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ORCIDPeter L. Bartlett
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- affiliation: University of California at Berkeley, Department of Statistics, CA, USA
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2020 – today
- 2024
[j65]Peter L. Bartlett, Philip M. Long:
Corrigendum to "Prediction, learning, uniform convergence, and scale-sensitive dimensions" [J. Comput. Syst. Sci. 56 (2) (1998) 174-190]. J. Comput. Syst. Sci. 140: 103465 (2024)- [j64]Ruiqi Zhang, Spencer Frei, Peter L. Bartlett:
Trained Transformers Learn Linear Models In-Context. J. Mach. Learn. Res. 25: 49:1-49:55 (2024) - [j63]Philip M. Long, Peter L. Bartlett:
Sharpness-Aware Minimization and the Edge of Stability. J. Mach. Learn. Res. 25: 179:1-179:20 (2024) - [j62]Wenlong Mou, Nhat Ho, Martin J. Wainwright
, Peter L. Bartlett, Michael I. Jordan:
A Diffusion Process Perspective on Posterior Contraction Rates for Parameters. SIAM J. Math. Data Sci. 6(2): 553-577 (2024) - [c142]Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu:
Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency. COLT 2024: 5019-5073 - [c141]Saptarshi Chakraborty, Peter L. Bartlett:
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data. ICLR 2024 - [c140]Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett:
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? ICLR 2024 - [c139]Gautam Goel, Peter L. Bartlett:
Can a transformer represent a Kalman filter? L4DC 2024: 1502-1512 - [i101]Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett:
Contextual Bandits with Stage-wise Constraints. CoRR abs/2401.08016 (2024) - [i100]Saptarshi Chakraborty, Peter L. Bartlett:
On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension. CoRR abs/2401.15801 (2024) - [i99]Ruiqi Zhang, Jingfeng Wu, Peter L. Bartlett:
In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization. CoRR abs/2402.14951 (2024) - [i98]Saptarshi Chakraborty, Peter L. Bartlett:
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data. CoRR abs/2402.15710 (2024) - [i97]Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu:
Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency. CoRR abs/2402.15926 (2024) - [i96]Licong Lin, Jingfeng Wu, Sham M. Kakade, Peter L. Bartlett, Jason D. Lee:
Scaling Laws in Linear Regression: Compute, Parameters, and Data. CoRR abs/2406.08466 (2024) - [i95]Yuhang Cai, Jingfeng Wu, Song Mei, Michael Lindsey, Peter L. Bartlett:
Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization. CoRR abs/2406.08654 (2024) - [i94]Naman Agarwal, Xinyi Chen, Evan Dogariu, Vladimir Feinberg, Daniel Suo, Peter L. Bartlett, Elad Hazan:
FutureFill: Fast Generation from Convolutional Sequence Models. CoRR abs/2410.03766 (2024) - 2023
- [j61]Alexander Tsigler, Peter L. Bartlett:
Benign overfitting in ridge regression. J. Mach. Learn. Res. 24: 123:1-123:76 (2023) - [j60]Juan C. Perdomo, Akshay Krishnamurthy, Peter L. Bartlett, Sham M. Kakade:
A Complete Characterization of Linear Estimators for Offline Policy Evaluation. J. Mach. Learn. Res. 24: 284:1-284:50 (2023) - [j59]Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett:
Random Feature Amplification: Feature Learning and Generalization in Neural Networks. J. Mach. Learn. Res. 24: 303:1-303:49 (2023) - [j58]Peter L. Bartlett, Philip M. Long, Olivier Bousquet:
The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima. J. Mach. Learn. Res. 24: 316:1-316:36 (2023) - [c138]Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. ALT 2023: 1166-1215 - [c137]Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro:
Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization. COLT 2023: 3173-3228 - [c136]Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu:
Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. ICLR 2023 - [c135]Spencer Frei, Gal Vardi, Peter L. Bartlett, Nati Srebro:
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. NeurIPS 2023 - [i93]Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro:
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. CoRR abs/2303.01456 (2023) - [i92]Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro:
Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization. CoRR abs/2303.01462 (2023) - [i91]Peter L. Bartlett, Philip M. Long:
Prediction, Learning, Uniform Convergence, and Scale-sensitive Dimensions. CoRR abs/2304.11059 (2023) - [i90]Ruiqi Zhang, Spencer Frei, Peter L. Bartlett:
Trained Transformers Learn Linear Models In-Context. CoRR abs/2306.09927 (2023) - [i89]Philip M. Long, Peter L. Bartlett:
Sharpness-Aware Minimization and the Edge of Stability. CoRR abs/2309.12488 (2023) - [i88]Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett:
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? CoRR abs/2310.08391 (2023) - [i87]Gautam Goel, Peter L. Bartlett:
Can a Transformer Represent a Kalman Filter? CoRR abs/2312.06937 (2023) - 2022
- [j57]Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, Peter L. Bartlett:
An Efficient Sampling Algorithm for Non-smooth Composite Potentials. J. Mach. Learn. Res. 23: 233:1-233:50 (2022) - [j56]Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett:
The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks. J. Mach. Learn. Res. 23: 263:1-263:48 (2022) - [c134]Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan:
Optimal Mean Estimation without a Variance. COLT 2022: 356-357 - [c133]Peter L. Bartlett, Piotr Indyk, Tal Wagner:
Generalization Bounds for Data-Driven Numerical Linear Algebra. COLT 2022: 2013-2040 - [c132]Wenlong Mou, Ashwin Pananjady, Martin J. Wainwright, Peter L. Bartlett:
Optimal and instance-dependent guarantees for Markovian linear stochastic approximation. COLT 2022: 2060-2061 - [c131]Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett:
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data. COLT 2022: 2668-2703 - [i86]Wenlong Mou, Koulik Khamaru, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
