Peter L. Bartlett

发表

Best of many worlds: Robust model selection for online supervised learning pdf

Anant Sahai, Vidya Muthukumar, Mitas Ray, 2018, AISTATS.

Bounded Parameter Markov Decision Processes with Average Reward Criterion

Ambuj Tewari, Peter L. Bartlett, P. Bartlett, 2007, COLT.

Corrigendum to "Shifting: One-inclusion mistake bounds and sample compression" [J. Comput. System Sci 75 (1) (2009) 37-59]

Peter L. Bartlett, Benjamin I. P. Rubinstein, J. Hyam Rubinstein, 2010, J. Comput. Syst. Sci..

Large-Scale Markov Decision Problems with KL Control Cost and its Application to Crowdsourcing

Peter L. Bartlett, Yasin Abbasi-Yadkori, Xi Chen, 2015, ICML.

Computational learning theory and neural network learning

Peter L. Bartlett, P. Bartlett, 1992 .

Neural Network Learning: Other Learning Problems

Peter L. Bartlett, Martin Anthony, P. Bartlett, 1999 .

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

John Shawe-Taylor, Kilian Q. Weinberger, Peter L. Bartlett, 2011, NIPS.

Large-Scale Markov Decision Problems via the Linear Programming Dual pdf

Xi Chen, Peter L. Bartlett, Yasin Abbasi-Yadkori, 2019, ArXiv.

Classification with a Reject Option using a Hinge Loss

Peter L. Bartlett, Marten H. Wegkamp, P. Bartlett, 2008, J. Mach. Learn. Res..

Neural Network Learning: Vapnik-Chervonenkis Dimension Bounds for Neural Networks

Peter L. Bartlett, Martin Anthony, P. Bartlett, 1999 .

Accelerated Mirror Descent in Continuous and Discrete Time

Alexandre M. Bayen, Peter L. Bartlett, Walid Krichene, 2015, NIPS.

Efficient agnostic learning of neural networks with bounded fan-in

Peter L. Bartlett, Wee Sun Lee, Robert C. Williamson, 1996, IEEE Trans. Inf. Theory.

Exchangeability Characterizes Optimality of Sequential Normalized Maximum Likelihood and Bayesian Prediction

Peter L. Bartlett, Fares Hedayati, P. Bartlett, 2012, IEEE Transactions on Information Theory.

Quantitative Central Limit Theorems for Discrete Stochastic Processes pdf

Michael I. Jordan, Peter L. Bartlett, Xiang Cheng, 2019, ArXiv.

Bounds on Error Expectation for SVM

Bernhard Schölkopf, Alexander J. Smola, Dale Schuurmans, 2000 .

The Pattern Classification Problem

Peter L. Bartlett, Martin Anthony, P. Bartlett, 1999 .

Sparse Greedy Gaussian Process Regression

Alexander J. Smola, Peter L. Bartlett, P. Bartlett, 2000, NIPS.

Local Complexities for Empirical Risk Minimization

Peter L. Bartlett, Shahar Mendelson, Petra Philips, 2004, COLT.

Lower bounds on the Vapnik-Chervonenkis dimension of multi-layer threshold networks

Peter L. Bartlett, P. Bartlett, 1993, COLT '93.

The Rademacher Complexity of Co-Regularized Kernel Classes

Peter L. Bartlett, David S. Rosenberg, P. Bartlett, 2007, AISTATS.

Leave-One-Out Methods

Bernhard Schölkopf, Alexander J. Smola, Dale Schuurmans, 2000 .

Direct Controller Adjustment by On-Line Performance Assessment

Robert R. Bitmead, Peter L. Bartlett, Leonardo C. Kammer, 1998 .

AdaBoost is Consistent

Peter L. Bartlett, Mikhail Traskin, P. Bartlett, 2006, J. Mach. Learn. Res..

Fast-Tracking Stationary MOMDPs for Adaptive Management Problems

Peter L. Bartlett, Kai Helge Becker, Martin Péron, 2016, AAAI.

Generalization Error of Combined Classifiers

Peter L. Bartlett, Llew Mason, Mostefa Golea, 2002, J. Comput. Syst. Sci..

Bounding Embeddings of VC Classes into Maximum Classes pdf

Peter L. Bartlett, Benjamin I. P. Rubinstein, J. Hyam Rubinstein, 2014, ArXiv.

An Introduction to Reinforcement Learning Theory: Value Function Methods

Peter L. Bartlett, P. Bartlett, 2002, Machine Learning Summer School.

Optimal Mean Estimation without a Variance pdf

Michael I. Jordan, Peter L. Bartlett, Yeshwanth Cherapanamjeri, 2020, COLT.

