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Palo Alto, We Have a Problem. There Is No Oracle!

Amin Karbasi – Professor, Yale University

Thu, 17-Mar-2022 / 4:00pm / Zoom: https://stanford.zoom.us/meeting/register/tJckfuCurzkvEtKKOBvDCrPv3McapgP6HygJ

Talk

• Video

Abstract

Artificial intelligence is fundamentally about making decisions under uncertainty from a massive pool of possibilities, where combinatorial techniques have long been central tools. Indeed, many scientific and engineering models feature inherently discrete decision variables—from phrases in a corpus to objects in an image. Similarly, nearly all aspects of the machine learning pipeline involve discrete tasks from data summarization and sketching to feature selection and model explanation.

Classically, in order to design optimization methods, we usually assume that the objective function is either fully known or accessible via an oracle. In many modern applications, however, the objectives we aim to optimize should be rather learned, estimated, or simulated from data, a process that is subject to stochastic fluctuations. Moreover, it has long been known that solutions obtained from combinatorial optimization methods can demonstrate striking sensitivity to changes in the parameters of the underlying problem. So, what are the guarantees of the combinatorial algorithms we develop (and teach) when the perfect oracle does not exist? In this talk, we will address this challenge and build a fundamentally new connection between discrete and (non-convex) continuous optimizations that aim to lift the current provable methods out of the sterile lab environment and scale them into the real world.

Bio

Amin Karbasi is currently an (untenured) associate professor of Electrical Engineering, Computer Science, and Statistics & Data Science at Yale University. He is also a research scientist at Google NY. He has been the recipient of the National Science Foundation (NSF) Career Award, Office of Naval Research (ONR) Young Investigator Award, Air Force Office of Scientific Research (AFOSR) Young Investigator Award, DARPA Young Faculty Award, National Academy of Engineering (NAE) Grainger Award, Nokia Bell-Labs Prize, Amazon Research Award, Google Faculty Research Award, Microsoft Azure Research Award, Simons Research Fellowship, and ETH Research Fellowship. His work on machine learning, statistics, and signal processing has received awards in a number of premier conferences and journals, including Medical Image Computing and Computer-Assisted Interventions Conference (MICCAI), Facebook-MAIN award from AI-Neuroscience symposium, International Conference on Artificial Intelligence and Statistics (AISTATS), IEEE Communications Society Data Storage, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), ACM SIGMETRICS, and IEEE International Symposium on Information Theory (ISIT). His Ph.D. work received the Patrick Denantes Memorial Prize for the best doctoral thesis from the School of Computer and Communication Sciences at EPFL, Switzerland.