Optimal variance-reduced stochastic approximation in Banach spaces. CoRR abs/2201.08518 (2022) - [i85]Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett:
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data. CoRR abs/2202.05928 (2022) - [i84]Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett:
Random Feature Amplification: Feature Learning and Generalization in Neural Networks. CoRR abs/2202.07626 (2022) - [i83]Juan C. Perdomo, Akshay Krishnamurthy, Peter L. Bartlett, Sham M. Kakade:
A Sharp Characterization of Linear Estimators for Offline Policy Evaluation. CoRR abs/2203.04236 (2022) - [i82]Peter L. Bartlett, Piotr Indyk, Tal Wagner:
Generalization Bounds for Data-Driven Numerical Linear Algebra. CoRR abs/2206.07886 (2022) - [i81]Aldo Pacchiano, Ofir Nachum, Nilesh Tripuraneni, Peter L. Bartlett:
Joint Representation Training in Sequential Tasks with Shared Structure. CoRR abs/2206.12441 (2022) - [i80]Wenlong Mou, Martin J. Wainwright, Peter L. Bartlett:
Off-policy estimation of linear functionals: Non-asymptotic theory for semi-parametric efficiency. CoRR abs/2209.13075 (2022) - [i79]Peter L. Bartlett, Philip M. Long, Olivier Bousquet:
The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima. CoRR abs/2210.01513 (2022) - [i78]Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu:
Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. CoRR abs/2210.07082 (2022) - 2021
- [j55]Peter L. Bartlett, Andrea Montanari, Alexander Rakhlin:
Deep learning: a statistical viewpoint. Acta Numer. 30: 87-201 (2021) - [j54]Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm. J. Mach. Learn. Res. 22: 42:1-42:41 (2021) - [j53]Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett:
When Does Gradient Descent with Logistic Loss Find Interpolating Two-Layer Networks? J. Mach. Learn. Res. 22: 159:1-159:48 (2021) - [j52]Peter L. Bartlett, Philip M. Long:
Failures of Model-dependent Generalization Bounds for Least-norm Interpolation. J. Mach. Learn. Res. 22: 204:1-204:15 (2021) - [c130]Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang:
Stochastic Bandits with Linear Constraints. AISTATS 2021: 2827-2835 - [c129]Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett:
When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations? COLT 2021: 927-1027 - [c128]Juan C. Perdomo, Max Simchowitz, Alekh Agarwal, Peter L. Bartlett:
Towards a Dimension-Free Understanding of Adaptive Linear Control. COLT 2021: 3681-3770 - [c127]Raman Arora, Peter L. Bartlett, Poorya Mianjy, Nathan Srebro:
Dropout: Explicit Forms and Capacity Control. ICML 2021: 351-361 - [c126]Kush Bhatia, Peter L. Bartlett, Anca D. Dragan, Jacob Steinhardt:
Agnostic Learning with Unknown Utilities. ITCS 2021: 55:1-55:20 - [c125]Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum:
Near Optimal Policy Optimization via REPS. NeurIPS 2021: 1100-1110 - [c124]Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Reinforcement Learning with Once-per-Episode Feedback. NeurIPS 2021: 3401-3412 - [c123]Peter L. Bartlett, Sébastien Bubeck, Yeshwanth Cherapanamjeri:
Adversarial Examples in Multi-Layer Random ReLU Networks. NeurIPS 2021: 9241-9252 - [i77]Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett:
When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations? CoRR abs/2102.04998 (2021) - [i76]Peter L. Bartlett, Andrea Montanari, Alexander Rakhlin:
Deep learning: a statistical viewpoint. CoRR abs/2103.09177 (2021) - [i75]Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum:
Near Optimal Policy Optimization via REPS. CoRR abs/2103.09756 (2021) - [i74]Lin Chen, Bruno Scherrer, Peter L. Bartlett:
Infinite-Horizon Offline Reinforcement Learning with Linear Function Approximation: Curse of Dimensionality and Algorithm. CoRR abs/2103.09847 (2021) - [i73]Juan C. Perdomo, Max Simchowitz, Alekh Agarwal, Peter L. Bartlett:
Towards a Dimension-Free Understanding of Adaptive Linear Control. CoRR abs/2103.10620 (2021) - [i72]Kush Bhatia, Peter L. Bartlett, Anca D. Dragan, Jacob Steinhardt:
Agnostic learning with unknown utilities. CoRR abs/2104.08482 (2021) - [i71]Kush Bhatia, Ashwin Pananjady, Peter L. Bartlett, Anca D. Dragan, Martin J. Wainwright:
Preference learning along multiple criteria: A game-theoretic perspective. CoRR abs/2105.01850 (2021) - [i70]Jeffrey Chan, Aldo Pacchiano, Nilesh Tripuraneni, Yun S. Song, Peter L. Bartlett, Michael I. Jordan:
Parallelizing Contextual Linear Bandits. CoRR abs/2105.10590 (2021) - [i69]Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Reinforcement Learning with Once-per-Episode Feedback. CoRR abs/2105.14363 (2021) - [i68]Peter L. Bartlett, Sébastien Bubeck, Yeshwanth Cherapanamjeri:
Adversarial Examples in Multi-Layer Random ReLU Networks. CoRR abs/2106.12611 (2021) - [i67]Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett:
The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks. CoRR abs/2108.11489 (2021) - [i66]Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. CoRR abs/2111.04873 (2021) - [i65]Wenlong Mou, Ashwin Pananjady, Martin J. Wainwright, Peter L. Bartlett:
Optimal and instance-dependent guarantees for Markovian linear stochastic approximation. CoRR abs/2112.12770 (2021) - 2020
- [j51]Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter L. Bartlett, Martin J. Wainwright:
Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems. J. Mach. Learn. Res. 21: 21:1-21:51 (2020) - [c122]Niladri S. Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter L. Bartlett:
Langevin Monte Carlo without smoothness. AISTATS 2020: 1716-1726 - [c121]Niladri S. Chatterji, Vidya Muthukumar, Peter L. Bartlett:
OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits. AISTATS 2020: 1844-1854 - [c120]Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration. COLT 2020: 2947-2997 - [c119]Xiang Cheng, Dong Yin, Peter L. Bartlett, Michael I. Jordan:
Stochastic Gradient and Langevin Processes. ICML 2020: 1810-1819 - [c118]Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
