Search Results for author: Ariel D. Procaccia

Found 25 papers, 4 papers with code

Axioms for AI Alignment from Human Feedback

no code implementations23 May 2024 Luise Ge, Daniel Halpern, Evi Micha, Ariel D. Procaccia, Itai Shapira, Yevgeniy Vorobeychik, Junlin Wu

The problem of learning a reward function is one of preference aggregation that, we argue, largely falls within the scope of social choice theory.

Generative Social Choice

1 code implementation3 Sep 2023 Sara Fish, Paul Gölz, David C. Parkes, Ariel D. Procaccia, Gili Rusak, Itai Shapira, Manuel Wüthrich

Traditionally, social choice theory has only been applicable to choices among a few predetermined alternatives but not to more complex decisions such as collectively selecting a textual statement.

Chatbot Language Modelling +1

The Distortion of Binomial Voting Defies Expectation

no code implementations NeurIPS 2023 Yannai A. Gonczarowski, Gregory Kehne, Ariel D. Procaccia, Ben Schiffer, Shirley Zhang

In computational social choice, the distortion of a voting rule quantifies the degree to which the rule overcomes limited preference information to select a socially desirable outcome.

Fair Sortition Made Transparent

no code implementations NeurIPS 2021 Bailey Flanigan, Gregory Kehne, Ariel D. Procaccia

Sortition is an age-old democratic paradigm, widely manifested today through the random selection of citizens' assemblies.

Fairness

District-Fair Participatory Budgeting

no code implementations11 Feb 2021 D Ellis Hershkowitz, Anson Kahng, Dominik Peters, Ariel D. Procaccia

On the other hand, decision making that only takes global social welfare into account can be unfair to districts: A social-welfare-maximizing solution might not fund any of the projects preferred by a district, despite the fact that its constituents pay taxes to the city.

Decision Making Fairness Computer Science and Game Theory Data Structures and Algorithms

Axioms for Learning from Pairwise Comparisons

no code implementations NeurIPS 2020 Ritesh Noothigattu, Dominik Peters, Ariel D. Procaccia

To be well-behaved, systems that process preference data must satisfy certain conditions identified by economic decision theory and by social choice theory.

Decision Making

The Phantom Steering Effect in Q&A Websites

2 code implementations14 Feb 2020 Nicholas Hoernle, Gregory Kehne, Ariel D. Procaccia, Kobi Gal

Moreover, we conduct a qualitative survey of the users on Stack Overflow which provides further evidence that the insights from the model reflect the true behavior of the community.

Paradoxes in Fair Machine Learning

1 code implementation NeurIPS 2019 Paul Goelz, Anson Kahng, Ariel D. Procaccia

Equalized odds is a statistical notion of fairness in machine learning that ensures that classification algorithms do not discriminate against protected groups.

BIG-bench Machine Learning Fairness +1

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.

Learning and Planning in the Feature Deception Problem

no code implementations13 May 2019 Zheyuan Ryan Shi, Ariel D. Procaccia, Kevin S. Chan, Sridhar Venkatesan, Noam Ben-Asher, Nandi O. Leslie, Charles Kamhoua, Fei Fang

In order to formally reason about deception, we introduce the feature deception problem (FDP), a domain-independent model and present a learning and planning framework for finding the optimal deception strategy, taking into account the adversary's preferences which are initially unknown to the defender.

Envy-Free Classification

no code implementations NeurIPS 2019 Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia

In classic fair division problems such as cake cutting and rent division, envy-freeness requires that each individual (weakly) prefer his allocation to anyone else's.

Classification Fairness +1

Loss Functions, Axioms, and Peer Review

no code implementations27 Aug 2018 Ritesh Noothigattu, Nihar B. Shah, Ariel D. Procaccia

The key challenge that arises is the specification of a loss function for ERM.

Fairly Allocating Many Goods with Few Queries

no code implementations30 Jul 2018 Hoon Oh, Ariel D. Procaccia, Warut Suksompong

For two agents with arbitrary monotonic utilities, we design an algorithm that computes an allocation satisfying envy-freeness up to one good (EF1), a relaxation of envy-freeness, using a logarithmic number of queries.

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

Collaborative PAC Learning

no code implementations NeurIPS 2017 Avrim Blum, Nika Haghtalab, Ariel D. Procaccia, Mingda Qiao

We introduce a collaborative PAC learning model, in which k players attempt to learn the same underlying concept.

PAC learning

The Provable Virtue of Laziness in Motion Planning

no code implementations11 Oct 2017 Nika Haghtalab, Simon Mackenzie, Ariel D. Procaccia, Oren Salzman, Siddhartha S. Srinivasa

The Lazy Shortest Path (LazySP) class consists of motion-planning algorithms that only evaluate edges along shortest paths between the source and target.

Robotics Data Structures and Algorithms

Why You Should Charge Your Friends for Borrowing Your Stuff

no code implementations20 May 2017 Kijung Shin, Euiwoong Lee, Dhivya Eswaran, Ariel D. Procaccia

We consider goods that can be shared with k-hop neighbors (i. e., the set of nodes within k hops from an owner) on a social network.

Small Representations of Big Kidney Exchange Graphs

no code implementations25 May 2016 John P. Dickerson, Aleksandr M. Kazachkov, Ariel D. Procaccia, Tuomas Sandholm

This growth results in more lives saved, but exacerbates the empirical hardness of the $\mathcal{NP}$-complete problem of optimally matching patients to donors.

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.

Learning Cooperative Games

1 code implementation30 Apr 2015 Maria-Florina Balcan, Ariel D. Procaccia, Yair Zick

This paper explores a PAC (probably approximately correct) learning model in cooperative games.

Computer Science and Game Theory

Learning Optimal Commitment to Overcome Insecurity

no code implementations NeurIPS 2014 Avrim Blum, Nika Haghtalab, Ariel D. Procaccia

Game-theoretic algorithms for physical security have made an impressive real-world impact.

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