2019 Machine Learning at the Flatiron Institute Seminar Series

January 24, 2019Noam BrownSuperhuman AI for heads-up no-limit poker: Libratus beats top professionals
February 5, 2019Aditya MishraLow-rank and sparse structure in modeling multivariate outcome
February 26, 2019Peter BattagliaLearning structured models of physics
March 14, 2019Leslie GreengardFast multipole methods
April 2, 2019Bin YuThree principles of data science: predictability, computability, and stability (PCS)
April 4, 2019Miles StoudenmireTensor Networks for Machine Learning
April 24, 2019Yashar HezavehEstimating the uncertainties of neural networks predictions
May 30, 2019Charles WindolfNeural Computations via Capsules
June 20, 2019Giacomo TorlaiReconstructing quantum states with Boltzmann machines
October 24, 2019Miles CranmerIntroduction to Hamiltonian Neural Networks
November 7, 2019 Dan Foreman-MackeyOverview of Gaussian Processes for scientific computing
November 21, 2019Erik Henning ThiedeGroup theory for machine learning in the presence of symmetry
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