Accelerated Message Passing for Entropy-Regularized MAP Inference. ICML 2020: 5736-5746 - [c117]Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett:
On Approximate Thompson Sampling with Langevin Algorithms. ICML 2020: 6797-6807 - [c116]Thanh Tan Nguyen, Nan Ye, Peter L. Bartlett:
Greedy Convex Ensemble. IJCAI 2020: 3101-3107 - [c115]Kush Bhatia, Ashwin Pananjady, Peter L. Bartlett, Anca D. Dragan, Martin J. Wainwright:
Preference learning along multiple criteria: A game-theoretic perspective. NeurIPS 2020 - [c114]Hossein Mobahi, Mehrdad Farajtabar, Peter L. Bartlett:
Self-Distillation Amplifies Regularization in Hilbert Space. NeurIPS 2020 - [i64]Niladri S. Chatterji, Peter L. Bartlett, Philip M. Long:
Oracle lower bounds for stochastic gradient sampling algorithms. CoRR abs/2002.00291 (2020) - [i63]Hossein Mobahi, Mehrdad Farajtabar, Peter L. Bartlett:
Self-Distillation Amplifies Regularization in Hilbert Space. CoRR abs/2002.05715 (2020) - [i62]Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan:
On Thompson Sampling with Langevin Algorithms. CoRR abs/2002.10002 (2020) - [i61]Raman Arora, Peter L. Bartlett, Poorya Mianjy, Nathan Srebro:
Dropout: Explicit Forms and Capacity Control. CoRR abs/2003.03397 (2020) - [i60]Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration. CoRR abs/2004.04719 (2020) - [i59]Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang:
Stochastic Bandits with Linear Constraints. CoRR abs/2006.10185 (2020) - [i58]Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
Accelerated Message Passing for Entropy-Regularized MAP Inference. CoRR abs/2007.00699 (2020) - [i57]Yeshwanth Cherapanamjeri, Efe Aras, Nilesh Tripuraneni, Michael I. Jordan, Nicolas Flammarion, Peter L. Bartlett:
Optimal Robust Linear Regression in Nearly Linear Time. CoRR abs/2007.08137 (2020) - [i56]Peter L. Bartlett, Philip M. Long:
Failures of model-dependent generalization bounds for least-norm interpolation. CoRR abs/2010.08479 (2020) - [i55]Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan:
Optimal Mean Estimation without a Variance. CoRR abs/2011.12433 (2020) - [i54]Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett:
When does gradient descent with logistic loss find interpolating two-layer networks? CoRR abs/2012.02409 (2020) - [i53]Aldo Pacchiano, Christoph Dann, Claudio Gentile, Peter L. Bartlett:
Regret Bound Balancing and Elimination for Model Selection in Bandits and RL. CoRR abs/2012.13045 (2020)
2010 – 2019
- 2019
- [j50]Peter L. Bartlett, Nick Harvey, Christopher Liaw, Abbas Mehrabian:
Nearly-tight VC-dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks. J. Mach. Learn. Res. 20: 63:1-63:17 (2019) - [j49]Peter L. Bartlett, David P. Helmbold, Philip M. Long:
Gradient Descent with Identity Initialization Efficiently Learns Positive-Definite Linear Transformations by Deep Residual Networks. Neural Comput. 31(3) (2019) - [c113]Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter L. Bartlett, Martin J. Wainwright:
Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems. AISTATS 2019: 2916-2925 - [c112]Vidya Muthukumar, Mitas Ray, Anant Sahai, Peter L. Bartlett:
Best of many worlds: Robust model selection for online supervised learning. AISTATS 2019: 3177-3186 - [c111]Peter L. Bartlett, Victor Gabillon, Michal Valko:
A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption. ALT 2019: 184-206 - [c110]Yeshwanth Cherapanamjeri, Peter L. Bartlett:
Testing Symmetric Markov Chains Without Hitting. COLT 2019: 758-785 - [c109]Yeshwanth Cherapanamjeri, Nicolas Flammarion, Peter L. Bartlett:
Fast Mean Estimation with Sub-Gaussian Rates. COLT 2019: 786-806 - [c108]Peter L. Bartlett, Victor Gabillon, Jennifer Healey, Michal Valko:
Scale-free adaptive planning for deterministic dynamics & discounted rewards. ICML 2019: 495-504 - [c107]Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett:
Online learning with kernel losses. ICML 2019: 971-980 - [c106]Yasin Abbasi-Yadkori, Peter L. Bartlett, Kush Bhatia, Nevena Lazic, Csaba Szepesvári, Gellért Weisz:
POLITEX: Regret Bounds for Policy Iteration using Expert Prediction. ICML 2019: 3692-3702 - [c105]Dong Yin, Yudong Chen, Kannan Ramchandran, Peter L. Bartlett:
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning. ICML 2019: 7074-7084 - [c104]Dong Yin, Kannan Ramchandran, Peter L. Bartlett:
Rademacher Complexity for Adversarially Robust Generalization. ICML 2019: 7085-7094 - [i52]Yasin Abbasi-Yadkori, Peter L. Bartlett, Xi Chen, Alan Malek:
Large-Scale Markov Decision Problems via the Linear Programming Dual. CoRR abs/1901.01992 (2019) - [i51]Xiang Cheng, Peter L. Bartlett, Michael I. Jordan:
Quantitative Central Limit Theorems for Discrete Stochastic Processes. CoRR abs/1902.00832 (2019) - [i50]Yi-An Ma, Niladri S. Chatterji, Xiang Cheng, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan:
Is There an Analog of Nesterov Acceleration for MCMC? CoRR abs/1902.00996 (2019) - [i49]Yeshwanth Cherapanamjeri, Nicolas Flammarion, Peter L. Bartlett:
Fast Mean Estimation with Sub-Gaussian Rates. CoRR abs/1902.01998 (2019) - [i48]Yeshwanth Cherapanamjeri, Peter L. Bartlett:
Testing Markov Chains without Hitting. CoRR abs/1902.01999 (2019) - [i47]Niladri S. Chatterji, Vidya Muthukumar, Peter L. Bartlett:
OSOM: A Simultaneously Optimal Algorithm for Multi-Armed and Linear Contextual Bandits. CoRR abs/1905.10040 (2019) - [i46]Niladri S. Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter L. Bartlett:
Langevin Monte Carlo without Smoothness. CoRR abs/1905.13285 (2019) - [i45]Peter L. Bartlett, Philip M. Long, Gábor Lugosi, Alexander Tsigler:
Benign Overfitting in Linear Regression. CoRR abs/1906.11300 (2019) - [i44]Xiang Cheng, Dong Yin, Peter L. Bartlett, Michael I. Jordan:
Quantitative W1 Convergence of Langevin-Like Stochastic Processes with Non-Convex Potential State-Dependent Noise. CoRR abs/1907.03215 (2019) - [i43]Kush Bhatia, Yi-An Ma, Anca D. Dragan, Peter L. Bartlett, Michael I. Jordan:
