# Browse By Person: Bartlett, Peter

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**72**.## Book

Anthony, Martin & Bartlett, P. L.
(1999)

*Neural network learning:theoretical foundations.*Cambridge University Press, Cambridge, U.K..## Journal Article

Najafi, Massieh, Auslander, David, Bartlett, Peter, Haves, Philip, & Sohn, Michael
(2012)
Application of machine learning in the fault diagnostics of air handling units.

*Applied Energy*, pp. 347-358.
7

7

Agarwal, Alekh, Bartlett, Peter, Ravikumar, Pradeep, & Wainwright, Martin
(2012)
Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization.

*IEEE Transactions on Information Theory*,*58*(5), pp. 3235-3249.
29

13

Duchi, John, Bartlett, Peter, & Wainwright, Martin
(2012)
Randomized smoothing for stochastic optimization.

*SIAM Journal on Optimization*,*22*(2), pp. 674-701.
8

4

Bartlett, Peter, Mendelson, Shahar, & Neeman, Joseph
(2012)
l1-regularized linear regression: persistence and oracle inequalities.

*Probability Theory and Related Fields*,*154*(1 - 2), pp. 193-224.
9

5

Barth, Adam, Rubinstein, Benjamin, Sundararajan, Mukund, Mitchell, John, Song, Dawn, & Bartlett, Peter
(2012)
A learning-based approach to reactive security.

*IEEE Transactions on Dependable and Secure Computing*,*9*(4), pp. 482-493.
1

1

Arlot, Sylvain & Bartlett, Peter L.
(2011)
Margin-adaptive model selection in statistical learning.

*Bernoulli*.
1

2

Bartlett, Peter L.
(2010)
Learning to act in uncertain environments : technical perspective.

*Communications of the ACM*,*53*(5), p. 98.

Bartlett, Peter L., Mendelson, Shahar, & Philips, Petra
(2010)
On the optimality of sample-based estimates of the expectation of the empirical minimizer.

*ESAIM : Probability and Statistics*,*14*(Jan), pp. 315-337.

Rubinstein, Benjamin I.P., Bartlett, Peter L., & Rubinstein, J. Hyam
(2010)
Corrigendum to “Shifting : one-inclusion mistake bounds and sample compression” [J. Comput. System Sci. 75 (1) (2009) 37–59].

*Journal of Computer and System Sciences*,*76*(3-4), pp. 278-280.

Bartlett, Peter L.
(2010)
Optimal online prediction in adversarial environments.

*Algorithmic Learning Theory: 21st International Conference Proceedings [Lecture Notes in Computer Science]*,*6331*, p. 34.

Bartlett, Peter L.
(2010)
Optimal online prediction in adversarial environments.

*Discovery Science [Lecture Notes in Artificial Intelligence]*,*6332*, p. 371.

Abernethy, Jacob, Bartlett, Peter L., Buchbinder, Niv, & Stanton, Isabelle
(2010)
A Regularization Approach to Metrical Task Systems.

*LNCS- Algorithmic Learning Theory*,*6331*, pp. 270-284.
1

Barth, Adam, Rubinstein, Benjamin I.P., Sundararajan, Mukund, Mitchell, John C., Song, Dawn, & Bartlett, Peter L.
(2010)
A learning-based approach to reactive security.

*Financial Cryptography and Data Security*,*6052*, pp. 192-206.
4

4

Kloft, Marius, Rückert, Ulrich, & Bartlett, Peter L.
(2010)
A unifying view of multiple kernel learning.

*Machine Learning and Knowledge Discovery in Databases*,*6322*, pp. 66-81.
2

2

Rosenberg, David, Sindhwani, Vikas, Bartlett, Peter L., & Niyogi, Partha
(2009)
Multiview point cloud kernels for semisupervised learning [Lecture Notes].

