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UID:686@lincs.fr
DTSTART;TZID=Europe/Paris:20220330T150000
DTEND;TZID=Europe/Paris:20220330T160000
DTSTAMP:20220330T152727Z
URL:https://www.lincs.fr/events/seminar-talk-by-prof-ravi-mazumdar/
SUMMARY:Learning Optimal Bids in Second Price Auctions with Temporal and
 Overlapping Targeting Constraints
DESCRIPTION:Ad placement in web-browsing and wireless mobiles is an
 increasingly important component of the advertisement market. The market
 size is over $ 100 billion and counting. The mechanism is as follows: when
 a user opens a webpage or mobile ap a message is sent to an exchange where
 multiple bidders have the possibility of placing an ad that would target
 the right user\, eg. age\, sex\, location\, etc. The ad that is displayed
 corresponds to the bidder who bids the highest while the cost is calculated
 according to a first or second price. Typically bidders are DSP (Demand
 Side Platforms) that aggregate bids on behalf of clients who wish to run a
 campaign for a given length of time with certain targeting criteria. The
 goal is to minimize the total cost of obtaining the required number of
 impressions (ads that meet targeting criteria) over the duration of a
 contract. The real time constraint is that bidding must be done within
 100ms.\n\nIn this talk I will build upon the theory that we had earlier
 developed for computing the least cost bids in the second price context.
 This involves the notion of an information state for the problem. There is
 a very rich primal-dual theory that emerges\, one in the so called
 impressions space and the other in the contracts space. Computationally and
 structurally the primal and dual views of the optimization have different
 properties that can be exploited to come up with fast algorithms.\n\nThe
 optimal solutions depend on solving a constrained convex optimization
 problem when the information state is known. However this is not readily
 available and thus there is the problem of learning the information state.
 We show that in the second price case\, stochastic approximation (SA)
 algorithms that operate on censored data (prices are only known by a bidder
 when the bidder wins) can be devised that solve the constrained
 optimization problem without learning the information state explicitly and
 we prove their convergence. Finally I will present the dynamic behaviour
 through simulations.\n\nJoint work with Ryan Kinnear (Waterloo) and Peter
 Marbach (Toronto). We thank Addictive Mobility Inc.\, a Pelmorex company
 for having proposed the problem and to Addictive Mobility\, Ontario OCE VIP
 II\, and NSERC funding the work.\nBiography:\nThe speaker was educated at
 the Indian Institute of Technology\, Bombay (B.Tech\, 1977)\, Imperial
 College\, London (MSc\, DIC\, 1978) and obtained his PhD in Control Theory
 under A. V. Balakrishnan at UCLA in 1983. He is currently a University
 Research Chair Professor in the Dept. of ECE at the University of
 Waterloo\, Ont.\, Canada where he has been since September 2004. Prior to
 this he was Professor of ECE at Purdue University\, West Lafayette\, USA.
 Since 2012 he is a D.J. Gandhi Distinguished Visiting Professor at the
 Indian Institute of Technology\, Bombay\, India and since May 2019 an
 Adjunct Professor at the Tata Institute of Fundamental Research (TIFR)\,
 Mumbai. He is a Fellow of the IEEE and the Royal Statistical Society. He is
 a recipient of the INFOCOM 2006 Best Paper Award\, the ITC-27 2015 Best
 Paper Award\, the Performance 2015 Best Paper Award and was runner-up for
 the Best Paper Award at INFOCOM 1998. His research interests are in
 stochastic modelling and analysis applied to complex networks and
 statistical inference.
CATEGORIES:Seminars,Youtube
LOCATION:LINCS + Zoom\, 23 avenue d'Italie\, Paris\, 75013\, France
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 Paris\, 75013\, France;X-APPLE-RADIUS=100;X-TITLE=LINCS + Zoom:geo:0,0
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DTSTART:20220327T030000
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