Peter L. Bartlett

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)

Yevgeny Seldin, Peter L. Bartlett, Koby Crammer: Advice-Efficient Prediction with Expert Advice. CoRR abs/1304.3708 (2013)

Fares Hedayati, Peter L. Bartlett: Exchangeability Characterizes Optimality of Sequential Normalized Maximum Likelihood and Bayesian Prediction with Jeffreys Prior. Journal of Machine Learning Research - Proceedings Track 22: 504-510 (2012)

John C. Duchi, Peter L. Bartlett, Martin J. Wainwright: Randomized Smoothing for Stochastic Optimization. SIAM Journal on Optimization 22(2): 674-701 (2012)

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 Sec. Comput. 9(4): 482-493 (2012)

Alekh Agarwal, Peter L. Bartlett, Pradeep D. Ravikumar, Martin J. Wainwright: Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization. IEEE Transactions on Information Theory 58(5): 3235-3249 (2012)

John C. Duchi, Peter L. Bartlett, Martin J. Wainwright: Randomized smoothing for (parallel) stochastic optimization. CDC 2012: 5442-5444

Peter L. Bartlett, Fernando C. N. Pereira, Christopher J. C. Burges, Léon Bottou, Kilian Q. Weinberger (Eds.): 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

Peter L. Bartlett, Ambuj Tewari: REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs. CoRR abs/1205.2661 (2012)

Alekh Agarwal, Peter L. Bartlett, John C. Duchi: Oracle inequalities for computationally adaptive model selection. CoRR abs/1208.0129 (2012)

Jacob Abernethy, Peter L. Bartlett, Elad Hazan: Blackwell Approachability and No-Regret Learning are Equivalent. Journal of Machine Learning Research - Proceedings Track 19: 27-46 (2011)

Alekh Agarwal, John C. Duchi, Peter L. Bartlett, Clement Levrard: Oracle inequalities for computationally budgeted model selection. Journal of Machine Learning Research - Proceedings Track 19: 69-86 (2011)

Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett: Learning with Missing Features. UAI 2011: 635-642

John Shawe-Taylor, Richard S. Zemel, Peter L. Bartlett, Fernando C. N. Pereira, Kilian Q. Weinberger (Eds.): 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

Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett: Online and Batch Learning Algorithms for Data with Missing Features. CoRR abs/1104.0729 (2011)

Peter L. Bartlett, Jonathan Baxter: Infinite-Horizon Policy-Gradient Estimation. CoRR abs/1106.0665 (2011)

Peter L. Bartlett, Jonathan Baxter, Lex Weaver: Experiments with Infinite-Horizon, Policy-Gradient Estimation. CoRR abs/1106.0666 (2011)

Peter L. Bartlett: Learning to act in uncertain environments: technical perspective. Commun. ACM 53(5): 98 (2010)

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)

Alekh Agarwal, Peter L. Bartlett, Max Dama: Optimal Allocation Strategies for the Dark Pool Problem. Journal of Machine Learning Research - Proceedings Track 9: 9-16 (2010)

Peter L. Bartlett: Optimal Online Prediction in Adversarial Environments. ALT 2010: 34

Jacob Abernethy, Peter L. Bartlett, Niv Buchbinder, Isabelle Stanton: A Regularization Approach to Metrical Task Systems. ALT 2010: 270-284

Peter L. Bartlett: Optimal Online Prediction in Adversarial Environments. Discovery Science 2010: 371

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

Brian Kulis, Peter L. Bartlett: Implicit Online Learning. ICML 2010: 575-582

Marius Kloft, Ulrich Rückert, Peter L. Bartlett: A Unifying View of Multiple Kernel Learning. ECML/PKDD (2) 2010: 66-81

Marius Kloft, Ulrich Rückert, Peter L. Bartlett: A Unifying View of Multiple Kernel Learning. CoRR abs/1005.0437 (2010)

Jacob Abernethy, Peter L. Bartlett, Elad Hazan: Blackwell Approachability and Low-Regret Learning are Equivalent. CoRR abs/1011.1936 (2010)

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)

Jacob Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin: A Stochastic View of Optimal Regret through Minimax Duality. COLT 2009

