Peter Bartlett's Talks
-
Optimization in high-dimensional prediction
Non-Linear and High Dimensional Inference, IHP, Paris, 3-7 October 2022.
[slides]
-
Benign Overfitting in Linear Regression
AI Institute "Geometry of Deep Learning",
Microsoft Research Redmond, August 26-28, 2019.
[slides]
Frontiers of
Deep Learning Workshop, Simons Institute for the Theory of Computing, UC
Berkeley, July 15-18, 2019.
[slides]
Google, Mountain View, June 17, 2019.
[slides]
Peter G.
Hall Conference 2019: Statistics and Machine Learning, Department of
Statistics, UC Davis, May 10-11, 2019.
[slides]
NeurIPS 2021,
December 8, 2021.
[slides]
-
Tutorial: Generalization in Deep Learning
Deep Learning Boot Camp,
Simons Institute for the Theory of Computing, May 28-31, 2019. With Sasha Rakhlin.
[slides]
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Optimizing Probability Distributions for Learning:
Sampling meets Optimization
University of
California at Los Angeles Mathematics Colloquium
May 2, 2019.
[slides]
University of
Southern California MASCLE Machine Learning Seminar
April 16, 2019.
[slides]
BAIR/BDD Workshop. March 25, 2019.
[slides]
University of
Pennsylvania PRiML Seminar
February 24, 2019.
[slides]
-
Generalization and Optimization in Deep Networks
YES X :
"Understanding Deep Learning: Generalization, Approximation and
Optimization", Eurandom
March 19-22, 2019.
[slides: 1,
2,
3]
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Accurate Prediction from Interpolation: A New Challenge for
Statistical Learning Theory
NAS Colloquium: The Science of Deep Learning
March 13-14, 2019.
[slides]
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Efficient Optimal Strategies for Prediction Games
University of
Queensland Statistics, Modelling and Operations Research Seminar
October 4, 2018.
[slides]
-
Representation, Optimization and Generalization in Deep Learning
BayLearn2018 Bay Area Machine Learning Symposium,
Facebook, October 11, 2018.
[slides]
DIMACS/TRIPODS Workshop on Optimization in Machine Learning,
Lehigh University, August 13-15, 2018.
[slides]
Future Challenges in Statistical Scalability,
Isaac Newton Institute for Mathematical Sciences, June 27, 2018.
[slides]
Foundations of Machine Learning Reunion Workshop,
Simons Institute for the Theory of Computing, June 7, 2018.
[slides]
Bridging
Mathematical Optimization, Information Theory, and Data Science,
Princeton Center for Statistics and Machine Learning, May 14, 2018.
[slides]
Modern
Challenges of Learning Theory,
Centre de Recherches Mathematiques, April 23, 2018.
[slides]
University of
Queensland Maths Colloquium January 25, 2018.
[slides]
IEOR Department Colloquium. October 9, 2017.
[slides]
-
Statistical properties of deep networks
JSM session on Theory at the Intersection of Machine
Learning and Statistics August 2, 2018.
[slides]
NIPS
Workshop on Deep Learning Theory and Practice. December 9, 2017.
[slides]
BAIR/BDD Workshop. November 28, 2017.
[slides]
Machine Learning at Berkeley. October 5, 2017.
[slides]
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The optimal strategy for a linear regression game
[slides]
Dagstuhl seminar. June 19-23, 2017.
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Topics in prediction and learning
[Lecture
1,
Lectures 2 and 3,
Lecture 4,
references]
ENSAE/CREST. Feb 27-Mar 9, 2017.
-
Efficient Optimal Strategies for Universal Prediction.
[Slides: pdf]
Stochastics and Statistics Seminar,
MIT. December 11, 2015.
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Prediction and sequential decision problems in adversarial
environments.
[Slides: pdf]
CDAR Symposium.
Berkeley. October 16, 2015.
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Efficient minimax strategies for online prediction.
[Slides: pdf]
ITA.
February 6, 2015.
Caltech. February 9, 2015.
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Learning in Markov decision problems.
[Slides: pdf]
[Linear bandits survey slides: pdf]
UCLA.
November 10, 2014.
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Model selection and computational oracle inequalities
for large scale problems.
