For the up-to-date seminar information, visit this link.

April 23 Francis Bach Beyond stochastic gradient descent for large-scale machine learning
April 30 Balázs Kégl Learning to discover: signal/background separation and the Higgs boson challenge
May 8, 10:45-12 Sebastien Bubeck On the influence of the seed graph in the preferential attachment model

May 22, 10:45-12

Yishay Mansour Robust Bayesian Inference
May 27, 10:45-12 Ohad Shamir Information Trade-offs in Machine Learning
June 5, 10:30-11:30 Sasha Rakhlin A Tale of Three Regression Problems
June 6, 10:30-11:30 Amit Daniely From average case complexity to improper learning complexity

June 11, 15:30-18:00


June 12, 14:00-16:30

Stéphane Mallat Minicourse: Analyzing Deep Neural Networks for Learning
Place: Centre de Recerca Matemàtica, Facultat de Ciències. UAB, Bellaterra (Barcelona)
Auditori Conference Room
Time: 10:30 am after the Research Program's weekly coffee time. No registration is needed.




Learning theory is a field at the intersection of statistics, probability, computer science, and optimization. This mathematical theory is concerned with theoretical guarantees for machine learning algorithms. The dialogue between computation and statistics has been key to the enormous advances and growth in the field. This dialogue is becoming more and more important as we enter the age of large-scale data. The goal of the Program is to bring together experts from fields spanning the broad range of modern learning theory, from Statistics to Optimization.


The research program will take place in Barcelona, Spain, from April 7th, 2014 to July 14th, 2014. Over the course of these fourteen weeks, the Centre de Recerca Matemática in Barcelona will host a number of visitors, and we expect a lively research atmosphere with frequent talks and short seminars. In addition, there will be several co-located conferences and a large 3-day workshop on learning theory. 

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