Communications and Signal Processing Seminar

Online Decision-Making using Prediction Oracles

Siddhartha BanerjeeAssistant ProfessorCornell University, School of Operations Research and Information Engineering (ORIE)

The online allocation of scarce resources is one of the canonical problems in many fields of engineering. In this talk, I will re-examine basic online resource allocation, with the aim of building bridges between these problems and the ever-improving predictions provided my modern machine-learning methods. To this end, I will present a new Bayesian-learning inspired algorithm for online stochastic packing problems which achieves the first horizon and budget independent regret bounds for these settings. Surprisingly, the result stems from elementary underlying tools – LP sensitivity and basic concentration of measures.
Sid Banerjee is an assistant professor in the School of Operations Research and Information Engineering (ORIE) at Cornell, and a technical consultant at Lyft. His research is on stochastic modeling and control, and the design of algorithms and incentives for large-scale systems. He got his PhD in ECE from UT Austin, following which he was a postdoctoral researcher in the Social Algorithms Lab at Stanford. His work is supported by an NSF CAREER award, as well as grants from the NSF and ARL.

Sponsored by


Faculty Host

Vijay Subramanian