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Previous Seminars

Machine Learning for the Sciences

Machine Learning for the Sciences
Fri, Dec 13, 2019, 12:00 pm

Taking place every other Friday. Lunch will be provided.

Recent Advances in Non-Convex Distributed Optimization and Learning

Recent Advances in Non-Convex Distributed Optimization and Learning
Mon, Nov 18, 2019, 4:30 pm
We consider a class of distributed non-convex optimization problems, in which a number of agents are connected by a communication network, and they collectively optimize a sum of (possibly non-convex and non-smooth) local objective functions. This type of problem has gained some recent popularities, especially in the application of distributed...
Speaker(s):
Mingyi Hong
University of Minnesota

Diving into TensorFlow 2.0

Diving into TensorFlow 2.0
Fri, Nov 15, 2019, 2:00 pm

Description: Please join us for this 90-minute workshop, taught at an intermediate level. We will briefly introduce TensorFlow 2.0, then dive in to writing a few flavors of neural networks. Attendees will need a laptop and an internet connection.

Speaker(s):
Josh Gordon
Google
Diving in to TensorFlow 2.0

Can learning theory resist deep learning?

Can learning theory resist deep learning?
Fri, Nov 15, 2019, 12:30 pm

Abstract: 

Speaker(s):
Francis Bach
INRIA

Machine Learning for the Sciences

Machine Learning for the Sciences
Fri, Nov 15, 2019, 12:00 pm

Taking place every other Friday. Lunch will be provided.

Convergence Rates of Stochastic Algorithms in Nonsmooth Nonconvex Optimization

Convergence Rates of Stochastic Algorithms in Nonsmooth Nonconvex Optimization
Thu, Nov 14, 2019, 4:30 pm

Abstract:

Speaker(s):
Dmitriy Drusvyatskiy
University of Washington
Convergence Rates of Stochastic Algorithms in Nonsmooth Nonconvex Optimization

Exploration by Optimization in Partial Monitoring

Exploration by Optimization in Partial Monitoring
Tue, Nov 12, 2019, 4:30 pm

Abstract:   

Speaker(s):
Csaba Szepesvari
Professor of Computing Science, University of Alberta

Randomized Methods for Low-Rank Tensor Decomposition in Unsupervised Learning

Randomized Methods for Low-Rank Tensor Decomposition in Unsupervised Learning
Mon, Nov 11, 2019, 4:00 pm
Tensor decomposition discovers latent structure in higher-order data sets and is the higher-order analogue of the matrix decomposition.
Speaker(s):
Tamara Kolda
Sandia National Laboratories

Algorithm and Statistical Inference for Recovery of Discrete Structure

Algorithm and Statistical Inference for Recovery of Discrete Structure
Fri, Nov 8, 2019, 12:30 pm
Discrete structure recovery is an important topic in modern high-dimensional inference. Examples of discrete structure include clustering labels, ranks of players, and signs of variables in a regression model.
Speaker(s):
Chao Gao
Assistant Professor, Statistics University of Chicago

Optimizing for Fairness in ML

Optimizing for Fairness in ML
Thu, Nov 7, 2019, 4:30 pm
Recent events have made evident the fact that algorithms can be discriminatory, reinforce human prejudices, accelerate the spread of misinformation, and are generally not as objective as they are widely thought to be.
Speaker(s):
Elisa Celis
Assistant Professor of Statistics and Data Science, Yale University

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