Bayesian Robustness: A Nonasymptotic Viewpoint. CoRR abs/1907.11826 (2019) - [i42]Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm. CoRR abs/1908.10859 (2019) - [i41]Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, Peter L. Bartlett:
An Efficient Sampling Algorithm for Non-smooth Composite Potentials. CoRR abs/1910.00551 (2019) - [i40]Tan M. Nguyen, Nan Ye, Peter L. Bartlett:
Learning Near-optimal Convex Combinations of Basis Models with Generalization Guarantees. CoRR abs/1910.03742 (2019) - [i39]Peter L. Bartlett, Jonathan Baxter:
Hebbian Synaptic Modifications in Spiking Neurons that Learn. CoRR abs/1911.07247 (2019) - [i38]Wenlong Mou, Nhat Ho, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
Sampling for Bayesian Mixture Models: MCMC with Polynomial-Time Mixing. CoRR abs/1912.05153 (2019) - 2018
- [c103]Xiang Cheng, Fred (Farbod) Roosta, Stefan Palombo, Peter L. Bartlett, Michael W. Mahoney:
FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods. AISTATS 2018: 404-414 - [c102]Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett:
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning. AISTATS 2018: 1998-2007 - [c101]Xiang Cheng, Peter L. Bartlett:
Convergence of Langevin MCMC in KL-divergence. ALT 2018: 186-211 - [c100]Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, Michael I. Jordan:
Underdamped Langevin MCMC: A non-asymptotic analysis. COLT 2018: 300-323 - [c99]Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon, Alan Malek, Michal Valko:
Best of both worlds: Stochastic & adversarial best-arm identification. COLT 2018: 918-949 - [c98]Martin Péron, Peter L. Bartlett, Kai Helge Becker, Kate J. Helmstedt, Iadine Chadès:
Two Approximate Dynamic Programming Algorithms for Managing Complete SIS Networks. COMPASS 2018: 8:1-8:10 - [c97]Peter L. Bartlett, David P. Helmbold, Philip M. Long:
Gradient descent with identity initialization efficiently learns positive definite linear transformations. ICML 2018: 520-529 - [c96]Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. ICML 2018: 763-772 - [c95]Dong Yin, Yudong Chen, Kannan Ramchandran, Peter L. Bartlett:
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates. ICML 2018: 5636-5645 - [c94]Alan Malek, Peter L. Bartlett:
Horizon-Independent Minimax Linear Regression. NeurIPS 2018: 5264-5273 - [c93]Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan:
Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation. NeurIPS 2018: 7016-7025 - [i37]Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. CoRR abs/1802.05431 (2018) - [i36]Peter L. Bartlett, David P. Helmbold, Philip M. Long:
Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks. CoRR abs/1802.06093 (2018) - [i35]Aldo Pacchiano, Niladri S. Chatterji, Peter L. Bartlett:
Online learning with kernel losses. CoRR abs/1802.09732 (2018) - [i34]Dong Yin, Yudong Chen, Kannan Ramchandran, Peter L. Bartlett:
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates. CoRR abs/1803.01498 (2018) - [i33]Peter L. Bartlett, Steven N. Evans, Philip M. Long:
Representing smooth functions as compositions of near-identity functions with implications for deep network optimization. CoRR abs/1804.05012 (2018) - [i32]Xiang Cheng, Niladri S. Chatterji, Yasin Abbasi-Yadkori, Peter L. Bartlett, Michael I. Jordan:
Sharp Convergence Rates for Langevin Dynamics in the Nonconvex Setting. CoRR abs/1805.01648 (2018) - [i31]Vidya Muthukumar, Mitas Ray, Anant Sahai, Peter L. Bartlett:
Best of many worlds: Robust model selection for online supervised learning. CoRR abs/1805.08562 (2018) - [i30]Dong Yin, Yudong Chen, Kannan Ramchandran, Peter L. Bartlett:
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning. CoRR abs/1806.05358 (2018) - [i29]Peter L. Bartlett, Victor Gabillon, Michal Valko:
A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption. CoRR abs/1810.00997 (2018) - [i28]Dong Yin, Kannan Ramchandran, Peter L. Bartlett:
Rademacher Complexity for Adversarially Robust Generalization. CoRR abs/1810.11914 (2018) - [i27]Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan:
Gen-Oja: A Simple and Efficient Algorithm for Streaming Generalized Eigenvector Computation. CoRR abs/1811.08393 (2018) - [i26]Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter L. Bartlett, Martin J. Wainwright:
Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems. CoRR abs/1812.08305 (2018) - 2017
- [j48]Fares Hedayati, Peter L. Bartlett:
Exchangeability Characterizes Optimality of Sequential Normalized Maximum Likelihood and Bayesian Prediction. IEEE Trans. Inf. Theory 63(10): 6767-6773 (2017) - [c92]Martin Péron, Kai Helge Becker, Peter L. Bartlett, Iadine Chades:
Fast-Tracking Stationary MOMDPs for Adaptive Management Problems. AAAI 2017: 4531-4537 - [c91]Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon, Alan Malek:
Hit-and-Run for Sampling and Planning in Non-Convex Spaces. AISTATS 2017: 888-895 - [c90]Kai Zhong, Zhao Song, Prateek Jain, Peter L. Bartlett, Inderjit S. Dhillon:
Recovery Guarantees for One-hidden-layer Neural Networks. ICML 2017: 4140-4149 - [c89]Niladri S. Chatterji, Peter L. Bartlett:
Alternating minimization for dictionary learning with random initialization. NIPS 2017: 1997-2006 - [c88]Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon:
Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem. NIPS 2017: 3033-3042 - [c87]Peter L. Bartlett, Dylan J. Foster, Matus Telgarsky:
Spectrally-normalized margin bounds for neural networks. NIPS 2017: 6240-6249 - [c86]Walid Krichene, Peter L. Bartlett:
Acceleration and Averaging in Stochastic Descent Dynamics. NIPS 2017: 6796-6806 - [i25]Nan Ye, Peter L. Bartlett:
Approximate and Stochastic Greedy Optimization. CoRR abs/1705.09396 (2017) - [i24]Kai Zhong, Zhao Song, Prateek Jain, Peter L. Bartlett, Inderjit S. Dhillon:
Recovery Guarantees for One-hidden-layer Neural Networks. CoRR abs/1706.03175 (2017) - [i23]Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett:
