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Machine Learning for the Sciences

Machine Learning for the Sciences

Taking place every other Friday. Lunch will be provided.

Location: 26 Prospect Ave, Auditorium 103
Tags: Seminars

Grad Students: Interested in Data Science?

Grad Students: Interested in Data Science?

Center for Statistics and Machine Learning is holding an informal graduate information session about its certificate program.

Lunch will be served!

Location: 26 Prospect Ave

Recent Advances in Non-Convex Distributed Optimization and Learning

Recent Advances in Non-Convex Distributed Optimization and Learning
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...
Location: B205 Engineering Quadrangle
Speaker(s):
Mingyi Hong
University of Minnesota
Tags: Seminars

Diving into TensorFlow 2.0

Diving into TensorFlow 2.0

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.

Location: Lewis Science Library 138
Speaker(s):
Josh Gordon
Google
Diving in to TensorFlow 2.0
Tags: Seminars

Can learning theory resist deep learning?

Can learning theory resist deep learning?

Abstract: 

Location: CS 105
Speaker(s):
Francis Bach
INRIA
Tags: Seminars

Machine Learning for the Sciences

Machine Learning for the Sciences

Taking place every other Friday. Lunch will be provided.

Location: 26 Prospect Ave, Auditorium 103
Tags: Seminars

Convergence Rates of Stochastic Algorithms in Nonsmooth Nonconvex Optimization

Convergence Rates of Stochastic Algorithms in Nonsmooth Nonconvex Optimization

Abstract:

Location: B205 Engineering Quadrangle
Speaker(s):
Dmitriy Drusvyatskiy
University of Washington
Convergence Rates of Stochastic Algorithms in Nonsmooth Nonconvex Optimization
Tags: Seminars

Exploration by Optimization in Partial Monitoring

Exploration by Optimization in Partial Monitoring

Abstract:   

Location: Sherrerd 101
Speaker(s):
Csaba Szepesvari
Professor of Computing Science, University of Alberta
Tags: Seminars

Randomized Methods for Low-Rank Tensor Decomposition in Unsupervised Learning

Randomized Methods for Low-Rank Tensor Decomposition in Unsupervised Learning
Tensor decomposition discovers latent structure in higher-order data sets and is the higher-order analogue of the matrix decomposition.
Location: 214 Fine Hall
Speaker(s):
Tamara Kolda
Sandia National Laboratories
Tags: Seminars

Algorithm and Statistical Inference for Recovery of Discrete Structure

Algorithm and Statistical Inference for Recovery of Discrete Structure
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.
Location: Sherrerd 101
Speaker(s):
Chao Gao
Assistant Professor, Statistics University of Chicago
Tags: Seminars

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