Examples of learning curves from a modified VC-formalism

Peter L. Bartlett, Adam Kowalczyk, Robert C. Williamson, 1995, NIPS.

Fast Mean Estimation with Sub-Gaussian Rates pdf

Peter L. Bartlett, Yeshwanth Cherapanamjeri, Nicolas Flammarion, 2019, COLT.

Optimal Robust Linear Regression in Nearly Linear Time pdf

Michael I. Jordan, Peter L. Bartlett, Yeshwanth Cherapanamjeri, 2020, ArXiv.

On efficient agnostic learning of linear combinations of basis functions

Peter L. Bartlett, Wee Sun Lee, Robert C. Williamson, 1995, COLT '95.

The VC Dimension and Pseudodimension of Two-Layer Neural Networks with Discrete Inputs

Peter L. Bartlett, Robert C. Williamson, R. C. Williamson, 1996, Neural Computation.

Exploiting random walks for learning

Peter L. Bartlett, Klaus-Uwe Höffgen, Paul Fischer, 1994, COLT '94.

Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation

Michael I. Jordan, Peter L. Bartlett, Aldo Pacchiano, 2018, NeurIPS.

Tracking Adversarial Targets

Varun Kanade, Peter L. Bartlett, Yasin Abbasi-Yadkori, 2014, ICML.

Matrix regularization techniques for online multitask learning

Peter L. Bartlett, Alekh Agarwal, Alexander Rakhlin, 2008 .

More theorems about scale-sensitive dimensions and learning

Philip M. Long, Peter L. Bartlett, P. Bartlett, 1995, COLT '95.

On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo pdf

Michael I. Jordan, Peter L. Bartlett, Yi-An Ma, 2018, ICML.

Covering numbers for support vector machines

John Shawe-Taylor, Peter L. Bartlett, Ying Guo, 2002, IEEE Trans. Inf. Theory.

FLAG: Fast Linearly-Coupled Adaptive Gradient Method pdf

Peter L. Bartlett, Michael W. Mahoney, Farbod Roosta-Khorasani, 2016, ArXiv.

Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning

Peter L. Bartlett, Jonathan Baxter, Evan Greensmith, 2001, J. Mach. Learn. Res..

Linear Programming for Large-Scale Markov Decision Problems

Peter L. Bartlett, Yasin Abbasi-Yadkori, Alan Malek, 2014, ICML.

Chapter 14 - Learning Theory

Ambuj Tewari, Peter L. Bartlett, P. Bartlett, 2014 .

Optimal Online Prediction in Adversarial Environments

Peter L. Bartlett, P. Bartlett, 2010, Discovery Science.

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning pdf

Peter L. Bartlett, Pieter Abbeel, Xi Chen, 2016, ArXiv.

The Boolean Perceptron

Peter L. Bartlett, Martin Anthony, P. Bartlett, 1999 .

Regularization Networks and Support Vector Machines

Bernhard Schölkopf, Alexander J. Smola, Dale Schuurmans, 2000 .

Learning in Neural Networks: Theoretical Foundations

Peter L. Bartlett, Martin Anthony, P. Bartlett, 1999 .

Prediction with Limited Advice and Multiarmed Bandits with Paid Observations

Koby Crammer, Peter L. Bartlett, Yasin Abbasi-Yadkori, 2014, ICML.

Boosting Algorithms as Gradient Descent

Peter L. Bartlett, Marcus R. Frean, Llew Mason, 1999, NIPS.

Proceedings of the Nineteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2016

Peter L. Bartlett, Alessandro Lazaric, Ronald Ortner, 2016 .

Open problems in the security of learning

Peter L. Bartlett, Blaine Nelson, Benjamin I. P. Rubinstein, 2008, AISec '08.

Adaptive Averaging in Accelerated Descent Dynamics

Alexandre M. Bayen, Peter L. Bartlett, Walid Krichene, 2016, NIPS.

Sampling for Bayesian Mixture Models: MCMC with Polynomial-Time Mixing pdf

Martin J. Wainwright, Michael I. Jordan, Peter L. Bartlett, 2019, ArXiv.

Improved Learning Complexity in Combinatorial Pure Exploration Bandits

Peter L. Bartlett, Alessandro Lazaric, Ronald Ortner, 2016, AISTATS.

Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks

Peter L. Bartlett, Ron Meir, Vitaly Maiorov, 1998, Neural Computation.

Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds

Peter L. Bartlett, Benjamin I. P. Rubinstein, J. Hyam Rubinstein, 2006, NIPS.

