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UID:www.tcs.tifr.res.in/event/1717
DTSTAMP:20260511T053704Z
SUMMARY:Adversarial Hypothesis Testing: Channel Estimation\, Sequentiality 
 and Robustness
DESCRIPTION:Speaker: Eeshan Modak (TIFR)\n\nAbstract: \n\nIn this thesis\, 
 we study the following problems:\nAdversarial hypothesis testing is a mode
 l for problems where the observed data is not independent and identically 
 distributed according to a fixed distribution. The samples could instead c
 ome from distributions arbitrarily chosen by an adversary. We show how seq
 uential tests can obtain a strictly better performance compared to fixed l
 ength tests in this setting.\nArbitrarily Varying Channels (AVCs) model ch
 annels which can vary with time in an arbitrary way during the transmissio
 n. We study the problem of distinguishing between two AVCs where the trans
 mitter (i) is deterministic\, (ii) may privately randomize\, and (iii) sha
 res randomness with the detector.\n In many practical hypothesis testing 
 problems\, our hypotheses might not exactly model the observed data. In su
 ch a situation\, we would like our test to output the hypothesis which is 
 closer to the true distribution of the underlying data. It turns out that 
 this is possible only when the hypotheses are not too close. We give a low
 er bound on the optimal separation when the closeness is measured in terms
  of the Hellinger distance.\nObtaining bounds on the expected generalizati
 on error of a machine learning algorithm is an important problem. We obtai
 n a family of Rényi divergence-based bounds that recover some of the exis
 ting bounds as a special case. Also\, for certain values of the Rényi par
 ameter\, they can be tighter than the existing bounds.\n
URL:https://www.tcs.tifr.res.in/web/events/1717
DTSTART;TZID=Asia/Kolkata:20260529T103000
DTEND;TZID=Asia/Kolkata:20260529T113000
LOCATION:A-201 (STCS Seminar Room)
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