Assistant Professor, Allen School of Computer Science & Engineering
https://homes.cs.washington.edu/~jamieson/about.html
Abstract: Optimizing subsets of items arises in many contexts, from designing antibiotic cocktails, to bundling cable channels or streaming services, to selecting the tap list at a pub. Such problems often exhibit diminishing returns: adding a third antibiotic may improve efficacy, but not as much as adding the second. Prior …
Today, machine learning is responsible for most of what we perceive as the personalization of the web: automatic recommendations for movies (Netflix) or music (Spotify,Last.fm), personalized search results based on your recent searches or email (Google), automatic credit card fraud detection (Chase), social network friend identification (Facebook,Linked-in), and, of course, …
Kevin: This work provides lower bounds on the convergence rate of Derivative Free Optimization (DFO) with noisy function evaluations, exposing a fundamental and unavoidable gap between the performance of algorithms with access to gradients and those with access to only function evaluations. However, there are situations in which DFO is …
Title: Active Ranking using Pairwise Comparisons by Kevin Jamieson This talk examines the problem of ranking a collection of objects using pairwise comparisons (rankings of two objects). In general, the ranking of n objects can be identified by standard sorting methods using n log2 n pairwise comparisons. We are interested …
Adaptive experimental design (AED), or active learning, leverages already-collected data to guide future measurements, in a closed loop, to collect the most informative data for the learning problem at hand. In both theory and practice, AED can extract considerably richer insights than any measurement plan fixed in advance, using the …