Covering numbers for real-valued function classes

Sanjeev R. Kulkarni, Peter L. Bartlett, S. E. Posner, 1997, IEEE Trans. Inf. Theory.

Oracle lower bounds for stochastic gradient sampling algorithms pdf

Philip M. Long, Peter L. Bartlett, Niladri S. Chatterji, 2020, Bernoulli.

Learning with Missing Features

Peter L. Bartlett, Afshin Rostamizadeh, Alekh Agarwal, 2011, UAI.

Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs

Ambuj Tewari, Peter L. Bartlett, P. Bartlett, 2007, NIPS.

Neural Network Learning: Preface

Peter L. Bartlett, Martin Anthony, P. Bartlett, 1999 .

Failures of model-dependent generalization bounds for least-norm interpolation pdf

Philip M. Long, Peter L. Bartlett, P. Bartlett, 2020, J. Mach. Learn. Res..

High-Probability Regret Bounds for Bandit Online Linear Optimization

Thomas P. Hayes, Ambuj Tewari, Peter L. Bartlett, 2008, COLT.

The minimax distortion redundancy in empirical quantizer design

Tamás Linder, Peter L. Bartlett, Gábor Lugosi, 1997, Proceedings of IEEE International Symposium on Information Theory.

When does gradient descent with logistic loss find interpolating two-layer networks? pdf

Philip M. Long, Peter L. Bartlett, Niladri S. Chatterji, 2020, J. Mach. Learn. Res..

Implicit Online Learning

Peter L. Bartlett, Brian Kulis, P. Bartlett, 2010, ICML.

Learning Changing Concepts by Exploiting the Structure of Change

Sanjeev R. Kulkarni, Peter L. Bartlett, Shai Ben-David, 1996, COLT.

Neural Network Learning: Bounding the VC-Dimension using Geometric Techniques

Peter L. Bartlett, Martin Anthony, 1999 .

REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs

Ambuj Tewari, Peter L. Bartlett, P. Bartlett, 2009, UAI.

Hebbian Synaptic Modifications in Spiking Neurons that Learn pdf

Peter L. Bartlett, Jonathan Baxter, P. Bartlett, 2019, ArXiv.

How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal

Andre Wibisono, Peter L. Bartlett, Jacob D. Abernethy, 2013, NIPS.

On Approximate Thompson Sampling with Langevin Algorithms

Michael I. Jordan, Peter L. Bartlett, Yi-An Ma, 2020, ICML.

Learning Changing Concepts by Exploiting the Structure of Change

Shai Ben-David, Sanjeev R. Kulkarni, Peter L. Bartlett, 1996, COLT '96.

Prediction, Learning, Uniform Convergence, and Scale-Sensitive Dimensions

Philip M. Long, Peter L. Bartlett, P. Bartlett, 1998, J. Comput. Syst. Sci..

Testing Markov Chains without Hitting pdf

Peter L. Bartlett, Yeshwanth Cherapanamjeri, P. Bartlett, 2019, ArXiv.

Optimal Allocation Strategies for the Dark Pool Problem

Peter L. Bartlett, Alekh Agarwal, Max Dama, 2010, AISTATS.

Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning pdf

Ling Huang, Peter L. Bartlett, Benjamin I. P. Rubinstein, 2009, J. Priv. Confidentiality.

Benign overfitting in linear regression

Philip M. Long, Peter L. Bartlett, Alexander Tsigler, 2019, Proceedings of the National Academy of Sciences.

Online discovery of similarity mappings

Peter L. Bartlett, Jacob D. Abernethy, Alexander Rakhlin, 2007, ICML '07.

Covering numbers for support vector machines

John Shawe-Taylor, Peter L. Bartlett, Ying Guo, 1999, COLT '99.

Vapnik-Chervonenkis dimension of neural nets

Peter L. Bartlett, Wolfgang Maass, P. Bartlett, 2003 .

The Canonical Distortion Measure in Feature Space and 1-NN Classification

Peter L. Bartlett, Jonathan Baxter, P. Bartlett, 1997, NIPS.

Oracle inequalities for computationally adaptive model selection pdf

Peter L. Bartlett, John C. Duchi, Alekh Agarwal, 2012, ArXiv.

The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network

Peter L. Bartlett, P. Bartlett, 1998, IEEE Trans. Inf. Theory.

Vapnik-Chervonenkis Dimension Bounds for Two- and Three-Layer Networks

Peter L. Bartlett, P. Bartlett, 1993, Neural Computation.

Testing Symmetric Markov Chains Without Hitting

Peter L. Bartlett, Yeshwanth Cherapanamjeri, P. Bartlett, 2019, COLT.

