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UID:www.tcs.tifr.res.in/event/1286
DTSTAMP:20230914T125957Z
SUMMARY:Dual Mirror Descent for Online Allocation Problems
DESCRIPTION:Speaker: Agniv Bandyopadhyay\n\nAbstract: \nWe will look at the
  situation where an agent decides what action to take sequentially over a 
 finite time horizon to maximize revenue subject to resource consumption co
 nstraints. Every time the revenue generated will be some concave function 
 of the action picked. Also\, the revenue function and resource consumption
  will be sampled i.i.d. from some fixed distribution every time\, and that
  distribution will be unknown to the agent.  Balseiro\, Hu\, and Mirrokni
  (2020) introduced a general class of algorithms for this problem\, which 
 achieves sublinear regret. The algorithm maintains a dual multiplier at ev
 ery time step and uses online mirror descent to update it.  Depending on 
 the regularizer chosen\, the policy can be equivalent to dual sub-gradient
  descent and dual-multiplicative weight update. Moreover\, this framework 
 can achieve sublinear regret in repeated second-price auctions where\, unl
 ike the online allocation problem\, bidders are unaware of their revenue f
 unctions and resource consumption (payment) before deciding their bid.\n\n
 Our discussion will be based on the contents of the following paper:\n"Dua
 l Mirror Descent for Online Allocation Problems"-Santiago Balseiro\, Haiha
 o Lu\, Vahab Mirrokni\, In: Proceedings of the 37th International Conferen
 ce on Machine Learning\, PMLR 119:613-628\, 2020\n
URL:https://www.tcs.tifr.res.in/web/events/1286
DTSTART;TZID=Asia/Kolkata:20230421T160000
DTEND;TZID=Asia/Kolkata:20230421T170000
LOCATION:A201
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