Alekh Agarwal, Peter L. Bartlett, Pradeep D. Ravikumar, Martin J. Wainwright: Information-theoretic lower bounds on the oracle complexity of convex optimization. NIPS 2009: 1-9

Peter L. Bartlett, Ambuj Tewari: REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs. UAI 2009: 35-42

Jacob Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin: A Stochastic View of Optimal Regret through Minimax Duality. CoRR abs/0903.5328 (2009)

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)

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)

Michael Collins, Amir Globerson, Terry Koo, Xavier Carreras, Peter L. Bartlett: Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks. Journal of Machine Learning Research 9: 1775-1822 (2008)

Peter L. Bartlett, Marten H. Wegkamp: Classification with a Reject Option using a Hinge Loss. Journal of Machine Learning Research 9: 1823-1840 (2008)

Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: Correction to "The Importance of Convexity in Learning With Squared Loss". IEEE Transactions on Information Theory 54(9): 4395 (2008)

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

Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, Sham Kakade, Alexander Rakhlin, Ambuj Tewari: High-Probability Regret Bounds for Bandit Online Linear Optimization. COLT 2008: 335-342

Jacob Abernethy, Peter L. Bartlett, Alexander Rakhlin, Ambuj Tewari: Optimal Stragies and Minimax Lower Bounds for Online Convex Games. COLT 2008: 415-424

David S. Rosenberg, Peter L. Bartlett: The Rademacher Complexity of Co-Regularized Kernel Classes. Journal of Machine Learning Research - Proceedings Track 2: 396-403 (2007)

Peter L. Bartlett, Ambuj Tewari: Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results. Journal of Machine Learning Research 8: 775-790 (2007)

Ambuj Tewari, Peter L. Bartlett: On the Consistency of Multiclass Classification Methods. Journal of Machine Learning Research 8: 1007-1025 (2007)

Peter L. Bartlett, Mikhail Traskin: AdaBoost is Consistent. Journal of Machine Learning Research 8: 2347-2368 (2007)

Ambuj Tewari, Peter L. Bartlett: Bounded Parameter Markov Decision Processes with Average Reward Criterion. COLT 2007: 263-277

Jacob Abernethy, Peter L. Bartlett, Alexander Rakhlin: Multitask Learning with Expert Advice. COLT 2007: 484-498

Alexander Rakhlin, Jacob Abernethy, Peter L. Bartlett: Online discovery of similarity mappings. ICML 2007: 767-774

Peter L. Bartlett, Elad Hazan, Alexander Rakhlin: Adaptive Online Gradient Descent. NIPS 2007

Ambuj Tewari, Peter L. Bartlett: Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs. NIPS 2007

Peter L. Bartlett, Ambuj Tewari: Sample Complexity of Policy Search with Known Dynamics. NIPS 2006: 97-104

Peter L. Bartlett, Mikhail Traskin: AdaBoost is Consistent. NIPS 2006: 105-112

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

Ambuj Tewari, Peter L. Bartlett: On the Consistency of Multiclass Classification Methods. COLT 2005: 143-157

Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan: Learning the Kernel Matrix with Semidefinite Programming. Journal of Machine Learning Research 5: 27-72 (2004)

Evan Greensmith, Peter L. Bartlett, Jonathan Baxter: Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. Journal of Machine Learning Research 5: 1471-1530 (2004)

Peter L. Bartlett, Shahar Mendelson, Petra Philips: Local Complexities for Empirical Risk Minimization. COLT 2004: 270-284

Peter L. Bartlett, Ambuj Tewari: Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results. COLT 2004: 564-578

Peter L. Bartlett, Michael Collins, Benjamin Taskar, David A. McAllester: Exponentiated Gradient Algorithms for Large-margin Structured Classification. NIPS 2004

Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe: Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates. NIPS 2003

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

Peter L. Bartlett, Paul Fischer, Klaus-Uwe Höffgen: Exploiting Random Walks for Learning. Inf. Comput. 176(2): 121-135 (2002)

Peter L. Bartlett, Jonathan Baxter: Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. J. Comput. Syst. Sci. 64(1): 133-150 (2002)

Llew Mason, Peter L. Bartlett, Mostefa Golea: Generalization Error of Combined Classifiers. J. Comput. Syst. Sci. 65(2): 415-438 (2002)