Greedy Convex Ensemble

Peter L. Bartlett, Nan Ye, Thanh Tan Nguyen, 2020, IJCAI.

A Stochastic View of Optimal Regret through Minimax Duality pdf

Peter L. Bartlett, Alekh Agarwal, Jacob D. Abernethy, 2009, COLT.

Optimal Stragies and Minimax Lower Bounds for Online Convex Games

Ambuj Tewari, Peter L. Bartlett, Jacob D. Abernethy, 2008, COLT.

Multitask Learning with Expert Advice

Peter L. Bartlett, Jacob D. Abernethy, Alexander Rakhlin, 2007, COLT.

Alternating minimization for dictionary learning with random initialization

Peter L. Bartlett, Niladri S. Chatterji, P. Bartlett, 2017, NIPS.

The Importance of Convexity in Learning with Squared Loss

Peter L. Bartlett, Wee Sun Lee, Robert C. Williamson, 1998, IEEE Trans. Inf. Theory.

Learning with a slowly changing distribution

Peter L. Bartlett, P. Bartlett, 1992, COLT '92.

Shrinking the Tube: A New Support Vector Regression Algorithm

Bernhard Schölkopf, Alexander J. Smola, Peter L. Bartlett, 1998, NIPS.

Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem

Peter L. Bartlett, Yasin Abbasi-Yadkori, Victor Gabillon, 2017, NIPS.

Learning Near-optimal Convex Combinations of Basis Models with Generalization Guarantees pdf

Peter L. Bartlett, Nan Ye, Tan Nguyen, 2019, ArXiv.

Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems

Martin J. Wainwright, Dhruv Malik, Peter L. Bartlett, 2018, AISTATS.

Randomized smoothing for (parallel) stochastic optimization

Martin J. Wainwright, Peter L. Bartlett, John C. Duchi, 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

OSOM: A Simultaneously Optimal Algorithm for Multi-Armed and Linear Contextual Bandits

Peter L. Bartlett, Niladri S. Chatterji, Vidya Muthukumar, 2019, AISTATS.

Neural Network Learning: Contents

Peter L. Bartlett, Martin Anthony, 1999 .

Efficient Minimax Strategies for Square Loss Games

Wouter M. Koolen, Peter L. Bartlett, Alan Malek, 2014, NIPS.

Correction to Lower Bounds on VC-Dimension of Smoothly Parameterized Function Classes1

Peter L. Bartlett, Wee Sun Lee, Robert C. Williamson, 1997, Neural Computation.

Best of both worlds: Stochastic & adversarial best-arm identification

Peter L. Bartlett, Michal Valko, Yasin Abbasi-Yadkori, 2018, COLT.

Two Approximate Dynamic Programming Algorithms for Managing Complete SIS Networks

Peter L. Bartlett, Kai Helge Becker, Martin Péron, 2018, COMPASS.

Valid Generalisation from Approximate Interpolation

Yuval Ishai, John Shawe-Taylor, Peter L. Bartlett, 1996, Combinatorics, Probability and Computing.

High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm pdf

Martin J. Wainwright, Michael I. Jordan, Peter L. Bartlett, 2019, J. Mach. Learn. Res..

A Unifying View of Multiple Kernel Learning

Peter L. Bartlett, Marius Kloft, Ulrich Rückert, 2010, ECML/PKDD.

Error and variance bounds on sigmoidal neurons with weight and input errors

Peter L. Bartlett, T. Downs, David Lovell, 1992 .

Minimax Fixed-Design Linear Regression

Wouter M. Koolen, Peter L. Bartlett, Manfred K. Warmuth, 2015, COLT.

Shifting: One-inclusion mistake bounds and sample compression

Peter L. Bartlett, Benjamin I. P. Rubinstein, J. Hyam Rubinstein, 2009, J. Comput. Syst. Sci..

POLITEX: Regret Bounds for Policy Iteration using Expert Prediction

Peter L. Bartlett, Nevena Lazic, Csaba Szepesvári, 2019, ICML.

Model Selection and Error Estimation

Peter L. Bartlett, Gábor Lugosi, Stéphane Boucheron, 2000, Machine Learning.

Lower bounds on the VC-dimension of smoothly parametrized function classes

Peter L. Bartlett, Wee Sun Lee, Robert C. Williamson, 1994, COLT '94.

A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption pdf

Peter L. Bartlett, Michal Valko, Victor Gabillon, 2018, ALT.

Sample Complexity of Policy Search with Known Dynamics

Ambuj Tewari, Peter L. Bartlett, P. Bartlett, 2006, NIPS.

