Search Results for author: David Kempe

Found 13 papers, 1 papers with code

Fairness in Matching under Uncertainty

no code implementations8 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.


Active Learning for Non-Parametric Choice Models

no code implementations5 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.

Active Learning

Plurality Veto: A Simple Voting Rule Achieving Optimal Metric Distortion

no code implementations14 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.

Networked Restless Multi-Armed Bandits for Mobile Interventions

no code implementations28 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.

Multi-Armed Bandits

Fairness in Ranking under Uncertainty

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 $\phi \rho$.

Decision Making Fairness

Altruism Design in Networked Public Goods Games

no code implementations2 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.

Decision Making

Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds

no code implementations7 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).

PAC learning

Inducing Equilibria in Networked Public Goods Games through Network Structure Modification

no code implementations25 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

The Complexity of Interactively Learning a Stable Matching by Trial and Error

no code implementations18 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.


On the Distortion of Voting with Multiple Representative Candidates

no code implementations21 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).

A General Framework for Robust Interactive Learning

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.


Of the People: Voting Is More Effective with Representative Candidates

no code implementations4 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.

Learning Influence Functions from Incomplete Observations

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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