*Taking place every other Friday. Lunch will be provided.*

# Upcoming Events

No upcoming events found.

## Events Archive

### Machine Learning for the Sciences

Location:
26 Prospect Ave, Auditorium 103

Tags:
Seminars

### 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

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

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?

### 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

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

**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

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

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