Experiments with Infinite-Horizon, Policy-Gradient Estimation

Peter L. Bartlett, Lex Weaver, Jonathan Baxter, 2001, J. Artif. Intell. Res..

Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning

Peter L. Bartlett, Jonathan Baxter, P. Bartlett, 2000, J. Comput. Syst. Sci..

A framework for structural risk minimisation

John Shawe-Taylor, Peter L. Bartlett, Martin Anthony, 1996, COLT '96.

Using random weights to train multilayer networks of hard-limiting units

Peter L. Bartlett, Tom Downs, 1992, IEEE Trans. Neural Networks.

FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods

Peter L. Bartlett, Michael W. Mahoney, Fred Roosta, 2016, AISTATS.

A Fast and Reliable Policy Improvement Algorithm

Stephen J. Wright, Peter L. Bartlett, Yasin Abbasi-Yadkori, 2016, AISTATS.

Minimax Time Series Prediction

Wouter M. Koolen, Peter L. Bartlett, Yasin Abbasi-Yadkori, 2015, NIPS.

Fat-shattering and the learnability of real-valued functions

Philip M. Long, Peter L. Bartlett, Robert C. Williamson, 1994, COLT '94.

A Learning-Based Approach to Reactive Security

Dawn Xiaodong Song, John C. Mitchell, Peter L. Bartlett, 2009, IEEE Transactions on Dependable and Secure Computing.

Application of machine learning in fault diagnostics of mechanical systems

Philip Haves, Peter L. Bartlett, David M. Auslander, 2008, WCE 2008.

Application of machine learning in the fault diagnostics of air handling units

David M. Auslander, Philip Haves, Michael D. Sohn, 2012 .

Splines, Rational Functions and Neural Networks

Peter L. Bartlett, Robert C. Williamson, R. C. Williamson, 1991, NIPS.

A Minimax Lower Bound for Empirical Quantizer Design

Tamás Linder, Peter L. Bartlett, Gábor Lugosi, 1997, EuroCOLT.

Representing smooth functions as compositions of near-identity functions with implications for deep network optimization pdf

Philip M. Long, Peter L. Bartlett, Steven N. Evans, 2018, ArXiv.

Investigating the distribution assumptions in the Pac learning model

Peter L. Bartlett, Robert C. Williamson, R. C. Williamson, 1991, COLT '91.

Functional Gradient Techniques for Combining Hypotheses

Peter L. Bartlett, Marcus Frean, Llew Mason, 2000 .

Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions

Peter L. Bartlett, Yasin Abbasi-Yadkori, Csaba Szepesvari, 2013, NIPS.

Hardness Results for Neural Network Approximation Problems

Shai Ben-David, Peter L. Bartlett, 1999, EuroCOLT.

Gradient Descent with Identity Initialization Efficiently Learns Positive-Definite Linear Transformations by Deep Residual Networks

Philip M. Long, Peter L. Bartlett, David P. Helmbold, 2018, Neural Computation.

Reinforcement Learning in POMDP's via Direct Gradient Ascent

Peter L. Bartlett, Jonathan Baxter, P. Bartlett, 2000, ICML.

Multiview point cloud kernels for semisupervised learning

Partha Niyogi, Peter L. Bartlett, Vikas Sindhwani, 2009 .

Constructive Learning Algorithms for Two-Layer Networks

Peter L. Bartlett, Martin Anthony, P. Bartlett, 1999 .

An Efficient Sampling Algorithm for Non-smooth Composite Potentials pdf

Martin J. Wainwright, Peter L. Bartlett, Nicolas Flammarion, 2019, J. Mach. Learn. Res..

Infinite-Horizon Policy-Gradient Estimation

Peter L. Bartlett, Jonathan Baxter, P. Bartlett, 2001, J. Artif. Intell. Res..

Sharp Convergence Rates for Langevin Dynamics in the Nonconvex Setting pdf

Michael I. Jordan, Peter L. Bartlett, Yasin Abbasi-Yadkori, 2018, ArXiv.

Large-Margin Convex Polytope Machine

Ling Huang, Michael Carl Tschantz, Peter L. Bartlett, 2014, NIPS.

Langevin Monte Carlo without Smoothness

Michael I. Jordan, Peter L. Bartlett, Jelena Diakonikolas, 2019, AISTATS.

On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration pdf

Martin J. Wainwright, Michael I. Jordan, Peter L. Bartlett, 2020, COLT.

Information-theoretic lower bounds on the oracle complexity of convex optimization

Martin J. Wainwright, Pradeep Ravikumar, Peter L. Bartlett, 2009, NIPS.

Beyond the Margin

Bernhard Schölkopf, Alexander J. Smola, Dale Schuurmans, 2000 .

