Theoretical Machine Learning Seminar

Theoretical Machine Learning Seminar

March 03, 2020 | 12:00pm - 1:30pm

I’ll discuss the Noisy Quadratic Model, the toy problem of minimizing a convex quadratic function with noisy gradient observations. While the NQM is simple enough to have closed-form dynamics for a variety of optimizers, it gives a surprising amount...

Theoretical Machine Learning Seminar

February 27, 2020 | 12:00pm - 1:30pm

This talk considers the preference modeling problem and addresses the fact that pairwise comparison data often reflects irrational choice, e.g. intransitivity. Our key observation is that two items compared in isolation from other items may be...

Theoretical Machine Learning Seminar

February 25, 2020 | 12:00pm - 1:30pm

When high-quality labeled training data are unavailable, an alternative is to learn from training sources that are biased in some way. This talk will cover my group’s recent work on three problems where a learner has access to multiple biased...

Theoretical Machine Learning Seminar

February 20, 2020 | 12:00pm - 1:30pm

Protein-based drugs are becoming some of the most important drugs of the XXI century. The typical mechanism of action of these drugs is a strong protein-protein interaction (PPI) between surfaces with complementary geometry and chemistry. Over the...

Joint IAS/PNI Seminar on ML and Neuroscience

February 18, 2020 | 4:00pm - 5:30pm

People learn in fast and flexible ways that elude the best artificial neural networks. Once a person learns how to “dax,” they can effortlessly understand how to “dax twice” or “dax vigorously” thanks to their compositional skills. In this talk, we...

Theoretical Machine Learning Seminar

February 13, 2020 | 12:00pm - 1:30pm

We recently proposed the "Lottery Ticket Hypothesis," which conjectures that the dense neural networks we typically train have much smaller subnetworks capable of training in isolation to the same accuracy starting from the original initialization...

Theoretical Machine Learning Seminar

February 11, 2020 | 12:00pm - 1:30pm

While there are convergence guarantees for temporal difference (TD) learning when using linear function approximators, the situation for nonlinear models is far less understood, and divergent examples are known. We take a first step towards...