7 · 2 citations
    Computer Science · Engineering · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  38. Optimal Robust Linear Regression in Nearly Linear Time
    2020/07/16 by Yeshwanth Cherapanamjeri, Cherapanamjeri, Yeshwanth, Efe Aras +9 · 2 citations
    Computer Science · Engineering · Mathematics · #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference
  39. 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)
  40. Alternating minimization for dictionary learning: Local Convergence Guarantees
    2017/11/09 by Niladri S. Chatterji, Peter L. Bartlett, Chatterji, Niladri S. +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
  41. 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)
  42. A Diffusion Process Perspective on Posterior Contraction Rates for Parameters
    2019/09/03 by Wenlong Mou, Nhat Ho, Mou, Wenlong +7 · 1 citation
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Mathematics #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics Theory (math.ST)
  43. 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 · 2 citations
    Computer Science · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #Generative Adversarial Networks and Image Synthesis
  44. Sampling for Bayesian Mixture Models: MCMC with Polynomial-Time Mixing
    2019/12/11 by Wenlong Mou, Nhat Ho, Mou, Wenlong +7 · 1 citation
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Probability (math.PR) #Statistical Methods and Inference
  45. Regret Bound Balancing and Elimination for Model Selection in Bandits\n and RL
    2020/12/23 by Aldo Pacchiano, Christoph Dann, Pacchiano, Aldo +5 · 4 citations
    Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Other Statistics (stat.OT) #Reinforcement Learning in Robotics
  46. Preference learning along multiple criteria: A game-theoretic\n perspective
    2021/05/04 by Kush Bhatia, Ashwin Pananjady, Bhatia, Kush +7 · 1 citation
    Business, Management and Accounting · Decision Sciences · Economics, Econometrics and Finance · #Consumer Market Behavior and Pricing #Economic and Environmental Valuation #FOS: Computer and information sciences #Game Theory and Voting Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Multi-Criteria Decision Making
  47. 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)
  48. Large-Scale Markov Decision Problems via the Linear Programming Dual
    2019/01/06 by Yasin Abbasi-Yadkori, Peter L. Bartlett, Abbasi-Yadkori, Yasin +5 · 2 citations
    Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Optimization and Search Problems #Reinforcement Learning in Robotics
  49. Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression
    2025/02/18 by Jingfeng Wu, Wu, Jingfeng, Peter L. Bartlett +5 · 2 citations
    Computer Science · #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  50. Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks
    2025/02/22 by Yuhang Cai, Cai, Yuhang, Kangjie Zhou +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
  51. 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
  52. Hilbert Operator for Progressive Encoding (HOPE): A Mathematical Framework for Deconstructing Learned Representations in Deep Networks
    2026/07/23 by Hossein Mobahi, Peter L. Bartlett
    #cs.LG #cs.AI #stat.ML