On the Consistency of Multiclass Classification Methods

Ambuj Tewari, Peter L. Bartlett, P. Bartlett, 2007, J. Mach. Learn. Res..

Nearly-tight VC-dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks

Peter L. Bartlett, Abbas Mehrabian, Christopher Liaw, 2017, J. Mach. Learn. Res..

Generalization in Decision Trees and DNF: Does Size Matter?

Peter L. Bartlett, Wee Sun Lee, Llew Mason, 1997, NIPS.

Blackwell Approachability and No-Regret Learning are Equivalent pdf

Peter L. Bartlett, Elad Hazan, Jacob D. Abernethy, 2010, COLT.

A Result Relating Convex n-Widths to Covering Numbers with some Applications to Neural Networks

Peter L. Bartlett, Jonathan Baxter, P. Bartlett, 1997, EuroCOLT.

nostic Learnin ral

Peter L. Bartlett, Wee Sun Lee, Robert C. Williamson, 1996 .

Neural Network Learning: Learning Real-Valued Functions

Peter L. Bartlett, Martin Anthony, 1999 .

Structural Risk Minimization Over Data-Dependent Hierarchies

John Shawe-Taylor, Peter L. Bartlett, Martin Anthony, 1998, IEEE Trans. Inf. Theory.

Adaptive Online Gradient Descent

Peter L. Bartlett, Elad Hazan, Alexander Rakhlin, 2007, NIPS.

Neural Network Learning: The Dimensions of Neural Networks

Peter L. Bartlett, Martin Anthony, P. Bartlett, 1999 .

Learning to act in uncertain environments

Peter L. Bartlett, 2010, Commun. ACM.

Self-Distillation Amplifies Regularization in Hilbert Space pdf

Hossein Mobahi, Peter L. Bartlett, Mehrdad Farajtabar, 2020, NeurIPS.

Recovery Guarantees for One-hidden-layer Neural Networks

Inderjit S. Dhillon, Prateek Jain, Peter L. Bartlett, 2017, ICML.

Underdamped Langevin MCMC: A non-asymptotic analysis pdf

Michael I. Jordan, Peter L. Bartlett, Xiang Cheng, 2017, COLT.

Fault diagnostics and supervised testing : How fault diagnostic tools can be proactive?

David M. Auslander, Philip Haves, Peter L. Bartlett, 2008 .

Acceleration and Averaging in Stochastic Descent Dynamics

Peter L. Bartlett, Walid Krichene, P. Bartlett, 2017, NIPS.

Generalized Support Vector Machines

Bernhard Schölkopf, Alexander J. Smola, Dale Schuurmans, 2000 .

Gradient Diversity Empowers Distributed Learning: Convergence and Stability of Mini-batch SGD

Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett, 2017 .

Convergence of Langevin MCMC in KL-divergence

Peter L. Bartlett, Xiang Cheng, P. Bartlett, 2017, ALT.

Exploiting Random Walks for Learning

Peter L. Bartlett, Klaus-Uwe Höffgen, Paul Fischer, 2002, Inf. Comput..

Open Problem: Adversarial Multiarmed Bandits with Limited Advice

Koby Crammer, Peter L. Bartlett, Yevgeny Seldin, 2013, COLT.

Neural Network Learning: Efficient Learning

Peter L. Bartlett, Martin Anthony, P. Bartlett, 1999 .

Spectrally-normalized margin bounds for neural networks

Matus Telgarsky, Peter L. Bartlett, Dylan J. Foster, 2017, NIPS.

Proceedings of the Eleventh Annual Conference on Computational Learning Theory, COLT 1998, Madison, Wisconsin, USA, July 24-26, 1998

Yishay Mansour, Peter L. Bartlett, Y. Mansour, 1998, COLT.

Quantitative W1 Convergence of Langevin-Like Stochastic Processes with Non-Convex Potential State-Dependent Noise pdf

Michael I. Jordan, Peter L. Bartlett, Dong Yin, 2019, ArXiv.

Improved Generalization Through Explicit Optimization of Margins

Peter L. Bartlett, Llew Mason, Jonathan Baxter, 2000, Machine Learning.

Function Learning from Interpolation

Peter L. Bartlett, Martin Anthony, P. Bartlett, 1995, Combinatorics, Probability and Computing.

Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization

Martin J. Wainwright, Pradeep Ravikumar, Peter L. Bartlett, 2010, IEEE Transactions on Information Theory.

Advice-Efficient Prediction with Expert Advice pdf

Koby Crammer, Peter L. Bartlett, Yevgeny Seldin, 2013, ArXiv.

