Search Results for author: Nisarg Shah

Found 12 papers, 1 papers with code

Best of Both Distortion Worlds

no code implementations30 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.

How Far Can I Go ? : A Self-Supervised Approach for Deterministic Video Depth Forecasting

1 code implementation1 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.

Depth Estimation Pose Estimation +1

Two-Sided Matching Meets Fair Division

no code implementations15 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.

Fairness Vocal Bursts Valence Prediction

Fair and Efficient Resource Allocation with Partial Information

no code implementations20 May 2021 Daniel Halpern, Nisarg Shah

We study the fundamental problem of allocating indivisible goods to agents with additive preferences.

Fairness

Surprisingly Popular Voting Recovers Rankings, Surprisingly!

no code implementations19 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.

Necessarily Optimal One-Sided Matchings

no code implementations17 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.

Fair Algorithms for Multi-Agent Multi-Armed Bandits

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.

Fairness Multi-Armed Bandits

Resolving the Optimal Metric Distortion Conjecture

no code implementations16 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.

LEMMA

Efficient and Thrifty Voting by Any Means Necessary

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.

Strategyproof Linear Regression in High Dimensions

no code implementations27 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.

regression Vocal Bursts Intensity Prediction

Is Approval Voting Optimal Given Approval Votes?

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.

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