Saeed Alaei
I am a staff research scientist in the market algorithms team in Mountain View. I received my Ph.D
in Computer Science from University of Maryland - College Park. I was a post doctoral
research associate at Cornell University prior to joining Google. My research interests
include mechanism design/algorithmic game theory, combinatorial and convex optimization,
and online algorithms. The focus of my research is on developing general algorithms and
techniques for optimization problems involving strategic agents arising in online markets.
Authored Publications
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Response Prediction for Low-Regret Agents
Ashwinkumar Badanidiyuru Varadaraja
Sadra Yazdanbod
Web and Internet Economics 2019
Preview abstract
Companies like Google and Microsoft run billions of auctions every day to sell advertising opportunities. Any change to the rules of these auctions can have a tremendous effect on the revenue of the company and the welfare of the advertisers and the users. Therefore, any change requires careful evaluation of its potential impacts. Currently, such impacts are often evaluated by running simulations or small controlled experiments. This, however, misses the important factor that the advertisers respond to changes. Our goal is to build a theoretical framework for predicting the actions of an agent (the advertiser) that is optimizing her actions in an uncertain environment. We model this problem using a variant of the multi armed bandit setting where playing an arm is costly. The cost of each arm changes over time and is publicly observable. The value of playing an arm is drawn stochastically from a static distribution and is observed by the agent and not by us. We, however, observe the actions of the agent. Our main result is that assuming the agent is playing a strategy with a regret of at most f(T) within the first T rounds, we can learn to play the multi-armed bandits game without observing the rewards) in such a way that the regret of our selected actions is at most O(k^4 (f(T) + 1) log(T)).
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