Bayesian Robustness: A Nonasymptotic Viewpoint pdf

Anca D. Dragan, Michael I. Jordan, Peter L. Bartlett, 2019, Journal of the American Statistical Association.

For Valid Generalization the Size of the Weights is More Important than the Size of the Network

Peter L. Bartlett, P. Bartlett, 1996, NIPS.

Profiling in the ASP codesign environment

Sri Parameswaran, Peter L. Bartlett, Matthew F. Parkinson, 2000, J. Syst. Archit..

Oracle inequalities for computationally budgeted model selection

Peter L. Bartlett, John C. Duchi, Alekh Agarwal, 2011, COLT.

Hit-and-Run for Sampling and Planning in Non-Convex Spaces pdf

Peter L. Bartlett, Yasin Abbasi-Yadkori, Alan Malek, 2016, AISTATS.

Learning the Kernel Matrix with Semidefinite Programming

Nello Cristianini, Michael I. Jordan, Peter L. Bartlett, 2002, J. Mach. Learn. Res..

Reinforcement learning in large or unknown mdps

Ambuj Tewari, Peter L. Bartlett, P. Bartlett, 2007 .

Direct Optimization of Margins Improves Generalization in Combined Classifiers

Peter L. Bartlett, Llew Mason, Jonathan Baxter, 1998, NIPS.

Large margin methods for structured classification : Exponentiated gradient algorithms and PAC-Bayesian generalization bounds

Ben Taskar, Peter L. Bartlett, Michael Collins, 2004 .

Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates

Michael I. Jordan, Peter L. Bartlett, Jon D. McAuliffe, 2003, NIPS.

GACV for Support Vector Machines

Bernhard Schölkopf, Alexander J. Smola, Dale Schuurmans, 2000 .

Direct iterative tuning via spectral analysis

Robert R. Bitmead, Leonardo C. Kammer, Peter L. Bartlett, 2000, Autom..

Exponentiated Gradient Algorithms for Large-margin Structured Classification

Ben Taskar, Peter L. Bartlett, Michael Collins, 2004, NIPS.

Horizon-Independent Minimax Linear Regression

Peter L. Bartlett, Alan Malek, 2018, NeurIPS.

Localized Rademacher Complexities

Peter L. Bartlett, Shahar Mendelson, Olivier Bousquet, 2002, COLT.

Fat-shattering and the learnability of real-valued functions

Philip M. Long, Peter L. Bartlett, Robert C. Williamson, 1994, COLT '94.

Boosting Algorithms as Gradient Descent in Function Space

Peter L. Bartlett, Marcus Frean, Llew Mason, 2007 .

Rademacher and Gaussian Complexities: Risk Bounds and Structural Results

Peter L. Bartlett, Shahar Mendelson, P. Bartlett, 2003, J. Mach. Learn. Res..

Neural Network Learning - Theoretical Foundations

Peter L. Bartlett, Martin Anthony, P. Bartlett, 1999 .

Randomized Smoothing for Stochastic Optimization

Martin J. Wainwright, Peter L. Bartlett, John C. Duchi, 2011, SIAM J. Optim..

Introduction to the special issue on learning theory

Peter L. Bartlett, M. Vidyasagar, P. Bartlett, 1998 .

Learning as Optimization

Peter L. Bartlett, Martin Anthony, P. Bartlett, 1999 .

Matrix regularization techniques for online multitask learning [Technical Report No. UCB/EECS-2008-138]

Peter L. Bartlett, Alekh Agarwal, Alexander Rakhlin, 2008 .

Dynamic Alignment Kernels

Bernhard Schölkopf, Alexander J. Smola, Dale Schuurmans, 2000 .

Online learning with kernel losses

Peter L. Bartlett, Aldo Pacchiano, Niladri S. Chatterji, 2018, ICML.

A Regularization Approach to Metrical Task Systems

Peter L. Bartlett, Niv Buchbinder, Isabelle Stanton, 2010, ALT.

Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks

Peter L. Bartlett, Xavier Carreras, Michael Collins, 2008, J. Mach. Learn. Res..

Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results

Ambuj Tewari, Peter L. Bartlett, P. Bartlett, 2007, J. Mach. Learn. Res..

Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families

Peter L. Bartlett, Wojciech Kotlowski, Fares Hedayati, 2013, COLT.

Stochastic Gradient and Langevin Processes

Michael I. Jordan, Peter L. Bartlett, Xiang Cheng, 2020, ICML.

Correction to "The Importance of Convexity in Learning With Squared Loss"

Peter L. Bartlett, Wee Sun Lee, Robert C. Williamson, 2008, IEEE Trans. Inf. Theory.

