Seminars

Upcoming Seminars

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Previous Seminars

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

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...
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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.

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Can learning theory resist deep learning?

Fri, Nov 15, 2019, 12:30 pm

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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

Thu, Nov 14, 2019, 4:30 pm

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Exploration by Optimization in Partial Monitoring

Tue, Nov 12, 2019, 4:30 pm

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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.
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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.
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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.
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