*IEEE Signal Processing Magazine*,*26*(5), 145-150 .
68

6

4

Rubinstein, B., Bartlett, P.L., & Rubinstein, J.
(2009)
Shifting : one-inclusion mistake bounds and sample compression.

*Journal of Computer and System Sciences*,*75*(1), pp. 37-59.
6

5

Lee, Wee Sun, Bartlett, Peter L., & Williamson, Robert C.
(2008)
Correction to “The Importance of Convexity in Learning With Squared Loss” [Sep 98 1974-1980].

*IEEE Transactions on Information Theory*,*54*(9), p. 4395.
67

Bartlett, Peter L. & Wegkamp, Marten H.
(2008)
Classification with a reject option using a hinge loss.

*Journal of Machine Learning Research*,*9*, pp. 1823-1840.
58

33

Collins, Michael, Globerson, Amir, Koo, Terry, Carreras, Xavier, & Bartlett, Peter L.
(2008)
Exponentiated gradient algorithms for conditional random fields and max-margin Markov Networks.

*Journal of Machine Learning Research*,*9*(Aug), pp. 1775-1822.
61

28

Bartlett, Peter L.
(2008)
Fast rates for estimation error and oracle inequalities for model section.

*Econometric Theory*,*24*(2), pp. 545-552.
5

6

Bartlett, Peter L. & Traskin, Mikhail
(2007)
Adaboost is consistent.

*Journal of Machine Learning Research*,*8*, pp. 2347-2368.
578

35

22

Tewari, Ambuj & Bartlett, Peter L.
(2007)
On the consistency of multiclass classification methods.

*Journal of Machine Learning Research*,*8*, pp. 1007-1025.
56

33

Bartlett, Peter L. & Tewari, Ambuj
(2007)
Sparseness vs estimating conditional probabilities : some asymptotic results.

*Journal of Machine Learning Research*,*8*(April), pp. 775-790.
25

18

Tewari, Ambuj & Bartlett, Peter L.
(2007)
Bounded parameter Markov Decision Processes with average reward criterion.

*Learning Theory*,*4539*, pp. 263-277.
1

4

Abernethy, Jacob, Bartlett, Peter L., & Rakhlin, Alexander
(2007)
Multitask learning with expert advice.

*Learning Theory*,*4539*, pp. 484-498.
1

2

Bartlett, Peter L. & Mendelson, Shahar
(2006)
Empirical minimization.

*Probability Theory and Related Fields*,*135*(3), pp. 311-334.
33

30

Bartlett, Peter L., Jordan, Michael I., & McAuliffe, Jon D.
(2006)
Comment.

*Statistical Science*,*21*(3), pp. 341-346.
2

Bartlett, Peter L., Jordan, Michael I, & McAuliffe, Jon D
(2006)
Convexity, Classification, and Risk Bounds.

*Journal of the American Statistical Association*,*101*(473), pp. 138-156.
245

181

Bartlett, Peter & Mendelson, Shahar
(2006)
Discussion: Local Rademacher complexities and oracle inequalities in risk minimization.

*Annals of Statistics*,*34*(6), pp. 2657-2663.
1

Bartlett, Peter L., Bousquet, Olivier, & Mendelson, Shahar
(2005)
Local Rademacher complexities.

*The Annals of Statistics*,*33*(4), pp. 1497-1537.
121

80

Tewari, Ambuj & Bartlett, Peter L.
(2005)
On the consistency of multiclass classification methods.

*Lecture Notes in Computer Science*,*3559*, pp. 143-157.
6

Lanckriet, G. R. G., Cristianini, N., Bartlett, P. L., El Ghaoui, L., & Jordan, M. I.
(2004)
Learning the kernel matrix with semidefinite programming.

*Journal of Machine Learning Research*,*5*, pp. 27-72.
1,040

623

Bartlett, Peter L. & Mendelson, Shahar
(2003)
Rademacher and Gaussian complexities: Risk bounds and structural results.