Overcoming the complexity of diagnostic problems due to sensor network architecture

David M. Auslander, Philip Haves, Peter L. Bartlett, 2008 .

The importance of convexity in learning with squared loss

Peter L. Bartlett, Wee Sun Lee, Robert C. Williamson, 1998, COLT '96.

Deep learning: a statistical viewpoint

Andrea Montanari, Peter L. Bartlett, Alexander Rakhlin, 2021, Acta Numerica.

Infinite-Horizon Offline Reinforcement Learning with Linear Function Approximation: Curse of Dimensionality and Algorithm pdf

Peter L. Bartlett, Bruno Scherrer, Lin Chen, 2021, ArXiv.

Agnostic Learning with Unknown Utilities pdf

Anca D. Dragan, Peter L. Bartlett, Kush Bhatia, 2021, ITCS.

When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations?

Philip M. Long, Peter L. Bartlett, Niladri S. Chatterji, 2021, COLT.

Optimal and instance-dependent guarantees for Markovian linear stochastic approximation pdf

Martin J. Wainwright, Peter L. Bartlett, Ashwin Pananjady, 2021, COLT.

Optimal variance-reduced stochastic approximation in Banach spaces

Martin J. Wainwright, Michael I. Jordan, Peter L. Bartlett, 2022 .

On the Theory of Reinforcement Learning with Once-per-Episode Feedback pdf

Michael I. Jordan, Peter L. Bartlett, Aldo Pacchiano, 2021, ArXiv.

The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks pdf

Philip M. Long, Peter L. Bartlett, Niladri S. Chatterji, 2021, ArXiv.

Adversarial Examples in Multi-Layer Random ReLU Networks pdf

Peter L. Bartlett, Yeshwanth Cherapanamjeri, S'ebastien Bubeck, 2021, NeurIPS.

Self-Distillation Amplifies Regularization in Hilbert Space pdf

Hossein Mobahi, Peter L. Bartlett, Mehrdad Farajtabar, 2020, NeurIPS.

Adaboost and other large margin classifiers : convexity in pattern classification

Peter L. Bartlett, Mikhail Traskin, 2006 .

Improved bounds for discretization of Langevin diffusions: Near-optimal rates without convexity

Martin J. Wainwright, Peter L. Bartlett, Nicolas Flammarion, 2019, 1907.11331.

Drilling Results in Ice-bound Simulated Lunar Regolith (FJS-1) as Part of the Construction and Resource Utilization Explorer Project (CRUX)

Peter L. Bartlett, Kiel Davis, Kris A. Zacny, 2006 .

ON THE OPTIMALITY OF SAMPLE-BASED ESTIMATES OF THE EXPECTATION OF THE EMPIRICAL MINIMIZER ∗, ∗∗

Peter L. Bartlett, Shahar Mendelson, Petra Philips, 2010 .

Local Rademacher complexities and oracle inequalities in risk minimization

Peter L. Bartlett, Shahar Mendelson, P. Bartlett, 2006 .

When does gradient descent with logistic loss find interpolating two-layer networks? pdf

Philip M. Long, Peter L. Bartlett, Niladri S. Chatterji, 2020, ArXiv.

Margin Distribution and Soft Margin

Bernhard Schölkopf, Alexander J. Smola, Dale Schuurmans, 2000 .

Discussions of boosting papers, and rejoinders

Trevor Hastie, Peter J. Bickel, Robert Tibshirani, 2004 .

Direct iterative tuning via spectral analysis q

Robert R. Bitmead, Leonardo C. Kammer, Peter L. Bartlett, 2000 .

Learning a System-ID Embedding Space for Domain Specialization with Deep Reinforcement Learning

Razvan Pascanu, Peter L. Bartlett, Pieter Abbeel, 2018 .

Book Review: Neural Networks for Pattern Recognition. Christopher M. Bishop, Oxford University Press, New York, 1995. No. of pages: xvii+512. Price: £55. ISBN 0‐19‐853864‐2

Peter L. Bartlett, P. Bartlett, 1997 .

Social Influence Based Clustering of Heterogeneous Information Networks

Andrei Broder, Christos Faloutsos, Jiawei Han, 2013 .

Prediction Algorithms: Complexity, Concentration and Convexity

Peter L. Bartlett, P. Bartlett, 2003 .

Optimal Sample-Based Estimates of the Expectation of the Empirical Minimizer

Peter L. Bartlett, Shahar Mendelson, Petra Philips, 2005 .

Acceleration and Averaging in Stochastic Mirror Descent Dynamics

Peter L. Bartlett, Walid Krichene, P. Bartlett, 2017, 1707.06219.