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no code implementations • 8 Feb 2023 • Siddartha Devic, David Kempe, Vatsal Sharan, Aleksandra Korolova

The prevalence and importance of algorithmic two-sided marketplaces has drawn attention to the issue of fairness in such settings.

no code implementations • 5 Aug 2022 • Fransisca Susan, Negin Golrezaei, Ehsan Emamjomeh-Zadeh, David Kempe

We design an efficient active-learning algorithm to estimate the DAG representation of the non-parametric choice model, which runs in polynomial time when the set of frequent rankings is drawn uniformly at random.

no code implementations • 14 Jun 2022 • Fatih Erdem Kizilkaya, David Kempe

We also generalize Plurality Veto into a class of randomized voting rules in the following way: Plurality veto is run only for k < n rounds; then, a candidate is chosen with probability proportional to his residual score.

no code implementations • 28 Jan 2022 • Han-Ching Ou, Christoph Siebenbrunner, Jackson Killian, Meredith B Brooks, David Kempe, Yevgeniy Vorobeychik, Milind Tambe

Motivated by a broad class of mobile intervention problems, we propose and study restless multi-armed bandits (RMABs) with network effects.

1 code implementation • NeurIPS 2021 • Ashudeep Singh, David Kempe, Thorsten Joachims

We call an algorithm $\phi$-fair (for $\phi \in [0, 1]$) if it has the following property for all agents $x$ and all $k$: if agent $x$ is among the top $k$ agents with respect to merit with probability at least $\rho$ (according to the posterior merit distribution), then the algorithm places the agent among the top $k$ agents in its ranking with probability at least $\phi \rho$.

no code implementations • 2 May 2021 • Sixie Yu, David Kempe, Yevgeniy Vorobeychik

Many collective decision-making settings feature a strategic tension between agents acting out of individual self-interest and promoting a common good.

no code implementations • 7 Mar 2020 • Ehsan Emamjomeh-Zadeh, Chen-Yu Wei, Haipeng Luo, David Kempe

We revisit the problem of online learning with sleeping experts/bandits: in each time step, only a subset of the actions are available for the algorithm to choose from (and learn about).

no code implementations • 25 Feb 2020 • David Kempe, Sixie Yu, Yevgeniy Vorobeychik

Networked public goods games model scenarios in which self-interested agents decide whether or how much to invest in an action that benefits not only themselves, but also their network neighbors.

Computer Science and Game Theory Multiagent Systems

no code implementations • 18 Feb 2020 • Ehsan Emamjomeh-Zadeh, Yannai A. Gonczarowski, David Kempe

In a stable matching setting, we consider a query model that allows for an interactive learning algorithm to make precisely one type of query: proposing a matching, the response to which is either that the proposed matching is stable, or a blocking pair (chosen adversarially) indicating that this matching is unstable.

no code implementations • 21 Nov 2017 • Yu Cheng, Shaddin Dughmi, David Kempe

Our main result is a clean and tight characterization of positional voting rules that have constant expected distortion (independent of the number of candidates and the metric space).

no code implementations • NeurIPS 2017 • Ehsan Emamjomeh-Zadeh, David Kempe

Our general framework is based on a graph representation of the models and user feedback.

no code implementations • 4 May 2017 • Yu Cheng, Shaddin Dughmi, David Kempe

However, we show that independence alone is not enough to achieve the upper bound: even when candidates are drawn independently, if the population of candidates can be different from the voters, then an upper bound of $2$ on the approximation is tight.

no code implementations • NeurIPS 2016 • Xinran He, Ke Xu, David Kempe, Yan Liu

We establish both proper and improper PAC learnability of influence functions under randomly missing observations.

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