Gradient Diversity Empowers Distributed Learning. CoRR abs/1706.05699 (2017) - [i22]Peter L. Bartlett, Dylan J. Foster, Matus Telgarsky:
Spectrally-normalized margin bounds for neural networks. CoRR abs/1706.08498 (2017) - [i21]Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, Michael I. Jordan:
Underdamped Langevin MCMC: A non-asymptotic analysis. CoRR abs/1707.03663 (2017) - [i20]Niladri S. Chatterji, Peter L. Bartlett:
Alternating minimization for dictionary learning with random initialization. CoRR abs/1711.03634 (2017) - 2016
- [c85]Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Ronald Ortner, Peter L. Bartlett:
Improved Learning Complexity in Combinatorial Pure Exploration Bandits. AISTATS 2016: 1004-1012 - [c84]Yasin Abbasi-Yadkori, Peter L. Bartlett, Stephen J. Wright:
A Fast and Reliable Policy Improvement Algorithm. AISTATS 2016: 1338-1346 - [c83]Walid Krichene, Alexandre M. Bayen, Peter L. Bartlett:
Adaptive Averaging in Accelerated Descent Dynamics. NIPS 2016: 2991-2999 - [i19]Xiang Cheng, Farbod Roosta-Khorasani, Peter L. Bartlett, Michael W. Mahoney:
FLAG: Fast Linearly-Coupled Adaptive Gradient Method. CoRR abs/1605.08108 (2016) - [i18]Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon, Alan Malek:
Hit-and-Run for Sampling and Planning in Non-Convex Spaces. CoRR abs/1610.08865 (2016) - [i17]Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, Pieter Abbeel:
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning. CoRR abs/1611.02779 (2016) - 2015
- [c82]Peter L. Bartlett, Wouter M. Koolen, Alan Malek, Eiji Takimoto, Manfred K. Warmuth:
Minimax Fixed-Design Linear Regression. COLT 2015: 226-239 - [c81]Yasin Abbasi-Yadkori, Peter L. Bartlett, Xi Chen, Alan Malek:
Large-Scale Markov Decision Problems with KL Control Cost and its Application to Crowdsourcing. ICML 2015: 1053-1062 - [c80]Wouter M. Koolen, Alan Malek, Peter L. Bartlett, Yasin Abbasi-Yadkori:
Minimax Time Series Prediction. NIPS 2015: 2557-2565 - [c79]Walid Krichene, Alexandre M. Bayen, Peter L. Bartlett:
Accelerated Mirror Descent in Continuous and Discrete Time. NIPS 2015: 2845-2853 - 2014
- [c78]Yevgeny Seldin, Peter L. Bartlett, Koby Crammer, Yasin Abbasi-Yadkori:
Prediction with Limited Advice and Multiarmed Bandits with Paid Observations. ICML 2014: 280-287 - [c77]Yasin Abbasi-Yadkori, Peter L. Bartlett, Varun Kanade:
Tracking Adversarial Targets. ICML 2014: 369-377 - [c76]Alan Malek, Yasin Abbasi-Yadkori, Peter L. Bartlett:
Linear Programming for Large-Scale Markov Decision Problems. ICML 2014: 496-504 - [c75]Wouter M. Koolen, Alan Malek, Peter L. Bartlett:
Efficient Minimax Strategies for Square Loss Games. NIPS 2014: 3230-3238 - [c74]Alex Kantchelian, Michael Carl Tschantz, Ling Huang, Peter L. Bartlett, Anthony D. Joseph, J. Doug Tygar:
Large-Margin Convex Polytope Machine. NIPS 2014: 3248-3256 - [i16]J. Hyam Rubinstein, Benjamin I. P. Rubinstein, Peter L. Bartlett:
Bounding Embeddings of VC Classes into Maximum Classes. CoRR abs/1401.7388 (2014) - [i15]Yasin Abbasi-Yadkori, Peter L. Bartlett, Alan Malek:
Linear Programming for Large-Scale Markov Decision Problems. CoRR abs/1402.6763 (2014) - 2013
- [c73]Peter L. Bartlett, Peter Grünwald, Peter Harremoës, Fares Hedayati, Wojciech Kotlowski:
Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families. COLT 2013: 639-661 - [c72]Yevgeny Seldin, Koby Crammer, Peter L. Bartlett:
Open Problem: Adversarial Multiarmed Bandits with Limited Advice. COLT 2013: 1067-1072 - [c71]Jacob D. Abernethy, Peter L. Bartlett, Rafael M. Frongillo, Andre Wibisono:
How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal. NIPS 2013: 2346-2354 - [c70]Yasin Abbasi-Yadkori, Peter L. Bartlett, Varun Kanade, Yevgeny Seldin, Csaba Szepesvári:
Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions. NIPS 2013: 2508-2516 - [i14]Yasin Abbasi-Yadkori, Peter L. Bartlett, Csaba Szepesvári:
Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions. CoRR abs/1303.3055 (2013) - [i13]Yevgeny Seldin, Peter L. Bartlett, Koby Crammer:
Advice-Efficient Prediction with Expert Advice. CoRR abs/1304.3708 (2013) - [i12]Peter L. Bartlett, Peter Grunwald, Peter Harremoës, Fares Hedayati, Wojciech Kotlowski:
Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families. CoRR abs/1305.4324 (2013) - 2012
- [j47]Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang, Nina Taft:
Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning. J. Priv. Confidentiality 4(1) (2012) - [j46]John C. Duchi, Peter L. Bartlett, Martin J. Wainwright:
Randomized Smoothing for Stochastic Optimization. SIAM J. Optim. 22(2): 674-701 (2012) - [j45]Adam Barth, Benjamin I. P. Rubinstein, Mukund Sundararajan, John C. Mitchell, Dawn Song, Peter L. Bartlett:
A Learning-Based Approach to Reactive Security. IEEE Trans. Dependable Secur. Comput. 9(4): 482-493 (2012) - [j44]Alekh Agarwal, Peter L. Bartlett, Pradeep Ravikumar, Martin J. Wainwright:
Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization. IEEE Trans. Inf. Theory 58(5): 3235-3249 (2012) - [c69]John C. Duchi, Peter L. Bartlett, Martin J. Wainwright:
Randomized smoothing for (parallel) stochastic optimization. CDC 2012: 5442-5444 - [c68]Fares Hedayati, Peter L. Bartlett:
The Optimality of Jeffreys Prior for Online Density Estimation and the Asymptotic Normality of Maximum Likelihood Estimators. COLT 2012: 7.1-7.13 - [c67]Fares Hedayati, Peter L. Bartlett:
Exchangeability Characterizes Optimality of Sequential Normalized Maximum Likelihood and Bayesian Prediction with Jeffreys Prior. AISTATS 2012: 504-510 - [e3]Peter L. Bartlett, Fernando C. N. Pereira, Christopher J. C. Burges, Léon Bottou, Kilian Q. Weinberger:
Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States. 2012 [contents] - [i11]Peter L. Bartlett, Ambuj Tewari:
REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs. CoRR abs/1205.2661 (2012) - [i10]Alekh Agarwal, Peter L. Bartlett, John C. Duchi:
Oracle inequalities for computationally adaptive model selection. CoRR abs/1208.0129 (2012) - 2011
