Previous Special Year Seminar

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...

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...

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...

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...

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...

Please Note: The seminars are not open to the general public, but only to active researchers. Register here for this event: https://docs.google.com/forms/d/e/1FAIpQLScJ-BUVgJod6NGrreI26pedg8wGEyP… Abstract for talk #1: The affinity between...

For a function K : R^d x R^d -> R, and a set P = {x_1, ..., x_n} in d-dimension, the K graph G_P of P is the complete graph on n nodes where the weight between nodes i and j is given by K(x_i, x_j). In this paper, we initiate the study of when...

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...

Causal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods require that we observe all confounders, variables that affect both the causal variables and the outcome variables. But whether we have...