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DTSTART;TZID=America/Los_Angeles:20220511T143000
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CREATED:20250904T173748Z
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UID:7345-1652279400-1652283000@tilos.ai
SUMMARY:TILOS-OPTML++ Seminar: Constant Regret in Online Decision-Making
DESCRIPTION:Siddhartha Banerjee\, Cornell University \nAbstract: I will present a class of finite-horizon control problems\, where we see a random stream of arrivals\, need to select actions in each step\, and where the final objective depends only on the aggregate type-action counts; this includes many widely-studied control problems including online resource-allocation\, dynamic pricing\, generalized assignment\, online bin packing\, and bandits with knapsacks. For such settings\, I will introduce a unified algorithmic paradigm\, and provide a simple yet general condition under which these algorithms achieve constant regret\, i.e.\, additive loss compared to the hindsight optimal solution which is independent of the horizon and state-space. These results stem from an elementary coupling argument\, which may prove useful for many other questions in online decision-making. Time permitting\, I will illustrate this by showing how we can use this technique to incorporate side information and historical data in these settings\, and achieve constant regret with as little as a single data trace.
URL:https://tilos.ai/event/tilos-optml-seminar-constant-regret-in-online-decision-making/
LOCATION:Virtual
CATEGORIES:TILOS - OPTML++ Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2023/09/banerjee-siddhartha-e1757007458539.jpg
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