[Slides: pdf]
Workshop on Algorithms for Modern Massive Data Sets
Stanford University.
July 10 - 13, 2012.
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Large scale model selection and computational oracle inequalities
[Slides: pdf]
Conference on Statistical Learning and Data Mining
Rackham Graduate School, University of Michigan, Ann Arbor, MI,
June 5 - 7, 2012.
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Online Prediction
[Slides: pdf]
[Lecture notes: pdf]
Learning Theory: State of the Art
Institut Henri Poincare, Paris, May 9-11, 2011.
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Optimal online prediction in adversarial environments
[Slides: pdf]
The Second Asian Conference on Machine Learning
Tokyo Institute of Technology, Tokyo, Japan, November 8-10, 2010.
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An online allocation problem: Dark pools
[Slides: pdf]
The Mathematics of Ranking
American Institute of Mathematics, Palo Alto, California, August 16-20, 2010.
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l1-regularized linear regression: persistence and oracle inequalities
[Slides: pdf]
Probability and Statistics - an international conference in honor of
P.L. Hsu's 100th birthday
Peking University, Beijing, China. July 6, 2010.
10th International Vilnius Conference on Probability Theory and
Mathematical Statistics. June 30, 2010.
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Convex methods for classification
[Slides: pdf]
IMS Medallion Lecture. June 2008.
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Optimism in Sequential Decision Making
[Slides: pdf]
UC Berkeley Statistics. September 2007.
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Consistency of AdaBoost
[Slides: pdf]
Google. May 2007.
-
AdaBoost and other Large Margin Classifiers:
Convexity in Classification
[Slides: pdf]
Presented at DASP 2006. December 2006.
-
Convex methods for classification
[Slides: pdf]
-
AdaBoost and other Large Margin Classifiers:
Convexity in Classification
[Slides: ps]
Presented at the Institute
of Statistical Science, Academia
Sinica, Taipei, Taiwan., July 31, 2006.
-
AdaBoost is Universally Consistent
[Slides: ps,
pdf]
Presented at the
2006 Summer Institute
held by the Institute of Information Science (IIS), Academia Sinica,
Taipei, Taiwan., August 3, 2006.
-
Regression Methods for Pattern Classification:
Statistical Properties of Large Margin Classifiers
[Slides: ps,
pdf]
Presented at
Mathematisches Forschungsinstitut
Oberwolfach, October 16-22, 2005.
-
Empirical Minimization and Risk Bounds
[Slides: ps]
-
Statistical Properties of Large Margin Classifiers
[Slides: ps,
pdf]
-
Large Margin Classifiers: Convexity and Classification
[Slides: ps,
pdf]
-
Large Margin Methods for Structured Classification: Exponentiated
Gradient Algorithms
[Slides: ps,
ps.gz]
-
Local Rademacher Averages and Empirical Minimization
[Slides: ps,
pdf]
-
The Role of Convexity in Prediction Problems.
[Slides: ps,
ps.gz;
Handouts: ps,
ps.gz]
Presented at
UC Berkeley EECS Joint Colloquium Distinguished Lecture Series,
September 17, 2003.
-
Prediction Algorithms: Complexity, Concentration, and Convexity.
[Slides: ps,
ps.gz;
Handouts: ps,
ps.gz]
Presented at
SYSID2003: 13th IFAC Symposium on System
Identification, Rotterdam, The Netherlands, 27-29 August, 2003.
See:
Extended abstract.
-
Convexity, Classification, and Risk Bounds.
[Slides: ps,
ps.gz;
Handouts: ps,
ps.gz]
Presented at
Workshop
on Advances in Machine Learning, Montreal, Canada, June 8-11,
2003, and
AMS/IMS/SIAM Joint
Summer Research Conference on Machine Learning, Statistics, and
Discovery, Snowbird, Utah, June 22-26, 2003.
See:
Convexity, classification, and risk bounds.
Peter L. Bartlett, Michael I. Jordan and Jon D. McAuliffe.
Technical Report 638, Department of Statistics, U.C. Berkeley,
2003.
- NIPS'98 Tutorial
(an introduction to learning theory)
Last update: Mon Oct 9 23:20:27 PDT 2006