- [c66]Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett:
Learning with Missing Features. UAI 2011: 635-642 - [c65]Jacob D. Abernethy, Peter L. Bartlett, Elad Hazan:
Blackwell Approachability and No-Regret Learning are Equivalent. COLT 2011: 27-46 - [c64]Alekh Agarwal, John C. Duchi, Peter L. Bartlett, Clément Levrard:
Oracle inequalities for computationally budgeted model selection. COLT 2011: 69-86 - [e2]John Shawe-Taylor, Richard S. Zemel, Peter L. Bartlett, Fernando C. N. Pereira, Kilian Q. Weinberger:
Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain. 2011 [contents] - [i9]Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett:
Online and Batch Learning Algorithms for Data with Missing Features. CoRR abs/1104.0729 (2011) - [i8]Peter L. Bartlett, Jonathan Baxter:
Infinite-Horizon Policy-Gradient Estimation. CoRR abs/1106.0665 (2011) - [i7]Peter L. Bartlett, Jonathan Baxter, Lex Weaver:
Experiments with Infinite-Horizon, Policy-Gradient Estimation. CoRR abs/1106.0666 (2011) - 2010
- [j43]Peter L. Bartlett:
Learning to act in uncertain environments: technical perspective. Commun. ACM 53(5): 98 (2010) - [j42]Benjamin I. P. Rubinstein, Peter L. Bartlett, J. Hyam Rubinstein:
Corrigendum to "Shifting: One-inclusion mistake bounds and sample compression" [J. Comput. System Sci 75 (1) (2009) 37-59]. J. Comput. Syst. Sci. 76(3-4): 278-280 (2010) - [c63]Peter L. Bartlett:
Optimal Online Prediction in Adversarial Environments. ALT 2010: 34 - [c62]Jacob D. Abernethy, Peter L. Bartlett, Niv Buchbinder, Isabelle Stanton:
A Regularization Approach to Metrical Task Systems. ALT 2010: 270-284 - [c61]Peter L. Bartlett:
Optimal Online Prediction in Adversarial Environments. Discovery Science 2010: 371 - [c60]Adam Barth, Benjamin I. P. Rubinstein, Mukund Sundararajan, John C. Mitchell, Dawn Song, Peter L. Bartlett:
A Learning-Based Approach to Reactive Security. Financial Cryptography 2010: 192-206 - [c59]Brian Kulis, Peter L. Bartlett:
Implicit Online Learning. ICML 2010: 575-582 - [c58]Marius Kloft, Ulrich Rückert, Peter L. Bartlett:
A Unifying View of Multiple Kernel Learning. ECML/PKDD (2) 2010: 66-81 - [c57]Alekh Agarwal, Peter L. Bartlett, Max Dama:
Optimal Allocation Strategies for the Dark Pool Problem. AISTATS 2010: 9-16 - [i6]Marius Kloft, Ulrich Rückert, Peter L. Bartlett:
A Unifying View of Multiple Kernel Learning. CoRR abs/1005.0437 (2010) - [i5]Alekh Agarwal, Peter L. Bartlett, Pradeep Ravikumar, Martin J. Wainwright:
Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization. CoRR abs/1009.0571 (2010) - [i4]Jacob D. Abernethy, Peter L. Bartlett, Elad Hazan:
Blackwell Approachability and Low-Regret Learning are Equivalent. CoRR abs/1011.1936 (2010)
2000 – 2009
- 2009
- [j41]Benjamin I. P. Rubinstein, Peter L. Bartlett, J. Hyam Rubinstein:
Shifting: One-inclusion mistake bounds and sample compression. J. Comput. Syst. Sci. 75(1): 37-59 (2009) - [j40]David S. Rosenberg, Vikas Sindhwani, Peter L. Bartlett, Partha Niyogi:
Multiview point cloud kernels for semisupervised learning [Lecture Notes]. IEEE Signal Process. Mag. 26(5): 145-150 (2009) - [c56]Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin:
A Stochastic View of Optimal Regret through Minimax Duality. COLT 2009 - [c55]Alekh Agarwal, Peter L. Bartlett, Pradeep Ravikumar, Martin J. Wainwright:
Information-theoretic lower bounds on the oracle complexity of convex optimization. NIPS 2009: 1-9 - [c54]Peter L. Bartlett, Ambuj Tewari:
REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs. UAI 2009: 35-42 - [i3]Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin:
A Stochastic View of Optimal Regret through Minimax Duality. CoRR abs/0903.5328 (2009) - [i2]Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang, Nina Taft:
Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning. CoRR abs/0911.5708 (2009) - [i1]Adam Barth, Benjamin I. P. Rubinstein, Mukund Sundararajan, John C. Mitchell, Dawn Xiaodong Song, Peter L. Bartlett:
A Learning-Based Approach to Reactive Security. CoRR abs/0912.1155 (2009) - 2008
- [j39]Michael Collins, Amir Globerson, Terry Koo, Xavier Carreras, Peter L. Bartlett:
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks. J. Mach. Learn. Res. 9: 1775-1822 (2008) - [j38]Peter L. Bartlett, Marten H. Wegkamp:
Classification with a Reject Option using a Hinge Loss. J. Mach. Learn. Res. 9: 1823-1840 (2008) - [j37]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson:
Correction to "The Importance of Convexity in Learning With Squared Loss". IEEE Trans. Inf. Theory 54(9): 4395 (2008) - [c53]Marco Barreno, Peter L. Bartlett, Fuching Jack Chi, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, Udam Saini, J. Doug Tygar:
Open problems in the security of learning. AISec 2008: 19-26 - [c52]Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, Sham M. Kakade, Alexander Rakhlin, Ambuj Tewari:
High-Probability Regret Bounds for Bandit Online Linear Optimization. COLT 2008: 335-342 - [c51]Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin, Ambuj Tewari:
Optimal Stragies and Minimax Lower Bounds for Online Convex Games. COLT 2008: 415-424 - 2007
- [j36]Peter L. Bartlett, Ambuj Tewari:
Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results. J. Mach. Learn. Res. 8: 775-790 (2007) - [j35]Ambuj Tewari, Peter L. Bartlett:
On the Consistency of Multiclass Classification Methods. J. Mach. Learn. Res. 8: 1007-1025 (2007) - [j34]Peter L. Bartlett, Mikhail Traskin:
AdaBoost is Consistent. J. Mach. Learn. Res. 8: 2347-2368 (2007) - [c50]Ambuj Tewari, Peter L. Bartlett:
Bounded Parameter Markov Decision Processes with Average Reward Criterion. COLT 2007: 263-277 - [c49]Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin:
Multitask Learning with Expert Advice. COLT 2007: 484-498 - [c48]Alexander Rakhlin, Jacob D. Abernethy, Peter L. Bartlett:
Online discovery of similarity mappings. ICML 2007: 767-774 - [c47]Peter L. Bartlett, Elad Hazan, Alexander Rakhlin:
