Exploration with Limited Memory: Streaming Algorithms for Coin Tossing, Noisy Comparisons, and Multi-Armed Bandits
Wednesday, March 11, 2020, 11:00am - 12:00pm
Speaker: Chen Wang (Rutgers)
Location : CoRE 431
Event Type: Seminar
Abstract: Consider the following abstract coin tossing problem: Given a set of n coins with unknown biases, find the most biased coin using a minimal number of coin tosses. This is a common abstraction of various exploration problems in theoretical computer science and machine learning and has been studied extensively over the years. In particular, algorithms with optimal sample complexity (number of coin tosses) have been known for this problem for quite some time. Motivated by applications to processing massive datasets, we study the space complexity of solving this problem with optimal number of coin tosses in the streaming model. We will present an algorithm for this problem that simultaneously achieve asymptotically optimal sample complexity and space complexity which turns out to be surprisingly small. Furthermore, we extend our results to the problem of finding the k most biased coins as well as other exploration problems such as finding top-k elements using noisy comparisons or finding an ε-best arm in stochastic multi-armed bandits, and obtain efficient streaming algorithms for these problems. Join work with Sepehr Assadi.
DIMACS/Rutgers Theory of Computing Seminar