Peter L. Bartlett, Shahar Mendelson: Rademacher and Gaussian Complexities: Risk Bounds and Structural Results. Journal of Machine Learning Research 3: 463-482 (2002)

Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi: Model Selection and Error Estimation. Machine Learning 48(1-3): 85-113 (2002)

Peter L. Bartlett, Shai Ben-David: Hardness results for neural network approximation problems. Theor. Comput. Sci. 284(1): 53-66 (2002)

Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson: Covering numbers for support vector machines. IEEE Transactions on Information Theory 48(1): 239-250 (2002)

Peter L. Bartlett, Olivier Bousquet, Shahar Mendelson: Localized Rademacher Complexities. COLT 2002: 44-58

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

Peter L. Bartlett: An Introduction to Reinforcement Learning Theory: Value Function Methods. Machine Learning Summer School 2002: 184-202

Jonathan Baxter, Peter L. Bartlett: Infinite-Horizon Policy-Gradient Estimation. J. Artif. Intell. Res. (JAIR) 15: 319-350 (2001)

Jonathan Baxter, Peter L. Bartlett, Lex Weaver: Experiments with Infinite-Horizon, Policy-Gradient Estimation. J. Artif. Intell. Res. (JAIR) 15: 351-381 (2001)

Peter L. Bartlett, Shahar Mendelson: Rademacher and Gaussian Complexities: Risk Bounds and Structural Results. COLT/EuroCOLT 2001: 224-240

Evan Greensmith, Peter L. Bartlett, Jonathan Baxter: Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. NIPS 2001: 1507-1514

Leonardo C. Kammer, Robert R. Bitmead, Peter L. Bartlett: Direct iterative tuning via spectral analysis. Automatica 36(9): 1301-1307 (2000)

Martin Anthony, Peter L. Bartlett: Function Learning From Interpolation. Combinatorics, Probability & Computing 9(3): 213-225 (2000)

Sri Parameswaran, Matthew F. Parkinson, Peter L. Bartlett: Profiling in the ASP codesign environment. Journal of Systems Architecture 46(14): 1263-1274 (2000)

Llew Mason, Peter L. Bartlett, Jonathan Baxter: Improved Generalization Through Explicit Optimization of Margins. Machine Learning 38(3): 243-255 (2000)

Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni: Learning Changing Concepts by Exploiting the Structure of Change. Machine Learning 41(2): 153-174 (2000)

Bernhard Schölkopf, Alex J. Smola, Robert C. Williamson, Peter L. Bartlett: New Support Vector Algorithms. Neural Computation 12(5): 1207-1245 (2000)

Peter L. Bartlett, Jonathan Baxter: Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. COLT 2000: 133-141

Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi: Model Selection and Error Estimation. COLT 2000: 286-297

Jonathan Baxter, Peter L. Bartlett: Reinforcement Learning in POMDP's via Direct Gradient Ascent. ICML 2000: 41-48

Alex J. Smola, Peter L. Bartlett: Sparse Greedy Gaussian Process Regression. NIPS 2000: 619-625

Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson: Covering Numbers for Support Vector Machines. COLT 1999: 267-277

Peter L. Bartlett, Shai Ben-David: Hardness Results for Neural Network Approximation Problems. EuroCOLT 1999: 50-62

Llew Mason, Jonathan Baxter, Peter L. Bartlett, Marcus R. Frean: Boosting Algorithms as Gradient Descent. NIPS 1999: 512-518

Leonardo C. Kammer, Robert R. Bitmead, Peter L. Bartlett: Optimal controller properties from closed-loop experiments. Automatica 34(1): 83-91 (1998)

Peter L. Bartlett, Philip M. Long: Prediction, Learning, Uniform Convergence, and Scale-Sensitive Dimensions. J. Comput. Syst. Sci. 56(2): 174-190 (1998)

Peter L. Bartlett, Vitaly Maiorov, Ron Meir: Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks. Neural Computation 10(8): 2159-2173 (1998)

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 Transactions on Information Theory 44(2): 525-536 (1998)

Peter L. Bartlett, Tamás Linder, Gábor Lugosi: The Minimax Distortion Redundancy in Empirical Quantizer Design. IEEE Transactions on Information Theory 44(5): 1802-1813 (1998)