Adaptive Online Gradient Descent. NIPS 2007: 65-72 - [c46]Ambuj Tewari, Peter L. Bartlett:
Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs. NIPS 2007: 1505-1512 - [c45]David S. Rosenberg, Peter L. Bartlett:
The Rademacher Complexity of Co-Regularized Kernel Classes. AISTATS 2007: 396-403 - 2006
- [c44]Peter L. Bartlett, Ambuj Tewari:
Sample Complexity of Policy Search with Known Dynamics. NIPS 2006: 97-104 - [c43]Peter L. Bartlett, Mikhail Traskin:
AdaBoost is Consistent. NIPS 2006: 105-112 - [c42]Benjamin I. P. Rubinstein, Peter L. Bartlett, J. Hyam Rubinstein:
Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds. NIPS 2006: 1193-1200 - 2005
- [c41]Ambuj Tewari, Peter L. Bartlett:
On the Consistency of Multiclass Classification Methods. COLT 2005: 143-157 - 2004
- [j33]Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan:
Learning the Kernel Matrix with Semidefinite Programming. J. Mach. Learn. Res. 5: 27-72 (2004) - [j32]Evan Greensmith, Peter L. Bartlett, Jonathan Baxter:
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. J. Mach. Learn. Res. 5: 1471-1530 (2004) - [c40]Peter L. Bartlett, Shahar Mendelson, Petra Philips:
Local Complexities for Empirical Risk Minimization. COLT 2004: 270-284 - [c39]Peter L. Bartlett, Ambuj Tewari:
Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results. COLT 2004: 564-578 - [c38]Peter L. Bartlett, Michael Collins, Benjamin Taskar, David A. McAllester:
Exponentiated Gradient Algorithms for Large-margin Structured Classification. NIPS 2004: 113-120 - 2003
- [c37]Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe:
Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates. NIPS 2003: 1173-1180 - 2002
- [b2]Martin Anthony, Peter L. Bartlett:
Neural Network Learning - Theoretical Foundations. Cambridge University Press 2002, ISBN 978-0-521-57353-5, pp. I-XIV, 1-389 - [j31]Peter L. Bartlett, Paul Fischer, Klaus-Uwe Höffgen:
Exploiting Random Walks for Learning. Inf. Comput. 176(2): 121-135 (2002) - [j30]Peter L. Bartlett, Jonathan Baxter:
Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. J. Comput. Syst. Sci. 64(1): 133-150 (2002) - [j29]Llew Mason, Peter L. Bartlett, Mostefa Golea:
Generalization Error of Combined Classifiers. J. Comput. Syst. Sci. 65(2): 415-438 (2002) - [j28]Peter L. Bartlett, Shahar Mendelson:
Rademacher and Gaussian Complexities: Risk Bounds and Structural Results. J. Mach. Learn. Res. 3: 463-482 (2002) - [j27]Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi:
Model Selection and Error Estimation. Mach. Learn. 48(1-3): 85-113 (2002) - [j26]Peter L. Bartlett, Shai Ben-David:
Hardness results for neural network approximation problems. Theor. Comput. Sci. 284(1): 53-66 (2002) - [j25]Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson:
Covering numbers for support vector machines. IEEE Trans. Inf. Theory 48(1): 239-250 (2002) - [c36]Peter L. Bartlett, Olivier Bousquet, Shahar Mendelson:
Localized Rademacher Complexities. COLT 2002: 44-58 - [c35]Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan:
Learning the Kernel Matrix with Semi-Definite Programming. ICML 2002: 323-330 - [c34]Peter L. Bartlett:
An Introduction to Reinforcement Learning Theory: Value Function Methods. Machine Learning Summer School 2002: 184-202 - 2001
- [j24]Jonathan Baxter, Peter L. Bartlett:
Infinite-Horizon Policy-Gradient Estimation. J. Artif. Intell. Res. 15: 319-350 (2001) - [j23]Jonathan Baxter, Peter L. Bartlett, Lex Weaver:
Experiments with Infinite-Horizon, Policy-Gradient Estimation. J. Artif. Intell. Res. 15: 351-381 (2001) - [c33]Peter L. Bartlett, Shahar Mendelson:
Rademacher and Gaussian Complexities: Risk Bounds and Structural Results. COLT/EuroCOLT 2001: 224-240 - [c32]Evan Greensmith, Peter L. Bartlett, Jonathan Baxter:
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. NIPS 2001: 1507-1514 - 2000
- [j22]Leonardo C. Kammer, Robert R. Bitmead, Peter L. Bartlett:
Direct iterative tuning via spectral analysis. Autom. 36(9): 1301-1307 (2000) - [j21]Martin Anthony, Peter L. Bartlett:
Function Learning From Interpolation. Comb. Probab. Comput. 9(3): 213-225 (2000) - [j20]Sri Parameswaran, Matthew F. Parkinson, Peter L. Bartlett:
Profiling in the ASP codesign environment. J. Syst. Archit. 46(14): 1263-1274 (2000) - [j19]Llew Mason, Peter L. Bartlett, Jonathan Baxter:
Improved Generalization Through Explicit Optimization of Margins. Mach. Learn. 38(3): 243-255 (2000) - [j18]Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni:
Learning Changing Concepts by Exploiting the Structure of Change. Mach. Learn. 41(2): 153-174 (2000) - [j17]Bernhard Schölkopf, Alexander J. Smola, Robert C. Williamson, Peter L. Bartlett:
New Support Vector Algorithms. Neural Comput. 12(5): 1207-1245 (2000) - [c31]Peter L. Bartlett, Jonathan Baxter:
Stochastic optimization of controlled partially observable Markov decision processes. CDC 2000: 124-129 - [c30]Peter L. Bartlett, Jonathan Baxter:
Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. COLT 2000: 133-141 - [c29]Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi:
Model Selection and Error Estimation. COLT 2000: 286-297 - [c28]Jonathan Baxter, Peter L. Bartlett:
Reinforcement Learning in POMDP's via Direct Gradient Ascent. ICML 2000: 41-48 - [c27]Jonathan Baxter, Peter L. Bartlett:
Direct gradient-based reinforcement learning. ISCAS 2000: 271-274 - [c26]Alexander J. Smola, Peter L. Bartlett:
Sparse Greedy Gaussian Process Regression. NIPS 2000: 619-625
1990 – 1999
- 1999
- [c25]Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson:
Covering Numbers for Support Vector Machines. COLT 1999: 267-277 - [c24]Peter L. Bartlett, Shai Ben-David:
Hardness Results for Neural Network Approximation Problems. EuroCOLT 1999: 50-62 - [c23]Llew Mason, Jonathan Baxter, Peter L. Bartlett, Marcus R. Frean:
Boosting Algorithms as Gradient Descent. NIPS 1999: 512-518 - 1998
- [j16]Leonardo C. Kammer, Robert R. Bitmead, Peter L. Bartlett:
