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Found 15 papers, 1 papers with code
Stability and Multigroup Fairness in Ranking with Uncertain Predictions
However, when predictors trained for classification tasks have intrinsic uncertainty, it is not obvious how this uncertainty should be represented in the derived rankings.
Proportional Representation in Metric Spaces and Low-Distortion Committee Selection
Given a set V (e. g., documents or voters) to represent, and a set C of possible representatives, our criterion requires that for any subset S comprising a theta fraction of V, the average distance of S to their best theta*k points in R should not be more than a factor gamma compared to their average distance to the best theta*k points among all of C. This definition is a strengthening of proportional fairness and core fairness, but - different from those notions - requires that large cohesive clusters be represented proportionally to their size.
Fairness in Matching under Uncertainty
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
To overcome the identifiability problem, we introduce a directed acyclic graph (DAG) representation of the choice model.
Plurality Veto: A Simple Voting Rule Achieving Optimal Metric Distortion
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
Motivated by a broad class of mobile intervention problems, we propose and study restless multi-armed bandits (RMABs) with network effects.
Fairness in Ranking under Uncertainty
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 $\phi \rho$.
Altruism Design in Networked Public Goods Games
Many collective decision-making settings feature a strategic tension between agents acting out of individual self-interest and promoting a common good.
Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds
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).
Inducing Equilibria in Networked Public Goods Games through Network Structure Modification
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
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
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
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
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
We establish both proper and improper PAC learnability of influence functions under randomly missing observations.
