Peter L. Bartlett Professor Computer Science Division and Department of Statistics Berkeley AI Research Lab Simons Institute for the Theory of Computing UC Berkeley

Publications, Google Scholar

Talks

• Fall 2016: Stat155 Game theory
• Spring 2016: CS281B/Stat241B Statistical learning theory
• Fall 2015: CS281A/Stat241A Statistical learning theory
• Spring 2015: CS189/289A Introduction to Machine Learning
• Fall 2014: CS294/Stat260 Learning in sequential decision problems
• Spring 2013: Stat210B Theoretical Statistics
• Fall 2010: Stat153 Introduction to time series analysis

Conferences:

• TAU Theory-Fest "Workshop on Advances in Learning Theory", Tel Aviv University, December 31, 2019.
  
• AI Institute "Geometry of Deep Learning", Microsoft Research Redmond, August 26-28, 2019.
  
• Foundations of Deep Learning Program, Simons Institute for the Theory of Computing, May-August, 2019.
  
• Peter G. Hall Conference 2019: Statistics and Machine Learning, Department of Statistics, UC Davis, May 10-11, 2019.
  
• YES X : "Understanding Deep Learning: Generalization, Approximation and Optimization", Eurandom March 19-22, 2019.
  
• The Science of Deep Learning, Arthur M. Sackler Colloquium, National Academy of Science, Washington DC, March 13-14, 2019.
  
• Foundations of Data Science Program, Simons Institute, Berkeley, Fall 2018.
  
• DIMACS/TRIPODS Workshop, Lehigh University, 13-15 August, 2018.
  
• JSM 2018, Vancouver, Canada, July 28-August 2, 2018.
  
• ICML 2018, Stockholm, Sweden, July 10-15, 2018.
  
• COLT 2018, Stockholm, Sweden, July 5-9, 2018.
  
• Future Challenges in Statistical Scalability, Isaac Newton Institute for Mathematical Sciences, Cambridge, UK, June 25-29, 2018.
  
• Bridging Mathematical Optimization, Information Theory, and Data Science, Princeton Center for Theoretical Science, May 14-16, 2018.
  
• Workshop on Modern Challenges in Learning Theory, CRM, Montreal, Canada, April 23-26, 2018.
  
• AIStat 2018, Canary Islands, April 9-11, 2018.
  
• Algorithmic Learning Theory, Lanzarote, Spain, April 7-9, 2018.
  
• NIPS 2017, Long Beach, CA, December 4-9, 2017.
  

• Kush Bhatia
• Niladri Chatterji
• Xiang Cheng
• Victor Gabillon
• Tan Nguyen
• Aldo Pacchiano
• Yannik Pitcan
• Nan Ye

Former Students and Postdocs:

• Alan Malek (MIT)
• Martin Peron (CSIRO)
• Brendan van Rooyen (ANU)
• Yasin Abbasi-Yadkori (Adobe Research)
• Walid Krichene (Google)
• Wouter Koolen (Researcher, CWI, Amsterdam)
• Yevgeny Seldin (Assistant Professor, Computer Science, University of Copenhagen)
• Fares Hedayati (Senior Data Scientist, Upwork, San Francisco)
• Alekh Agarwal (Researcher, Microsoft Research, New York)
• Jake Abernethy (Assistant Professor, CSE, University of Michigan, Ann Arbor)
• Afshin Rostamizadeh (Research Scientist, Google Research, New York)
• Ben Rubinstein (Senior Lecturer, University of Melbourne)
• Alexander Rakhlin (Associate Professor, Statistics, University of Pennsylvania)
• David Rosenberg (Chief Scientist, YP Mobile Labs, New York)
• Mikhail Traskin (Quantitative Researcher, Citadel LLC, Chicago)
• Ambuj Tewari (Assistant Professor, Statistics, University of Michigan, Ann Arbor)
• Evan Greensmith (Senior Software Engineer, Exari Systems)
• Matthias Seeger (Senior Machine Learning Scientist, Amazon, Berlin)
• Ying Guo (CSIRO, Sydney, Australia)
• Shahar Mendelson (Professor, Technion and ANU)
• Llew Mason (Vice President, Consumer Website, Amazon, Seattle WA)
• Alexander J. Smola (Professor, Machine Learning, CMU).
• Jonathan Baxter (Vice President, Two Sigma Investments, New York)
• Mostefa Golea (SW R&D Manager, JDS Uniphase, Ottawa)
• Leonardo Kammer (Senior Controls Engineer, GE Global Research, New York)
• Wee Sun Lee (Associate Professor, Computer Science, National University of Singapore)

Note for Prospective Students:

"Neural Network Learning: Theoretical Foundations", Martin Anthony and Peter Bartlett, Cambridge University Press, 1999. CUP. Amazon.

Biography

Achievements

Contact Information: