no code implementations • 30 May 2023 • Vasilis Gkatzelis, Mohamad Latifian, Nisarg Shah
The input to the voting rule is each agent's ranking of the alternatives from most to least preferred, yet the agents have more refined (cardinal) preferences that capture the intensity with which they prefer one alternative over another.
1 code implementation • 1 Jul 2022 • Sauradip Nag, Nisarg Shah, Anran Qi, Raghavendra Ramachandra
Unlike previous methods, we model the depth estimation of the unobserved frame as a view-synthesis problem, which treats the depth estimate of the unseen video frame as an auxiliary task while synthesizing back the views using learned pose.
no code implementations • 15 Jul 2021 • Rupert Freeman, Evi Micha, Nisarg Shah
We introduce a new model for two-sided matching which allows us to borrow popular fairness notions from the fair division literature such as envy-freeness up to one good and maximin share guarantee.
no code implementations • 20 May 2021 • Daniel Halpern, Nisarg Shah
We study the fundamental problem of allocating indivisible goods to agents with additive preferences.
no code implementations • 19 May 2021 • Hadi Hosseini, Debmalya Mandal, Nisarg Shah, Kevin Shi
A clever recent approach, \emph{surprisingly popular voting}, elicits additional information from the individuals, namely their \emph{prediction} of other individuals' votes, and provably recovers the ground truth even when experts are in minority.
no code implementations • 17 Jul 2020 • Hadi Hosseini, Vijay Menon, Nisarg Shah, Sujoy Sikdar
We study the classical problem of matching $n$ agents to $n$ objects, where the agents have ranked preferences over the objects.
no code implementations • NeurIPS 2021 • Safwan Hossain, Evi Micha, Nisarg Shah
Unlike the classical multi-armed bandit problem, the goal is not to learn the "best arm"; indeed, each agent may perceive a different arm to be the best for her personally.
no code implementations • 16 Apr 2020 • Vasilis Gkatzelis, Daniel Halpern, Nisarg Shah
We study the following metric distortion problem: there are two finite sets of points, $V$ and $C$, that lie in the same metric space, and our goal is to choose a point in $C$ whose total distance from the points in $V$ is as small as possible.
no code implementations • NeurIPS 2019 • Debmalya Mandal, Ariel D. Procaccia, Nisarg Shah, David Woodruff
We take an unorthodox view of voting by expanding the design space to include both the elicitation rule, whereby voters map their (cardinal) preferences to votes, and the aggregation rule, which transforms the reported votes into collective decisions.
no code implementations • 27 May 2018 • Yiling Chen, Chara Podimata, Ariel D. Procaccia, Nisarg Shah
This paper is part of an emerging line of work at the intersection of machine learning and mechanism design, which aims to avoid noise in training data by correctly aligning the incentives of data sources.
no code implementations • NeurIPS 2015 • Ariel D. Procaccia, Nisarg Shah
Some crowdsourcing platforms ask workers to express their opinions by approving a set of k good alternatives.
no code implementations • NeurIPS 2014 • Albert Jiang, Leandro Soriano Marcolino, Ariel D. Procaccia, Tuomas Sandholm, Nisarg Shah, Milind Tambe
We investigate the power of voting among diverse, randomized software agents.