Optimal controller properties from closed-loop experiments. Autom. 34(1): 83-91 (1998) - [j15]Peter L. Bartlett, Philip M. Long:
Prediction, Learning, Uniform Convergence, and Scale-Sensitive Dimensions. J. Comput. Syst. Sci. 56(2): 174-190 (1998) - [j14]Peter L. Bartlett, Vitaly Maiorov, Ron Meir:
Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks. Neural Comput. 10(8): 2159-2173 (1998) - [j13]Peter L. Bartlett:
The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network. IEEE Trans. Inf. Theory 44(2): 525-536 (1998) - [j12]Peter L. Bartlett, Tamás Linder, Gábor Lugosi:
The Minimax Distortion Redundancy in Empirical Quantizer Design. IEEE Trans. Inf. Theory 44(5): 1802-1813 (1998) - [j11]John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony:
Structural Risk Minimization Over Data-Dependent Hierarchies. IEEE Trans. Inf. Theory 44(5): 1926-1940 (1998) - [j10]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson:
The Importance of Convexity in Learning with Squared Loss. IEEE Trans. Inf. Theory 44(5): 1974-1980 (1998) - [c22]Peter L. Bartlett, Vitaly Maiorov, Ron Meir:
Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks. NIPS 1998: 190-196 - [c21]Llew Mason, Peter L. Bartlett, Jonathan Baxter:
Direct Optimization of Margins Improves Generalization in Combined Classifiers. NIPS 1998: 288-294 - [c20]Bernhard Schölkopf, Peter L. Bartlett, Alexander J. Smola, Robert C. Williamson:
Shrinking the Tube: A New Support Vector Regression Algorithm. NIPS 1998: 330-336 - [e1]Peter L. Bartlett, Yishay Mansour:
Proceedings of the Eleventh Annual Conference on Computational Learning Theory, COLT 1998, Madison, Wisconsin, USA, July 24-26, 1998. ACM 1998, ISBN 1-58113-057-0 [contents] - 1997
- [j9]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson:
Correction to 'Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes'. Neural Comput. 9(4): 765-769 (1997) - [j8]Peter L. Bartlett, Sanjeev R. Kulkarni, S. E. Posner:
Covering numbers for real-valued function classes. IEEE Trans. Inf. Theory 43(5): 1721-1724 (1997) - [c19]Peter L. Bartlett, Tamás Linder, Gábor Lugosi:
A Minimax Lower Bound for Empirical Quantizer Design. EuroCOLT 1997: 210-222 - [c18]Jonathan Baxter, Peter L. Bartlett:
A Result Relating Convex n-Widths to Covering Numbers with some Applications to Neural Networks. EuroCOLT 1997: 251-259 - [c17]Jonathan Baxter, Peter L. Bartlett:
The Canonical Distortion Measure in Feature Space and 1-NN Classification. NIPS 1997: 245-251 - [c16]Mostefa Golea, Peter L. Bartlett, Wee Sun Lee, Llew Mason:
Generalization in Decision Trees and DNF: Does Size Matter? NIPS 1997: 259-265 - 1996
- [j7]Martin Anthony, Peter L. Bartlett, Yuval Ishai, John Shawe-Taylor:
Valid Generalisation from Approximate Interpolation. Comb. Probab. Comput. 5: 191-214 (1996) - [j6]Peter L. Bartlett, Philip M. Long, Robert C. Williamson:
Fat-Shattering and the Learnability of Real-Valued Functions. J. Comput. Syst. Sci. 52(3): 434-452 (1996) - [j5]Peter L. Bartlett, Robert C. Williamson:
The VC Dimension and Pseudodimension of Two-Layer Neural Networks with Discrete Inputs. Neural Comput. 8(3): 625-628 (1996) - [j4]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson:
Efficient agnostic learning of neural networks with bounded fan-in. IEEE Trans. Inf. Theory 42(6): 2118-2132 (1996) - [c15]John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony:
A Framework for Structural Risk Minimisation. COLT 1996: 68-76 - [c14]Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni:
Learning Changing Concepts by Exploiting the Structure of Change. COLT 1996: 131-139 - [c13]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson:
The Importance of Convexity in Learning with Squared Loss. COLT 1996: 140-146 - [c12]Peter L. Bartlett:
For Valid Generalization the Size of the Weights is More Important than the Size of the Network. NIPS 1996: 134-140 - 1995
- [j3]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson:
Lower Bounds on the VC Dimension of Smoothly Parameterized Function Classes. Neural Comput. 7(5): 1040-1053 (1995) - [c11]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson:
On Efficient Agnostic Learning of Linear Combinations of Basis Functions. COLT 1995: 369-376 - [c10]Peter L. Bartlett, Philip M. Long:
More Theorems about Scale-sensitive Dimensions and Learning. COLT 1995: 392-401 - [c9]Martin Anthony, Peter L. Bartlett:
Function learning from interpolation. EuroCOLT 1995: 211-221 - [c8]Adam Kowalczyk, Jacek Szymanski, Peter L. Bartlett, Robert C. Williamson:
Examples of learning curves from a modified VC-formalism. NIPS 1995: 344-350 - 1994
- [c7]Peter L. Bartlett, Philip M. Long, Robert C. Williamson:
Fat-Shattering and the Learnability of Real-Valued Functions. COLT 1994: 299-310 - [c6]Peter L. Bartlett, Paul Fischer, Klaus-Uwe Höffgen:
Exploiting Random Walks for Learning. COLT 1994: 318-327 - [c5]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson:
Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes. COLT 1994: 362-367 - 1993
- [j2]Peter L. Bartlett:
Vapnik-Chervonenkis Dimension Bounds for Two- and Three-Layer Networks. Neural Comput. 5(3): 371-373 (1993) - [c4]Peter L. Bartlett:
Lower Bounds on the Vapnik-Chervonenkis Dimension of Multi-Layer Threshold Networks. COLT 1993: 144-150 - 1992
- [b1]Peter L. Bartlett:
Computational learning theory and neural network learning. University of Queensland, Australia, 1992 - [j1]Peter L. Bartlett, Tom Downs:
Using random weights to train multilayer networks of hard-limiting units. IEEE Trans. Neural Networks 3(2): 202-210 (1992) - [c3]Peter L. Bartlett:
Learning With a Slowly Changing Distribution. COLT 1992: 243-252 - 1991
- [c2]Peter L. Bartlett, Robert C. Williamson:
Investigating the Distribution Assumptions in the Pac Learning Model. COLT 1991: 24-32 - [c1]Robert C. Williamson, Peter L. Bartlett:
Splines, Rational Functions and Neural Networks. NIPS 1991: 1040-1047
Coauthor Index
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