Systems | Information | Learning | Optimization
 

A Matrix Factorization Approach to Multiple Imputation and DCM Bandits: Learning to Rank with Multiple Clicks

A Matrix Factorization Approach to Multiple Imputation: https://vimeo.com/155397341 Almost all empirical analysis in the social sciences is plagued with the problem of missing data, for instance in opinion surveys, some respondents choose not to answer certain questions, or in longitudnal surveys respondents in the pilot round may drop out in …

Linear Dueling Bandits

Dueling bandits is a variant of the multi-armed bandit problem where instead of playing an arm and observing the reward at each instant, you duel two arms (pairwise comparison) and observe the winner among the two. Linear bandits is a special case of contextual bandits where the reward of an …

Gobble Gobble: Random Graph Models for Large Empirical Networks and Blind Source Separation Techniques for Multiply Labeled Fluorescence Images

Sarah Rich Sometimes in life things are complicated, and we just wish they were simpler! (Am I right, ladies?) A standard approach of theoreticians is to just pretend that they *are* simpler and keep going! We’ll consider this approach in the context of models for large empirical networks, like social …

A Well-Tempered Landscape for Non-convex Robust Subspace Recovery & Adaptive Sampling for Coarse Ranking

We present a mathematical analysis of a non-convex energy landscape for Robust Subspace Recovery. Under a deterministic condition, the only stationary point in a large neighborhood of an underlying subspace is the subspace itself. The same deterministic condition implies that a geodesic gradient descent method can exactly recover the underlying …