*Journal of Machine Learning Research*,*3*(3), pp. 463-482.
303

Bartlett, P. L., Boucheron, S., & Lugosi, G.
(2002)
Model selection and error estimation.

*Machine Learning*,*48*(1-3), pp. 85-113.
135

97

Baxter, J. & Bartlett, P. L.
(2001)
Infinite-horizon policy-gradient estimation.

*Journal of Artificial Intelligence Research*,*15*, 319-350 .
241

159

Schapire, R. E., Freund, Y., Bartlett, P.L., & Lee, W. S.
(1998)
Boosting the margin: A new explanation for the effectiveness of voting methods.

*The Annals of Statistics*,*26*(5), pp. 1651-1686.
887

536

Bartlett, P.L.
(1998)
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), pp. 525-536.
1,301

335

266

## Conference Paper

Seldin, Yevgeny, Bartlett, Peter L., Crammer, Koby, & Abbasi-Yadkori, Yasin
(2014)
Prediction with limited advice and multiarmed bandits with paid observations. In

*International Conference on Machine Learning*, 21–June 26, 2014, Beijing, China.
9

Bartlett, Peter, Grunwald, Peter, Harremoes, Peter, Hedayati, Fares, & Kotowski, Wojciech
(2013)
Horizon-independent optimal prediction with log-loss in exponential families. In
Shalev-Shwartz, S & Steinwart, I (Eds.)

*JMLR: Workshop and Conference Proceedings, Volume 30: Conference on Learning Theory*, 12 - 14 June, 2013, United States of America.
1

Seldin, Yevgeny, Bartlett, Peter L., & Crammer, Koby
(2013)
Open problem : adversarial multiarmed bandits with limited advice. In

*Conference on Learning Theory (COLT 2013)*, June 12-14, 2013, Princeton, NJ.
35

Hedayati, Fares & Bartlett, Peter
(2012)
Exchangeability characterizes optimality of sequential normalized maximum likelihood and Bayesian prediction with Jeffreys Prior. In
Lawrence, N & Girolami, M (Eds.)

*JMLR: Workshop and Conference Proceedings, Volume 22: Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics*, 21 - 23 April, 2012, Canary Islands.

Hedayati, Fares & Bartlett, Peter
(2012)
The optimality of Jeffreys Prior for online density estimation and the asymptotic normality of maximum likelihood estimators. In
Mannor, S, Srebro, N, & Williamson, R (Eds.)

*JMLR: Workshop and Conference Proceedings, Volume 23: Proceedings of the 25th Annual Conference on Learning Theory*, 25 - 27 June, 2012, Scotland.
1

Abernethy, Jacob, Bartlett, Peter, & Hazan, Elad
(2011)
Blackwell approachability and no-regret learning are equivalent. In
Kakade, S & von Luxburg, U (Eds.)

*Proceedings of the 24th Annual Conference on Learning Theory [JMLR Workshop and Conference Proceedings, Volume 19]*, 9 - 11 June, 2011, Hungary.
2

Rostamizadeh, Afshin, Agarwal, Alekh, & Bartlett, Peter
(2011)
Learning with missing features. In
Cozman, F & Pfeffer, A (Eds.)

*Uncertainty in Artificial Intelligence: Proceedings of the 27th Conference, UAI 2011*, 14 - 17 July, 2011, Spain.
4

Agarwal, Alekh, Duchi, John, Bartlett, Peter, & Levrard, Clement
(2011)
Oracle inequalities for computationally budgeted model selection. In
Kakade, S & von Luxburg, U (Eds.)

*Proceedings of the 24th Annual Conference on Learning Theory [JMLR Workshop and Conference Proceedings, Volume 19]*, 9 - 11 June, 2011, Hungary.
1

Kulis, Brian & Bartlett, Peter L.
(2010)
Implicit online learning. In

*Proceedings of the 27 th International Conference on Machine Learning*, Haifa, Israel.
2

Agarwal, Alekh , Bartlett, Peter L., & Dama, Max
(2010)
Optimal allocation strategies for the dark pool problem. In
Teh, Yee Whye & Titterington, Mike (Eds.)

*Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics*, Chia Laguna Resort, Sardinia, Italy, pp. 9-16.
33

1

Agarwal, Alekh, Bartlett, Peter, Ravikumar, Pradeep, & Wainwright, Martin
(2009)
Information-theoretic lower bounds on the oracle complexity of convex optimization. In
Bengio, Y, Schuurmans, D, Lafferty, J, Williams, C, & Culotta, A (Eds.)

*Advances in Neural Information Processing Systems 22: Proceedings of the 2009 Conference*, 7 - 10 December, 2009, Canada.
3

Bartlett, Peter L. & Tewari, Ambuj
(2009)
REGAL : a regularization based algorithm for reinforcement learning in weakly communicating MDPs. In

*Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009))*, McGill University, Montreal.
13

Abernethy, Jacob , Agarwal, Alekh , & Bartlett, Peter L.
(2009)
A Stochastic View of Optimal Regret through Minimax Duality. In
Dasgupta , S & Klivans, A (Eds.)

*Proceedings of the 22nd Annual Conference on Learning Theory*, Montreal, Quebec.
47

1

Bartlett, Peter L., Dani, Varsha, Hayes, Thomas, Kakade, Sham, Rakhlin, Alexander, & Tewari, Ambuj
(2008)
High-probability regret bounds for bandit online linear optimization. In

*21th Annual Conference on Learning Theory (COLT 2008)*, 9-12 July 2008, Helsinki, Finland.
140

11

Najafi, Massieh, Auslander, David M. , Bartlett, Peter L., & Haves, Philip
(2008)
Application of machine learning in fault diagnostics of mechanical systems. In

*Proceedings of the World Congress on Engineering and Computer Science 2008: International Conference on Modeling, Simulation and Control 2008*, International Association of Engineers, San Fransisco, pp. 957-962.

Najafi, M., Auslander, D.M. , Bartlett, P.L., & Haves, P.
(2008)
Fault diagnostics and supervised testing : How fault diagnostic tools can be proactive? In
Grigoriadis , K. (Ed.)

*Proceeding (633) Intelligent Systems and Control - 2008*, ACTA Press, Orlando, USA , pp. 34-45.

Barreno, M., Bartlett, P.L., Chi, F.J., Joseph, A.D., Nelson, B., Rubinstein, B.I.P., et al.
(2008)
Open problems in the security of learning. In

*Proceedings of the 1st ACM workshop on Workshop on AISec - AISec '08*, Association for Computing Machinery, Alexandria, VA, pp. 19-26.
8

Tewari, Ambuj & Bartlett, Peter L.
(2008)
Optimistic linear programming gives logarithmic regret for irreducible MDPs. In
Platt, John, Koller, Daphne, Singer, Yoram, & Rowies, Sam (Eds.)

*Advances in Neural Information Processing Systems 20 (NIPS)*, 2008, Cambridge, MA.

Najafi, M. , Auslander, D.M. , Bartlett, P.L. , & Haves, P.
(2008)
Overcoming the complexity of diagnostic problems due to sensor network architecture. In
Grigoriadis , K. (Ed.)

*Proceeding (633) Intelligent Systems and Control - 2008*, ACTA Press, Orlando, USA.

Rubinstein, Benjamin I. P., Bartlett, Peter L., & Rubinstein, J. Hyamm
(2007)
Shifting, one-inclusion mistake bounds and tight multiclass expected risk bounds. In
Schölkopf, Bernhard , Platt, John , & Hofmann, Thomas (Eds.)

*Advances in Neural Information Processing Systems 19 : Proceedings of the 2006 Conference*, MIT Press, Hyatt Regency, Vancouver, pp. 1193-1208.
16

3

Bartlett, Peter L., Hazan, Elad, & Rakhlin, Alexander
(2007)
Adaptive online gradient descent. In
Platt, J.C., Koller, D., Singer, Y., & Roweis, S.T. (Eds.)