John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony: Structural Risk Minimization Over Data-Dependent Hierarchies. IEEE Transactions on Information Theory 44(5): 1926-1940 (1998)

Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: The Importance of Convexity in Learning with Squared Loss. IEEE Transactions on Information Theory 44(5): 1974-1980 (1998)

Peter L. Bartlett, Vitaly Maiorov, Ron Meir: Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks. NIPS 1998: 190-196

Llew Mason, Peter L. Bartlett, Jonathan Baxter: Direct Optimization of Margins Improves Generalization in Combined Classifiers. NIPS 1998: 288-294

Bernhard Schölkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson: Shrinking the Tube: A New Support Vector Regression Algorithm. NIPS 1998: 330-336

Peter L. Bartlett, Yishay Mansour (Eds.): 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

Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: Correction to 'Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes'. Neural Computation 9(4): 765-769 (1997)

Peter L. Bartlett, Sanjeev R. Kulkarni, S. E. Posner: Covering numbers for real-valued function classes. IEEE Transactions on Information Theory 43(5): 1721-1724 (1997)

Peter L. Bartlett, Tamás Linder, Gábor Lugosi: A Minimax Lower Bound for Empirical Quantizer Design. EuroCOLT 1997: 210-222

Jonathan Baxter, Peter L. Bartlett: A Result Relating Convex n-Widths to Covering Numbers with some Applications to Neural Networks. EuroCOLT 1997: 251-259

Jonathan Baxter, Peter L. Bartlett: The Canonical Distortion Measure in Feature Space and 1-NN Classification. NIPS 1997

Mostefa Golea, Peter L. Bartlett, Wee Sun Lee, Llew Mason: Generalization in Decision Trees and DNF: Does Size Matter? NIPS 1997

Martin Anthony, Peter L. Bartlett, Yuval Ishai, John Shawe-Taylor: Valid Generalisation from Approximate Interpolation. Combinatorics, Probability & Computing 5: 191-214 (1996)

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)

Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: Efficient agnostic learning of neural networks with bounded fan-in. IEEE Transactions on Information Theory 42(6): 2118-2132 (1996)

John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony: A Framework for Structural Risk Minimisation. COLT 1996: 68-76

Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni: Learning Changing Concepts by Exploiting the Structure of Change. COLT 1996: 131-139

Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: The Importance of Convexity in Learning with Squared Loss. COLT 1996: 140-146

Peter L. Bartlett: For Valid Generalization the Size of the Weights is More Important than the Size of the Network. NIPS 1996: 134-140

Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: On Efficient Agnostic Learning of Linear Combinations of Basis Functions. COLT 1995: 369-376

Peter L. Bartlett, Philip M. Long: More Theorems about Scale-sensitive Dimensions and Learning. COLT 1995: 392-401

Martin Anthony, Peter L. Bartlett: Function learning from interpolation. EuroCOLT 1995: 211-221

Adam Kowalczyk, Jacek Szymanski, Peter L. Bartlett, Robert C. Williamson: Examples of learning curves from a modified VC-formalism. NIPS 1995: 344-350

Peter L. Bartlett, Philip M. Long, Robert C. Williamson: Fat-Shattering and the Learnability of Real-Valued Functions. COLT 1994: 299-310

Peter L. Bartlett, Paul Fischer, Klaus-Uwe Höffgen: Exploiting Random Walks for Learning. COLT 1994: 318-327

Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes. COLT 1994: 362-367

Peter L. Bartlett: Vapnik-Chervonenkis Dimension Bounds for Two- and Three-Layer Networks. Neural Computation 5(3): 371-373 (1993)

Peter L. Bartlett: Lower Bounds on the Vapnik-Chervonenkis Dimension of Multi-Layer Threshold Networks. COLT 1993: 144-150

Peter L. Bartlett: Learning With a Slowly Changing Distribution. COLT 1992: 243-252

Peter L. Bartlett, Robert C. Williamson: Investigating the Distribution Assumptions in the Pac Learning Model. COLT 1991: 24-32

Robert C. Williamson, Peter L. Bartlett: Splines, Rational Functions and Neural Networks. NIPS 1991: 1040-1047