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October 2017

On Learning Theory and Neural Networks

Amit Daniely (Google)

October 27 @ 11:00 am - 12:00 pm

Abstract:  Can learning theory, as we know it today, form a theoretical basis for neural networks. I will try to discuss this question in light of two new results -- one positive and one negative. Based on joint work with Roy Frostig, Vineet Gupta and Yoram Singer, and with Vitaly Feldman Biography: Amit Daniely is an Assistant Professor at the Hebrew University in Jerusalem, and a research scientist at Google Research, Tel-Aviv. Prior to that, he was a research scientist at Google Research,…

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November 2017

Unbiased Markov chain Monte Carlo with couplings

Pierre Jacob (Harvard)

November 1 @ 11:00 am - 12:00 pm

Abstract: Markov chain Monte Carlo methods provide consistent approximations of integrals as the number of iterations goes to infinity. However, these estimators are generally biased after any fixed number of iterations, which complicates both parallel computation. In this talk I will explain how to remove this burn-in  bias by using couplings of Markov chains and a telescopic sum argument, inspired by Glynn & Rhee (2014). The resulting unbiased estimators can be computed independently in parallel, and averaged. I will present…

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Statistics, Computation and Learning with Graph Neural Networks

Joan Bruna Estrach (NYU)

November 3 @ 11:00 am - 12:00 pm

Abstract: Deep Learning, thanks mostly to Convolutional architectures, has recently transformed computer vision and speech recognition. Their ability to encode geometric stability priors, while offering enough expressive power, is at the core of their success. In such settings, geometric stability is expressed in terms of local deformations, and it is enforced thanks to localized convolutional operators that separate the estimation into scales. Many problems across applied sciences, from particle physics to recommender systems, are formulated in terms of signals defined…

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Stochastics and Statistics Seminar

Alex Dimakis (University of Texas at Austin)

November 17 @ 11:00 am - 12:00 pm
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December 2017

Challenges in Developing Learning Algorithms to Personalize Treatment in Real Time

Susan Murphy (University of Michigan)

December 1 @ 11:00 am - 12:00 pm

Abstract:  A formidable challenge in designing sequential treatments is to  determine when and in which context it is best to deliver treatments.  Consider treatment for individuals struggling with chronic health conditions.  Operationally designing the sequential treatments involves the construction of decision rules that input current context of an individual and output a recommended treatment.   That is, the treatment is adapted to the individual's context; the context may include  current health status, current level of social support and current level of…

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Stochastics and Statistics Seminar

Alex Bloemendal (Broad Institute)

December 8 @ 11:00 am - 12:00 pm
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