Jennifer Wortman
University of Pennsylvania
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Publication
Featured researches published by Jennifer Wortman.
adaptive agents and multi-agents systems | 2004
Eugene Nudelman; Jennifer Wortman; Yoav Shoham; Kevin Leyton-Brown
We present GAMUT^1, a suite of game generators designed for testing game-theoretic algorithms. We explain why such a generator is necessary, offer a way of visualizing relationships between the sets of games supported by GAMUT, and give an overview of GAMUTýs architecture. We highlight the importance of using comprehensive test data by benchmarking existing algorithms. We show surprisingly large variation in algorithm performance across different sets of games for two widely-studied problems: computing Nash equilibria and multiagent learning in repeated games.
Proceedings of the National Academy of Sciences of the United States of America | 2009
Michael J. Kearns; Stephen Judd; Jinsong Tan; Jennifer Wortman
Many distributed collective decision-making processes must balance diverse individual preferences with a desire for collective unity. We report here on an extensive session of behavioral experiments on biased voting in networks of individuals. In each of 81 experiments, 36 human subjects arranged in a virtual network were financially motivated to reach global consensus to one of two opposing choices. No payments were made unless the entire population reached a unanimous decision within 1 min, but different subjects were paid more for consensus to one choice or the other, and subjects could view only the current choices of their network neighbors, thus creating tensions between private incentives and preferences, global unity, and network structure. Along with analyses of how collective and individual performance vary with network structure and incentives generally, we find that there are well-studied network topologies in which the minority preference consistently wins globally; that the presence of “extremist” individuals, or the awareness of opposing incentives, reliably improve collective performance; and that certain behavioral characteristics of individual subjects, such as “stubbornness,” are strongly correlated with earnings.
international conference on machine learning | 2008
John Langford; Alexander L. Strehl; Jennifer Wortman
We examine the problem of evaluating a policy in the contextual bandit setting using only observations collected during the execution of another policy. We show that policy evaluation can be impossible if the exploration policy chooses actions based on the side information provided at each time step. We then propose and prove the correctness of a principled method for policy evaluation which works when this is not the case, even when the exploration policy is deterministic, as long as each action is explored sufficiently often. We apply this general technique to the problem of offline evaluation of internet advertising policies. Although our theoretical results hold only when the exploration policy chooses ads independent of side information, an assumption that is typically violated by commercial systems, we show how clever uses of the theory provide non-trivial and realistic applications. We also provide an empirical demonstration of the effectiveness of our techniques on real ad placement data.
electronic commerce | 2008
Yiling Chen; Lance Fortnow; Nicolas S. Lambert; David M. Pennock; Jennifer Wortman
We analyze the computational complexity of market maker pricing algorithms for combinatorial prediction markets. We focus on Hansons popular logarithmic market scoring rule market maker (LMSR). Our goal is to implicitly maintain correct LMSR prices across an exponentially large outcome space. We examine both permutation combinatorics, where outcomes are permutations of objects, and Boolean combinatorics, where outcomes are combinations of binary events. We look at three restrictive languages that limit what traders can bet on. Even with severely limited languages, we find that LMSR pricing is #P-hard, even when the same language admits polynomial-time matching without the market maker. We then propose an approximation technique for pricing permutation markets based on an algorithm for online permutation learning. The connections we draw between LMSR pricing and the literature on online learning with expert advice may be of independent interest.
Algorithmica | 2010
John Langford; Lihong Li; Yevgeniy Vorobeychik; Jennifer Wortman
We introduce an exploration scheme aimed at learning advertiser click-through rates in sponsored search auctions with minimal effect on advertiser incentives. The scheme preserves both the current ranking and pricing policies of the search engine and only introduces one set of parameters which control the rate of exploration. These parameters can be set so as to allow enough exploration to learn advertiser click-through rates over time, but also eliminate incentives for advertisers to alter their currently submitted bids. When advertisers have much more information than the search engine, we show that although this goal is not achievable, incentives to deviate can be made arbitrarily small by appropriately setting the exploration rate. Given that advertisers do not alter their bids, we bound revenue loss due to exploration.
Machine Learning | 2008
Eyal Even-Dar; Michael J. Kearns; Yishay Mansour; Jennifer Wortman
AbstractWe study online regret minimization algorithms in an experts setting. In this setting, the algorithm chooses a distribution over experts at each time step and receives a gain that is a weighted average of the experts’ instantaneous gains. We consider a bicriteria setting, examining not only the standard notion of regret to the best expert, but also the regret to the average of all experts, the regret to any given fixed mixture of experts, or the regret to the worst expert. This study leads both to new understanding of the limitations of existing no-regret algorithms, and to new algorithms with novel performance guarantees. More specifically, we show that any algorithm that achieves only
electronic commerce | 2008
Nicolas S. Lambert; John Langford; Jennifer Wortman; Yiling Chen; Daniel M. Reeves; Yoav Shoham; David M. Penno k
O(\sqrt{T})
workshop on internet and network economics | 2007
Jennifer Wortman; Yevgeniy Vorobeychik; Lihong Li; John Langford
cumulative regret to the best expert on a sequence of T trials must, in the worst case, suffer regret
algorithmic learning theory | 2006
Eyal Even-Dar; Michael J. Kearns; Jennifer Wortman
\varOmega(\sqrt{T})
neural information processing systems | 2007
John Blitzer; Koby Crammer; Alex Kulesza; Fernando Pereira; Jennifer Wortman
to the average, and that for a wide class of update rules that includes many existing no-regret algorithms (such as Exponential Weights and Follow the Perturbed Leader), the product of the regret to the best and the regret to the average is, in the worst case, Ω(T). We then describe and analyze two alternate new algorithms that both achieve cumulative regret only