Talks & Seminars
Title: Hashing Meets Statistical Estimation and Inference: Adaptive Sampling at the Cost of Random Sampling
Prof. Anshumali Shrivastava, Rice University
Date & Time: June 28, 2019 14:30
Venue: Department of Computer Science and Engineering, Room No. 109, 01st Floor, New CSE/CC Building
Sampling is one of the fundamental hammers in machine learning (ML) for reducing the size and scale of the problem at hand. Many ML applications demand adaptive sampling for faster convergence. However, the cost of sampling itself is prohibitive, which creates a fundamental barrier. In this talk, I will demonstrate how hashing algorithms naturally breaks this barrier leading to efficient machine learning algorithms. I will discuss some of my recent and surprising findings on the use of hashing algorithms for large-scale estimations. Locality Sensitive Hashing (LSH) is a hugely popular algorithm for sub-linear near neighbor search. However, it turns out that fundamentally LSH is a constant time (amortized) adaptive sampler from which efficient near-neighbor search is one of the many possibilities. LSH offers a unique capability to do smart sampling and statistical estimations at the cost of few hash lookups. Our observation bridges data structures (probabilistic hash tables) with efficient unbiased statistical estimations. I will demonstrate how this dynamic and efficient sampling beak the computational barriers in adaptive estimations, where it is possible that we pay roughly the cost of uniform sampling but get the benefits of adaptive sampling. I will demonstrate the power of a straightforward idea for a variety of problems 1) Adaptive Gradient Estimations for efficient SGD, 2) Efficient Deep Learning, 3) Anomaly Detection, and 4) The first possibility of sub-linear sketches for near-neighbor queries
Speaker Profile:
Anshumali Shrivastava is an assistant professor in the computer science department at Rice University. His broad research interests include randomized algorithms for large-scale machine learning. In 2018, Science news named him one of the Top-10 scientists under 40 to watch. He is a recipient of National Science Foundation CAREER Award, a Young Investigator Award from Air Force Office of Scientific Research, and machine learning research award from Amazon. His research on hashing inner products has won Best Paper Award at NIPS 2014 while his work on representing graphs got the Best Paper Award at IEEE/ACM ASONAM 2014. Anshumali finished his Ph. D. in 2015 from Cornell University.
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