*Advances in Neural Information Processing Systems 20*, MIT Press, Canada, pp. 65-72.

Rakhlin, Alexander, Abernethy, Jacob, & Bartlett, Peter L.
(2007)
Online discovery of similarity mappings. In

*Proceedings of the 24th international conference on Machine learning - ICML '07*, Association for Computing Machinery, Oregon State University in Corvallis, Oregon, pp. 767-774.

Rosenberg, David & Bartlett, Peter L.
(2007)
The rademacher complexity of coregularized kernel classes. In
Meila, Marina & Shen, Xiaotng (Eds.)

*11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007)*, 21- 24 March 2007, Caribe Hilton Hotel, San Juan, Puerto Rico.

Bartlett, Peter L. & Traskin, Mikhail
(2006)
Adaboost and other large margin classifiers : convexity in pattern classification. In

*Proceedings of the 2006 Workshop on Defense Applications of Signal Processing (DASP'06)*, The Kingfisher Bay Resort, Fraser Island, Queensland, Australia.

Jimenez-Rodriquez , R., Sitar, N., & Bartlett, P.L.
(2006)
Maximum likelihood estimation of trace length distribution parameters using the EM algorithm. In
Barla, G. & Barla, M. (Eds.)

*Prediction, Analysis and Design in Geomechanical Applications : Proceedings of the Eleventh International Conference on Computer Methods and Advances in Geomechanics (IACMAG-2005)*, Pàtron editore S.r.l., Torino, Italy, pp. 619-626.

Bartlett, Peter L., Collins, Michael J., Taskar, Ben, & McAllester, David A.
(2005)
Exponentiated gradient algorithms for large-margin structured classification. In
Saul , Lawrence K. , Weiss, Yair , & Bottou, Léon (Eds.)

*Advances in Neural Information Processing Systems 17 : Proceedings of the 2004 Conference*, MIT Press, Hyatt Regency, Vancouver, pp. 113-120.
6

## Report

Kloft, Marius, Rückert, Ulrich, & Bartlett, Peter L.
(2010)

*A unifying view of multiple kernel learning.*University of California, Berkeley.

Abernethy, Jacob D., Bartlett, Peter L., Rakhlin, Alexander, & Tewari, Ambuj
(2008)

*Optimal strategies and minimax lower bounds for online convex games.*University of California, Berkeley, Berkeley, California (USA).

Abernethy, Jacob, Bartlett, Peter, Rakhlin, Alexander, & Tewari, Ambuj
(2008)

*Optimal strategies and minimax lower bounds for online convex games [Technical Report No. UCB/EECS-2008-19].*University of California, Berkeley, United States of America.

Rakhlin, Alexander, Tewari, Ambuj, & Bartlett, Peter L.
(2007)

*Closing the gap between bandit and full-information online optimization : high-probability regret bound.*University of California, Berkeley, California (USA).

Bartlett, Peter L., Hazan, Elad, & Rakhlin, Alexander
(2007)

*Adaptive online gradient descent.*University of California, Berkeley, California.

Abernethy, Jacob D., Bartlett, Peter L., & Rakhlin, Alexander
(2007)

*Multitask learning with expert advice.*University of California, Berkeley, California.

Rubinstein, Benjamin I.P., Bartlett, Peter L., & Rubinstein, J. Hyam
(2007)

*Shifting : one-inclusion mistake bounds and sample compression.*University of California, Berkeley, Berkeley, California.

Lanckriet, Gert R. G., Cristianini, Nello, Bartlett, Peter, El Ghaoui, Laurent, & Jordan, Michael I.
(2002)

*Learning the Kernel Matrix with Semi-Definite Programming.*University of California, Berkeley, California (USA).