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Deciding what to sense is a crucial task, made harder by dependencies and by a nonadditive utility function. We develop approximation algorithms for selecting an optimal set of measurements, under a dependency structure modeled by a…

Artificial Intelligence · Computer Science 2012-06-18 Yan Radovilsky , Solomon Eyal Shimony

We study the collaborative PAC learning problem recently proposed in Blum et al.~\cite{BHPQ17}, in which we have $k$ players and they want to learn a target function collaboratively, such that the learned function approximates the target…

Machine Learning · Computer Science 2018-10-15 Jiecao Chen , Qin Zhang , Yuan Zhou

In this work we extend the class of Consensus-Based Optimization (CBO) metaheuristic methods by considering memory effects and a random selection strategy. The proposed algorithm iteratively updates a population of particles according to a…

Optimization and Control · Mathematics 2023-08-16 Giacomo Borghi , Sara Grassi , Lorenzo Pareschi

Platforms for online civic participation rely heavily on methods for condensing thousands of comments into a relevant handful, based on whether participants agree or disagree with them. These methods should guarantee fair representation of…

Computer Science and Game Theory · Computer Science 2023-12-25 Daniel Halpern , Gregory Kehne , Ariel D. Procaccia , Jamie Tucker-Foltz , Manuel Wüthrich

We revisit the coalition structure generation problem in which the goal is to partition the players into exhaustive and disjoint coalitions so as to maximize the social welfare. One of our key results is a general polynomial-time algorithm…

Computer Science and Game Theory · Computer Science 2011-06-21 Haris Aziz , Bart de Keijzer

We consider elections where the voters come one at a time, in a streaming fashion, and devise space-efficient algorithms which identify an approximate winning committee with respect to common multiwinner proportional representation voting…

Computer Science and Game Theory · Computer Science 2017-03-01 Palash Dey , Nimrod Talmon , Otniel van Handel

We study multiwinner elections with approval-based preferences. An instance of a multiwinner election consists of a set of alternatives, a population of voters---each voter approves a subset of alternatives, and the desired committee size…

Computer Science and Game Theory · Computer Science 2019-10-15 Piotr Skowron

We consider the problem of cost-optimal utilization of a crowdsourcing platform for binary, unsupervised classification of a collection of items, given a prescribed error threshold. Workers on the crowdsourcing platform are assumed to be…

Machine Learning · Computer Science 2022-07-06 Yashvardhan Didwania , Jayakrishnan Nair , N. Hemachandra

We study the problem of a budget limited buyer who wants to buy a set of items, each from a different seller, to maximize her value. The budget feasible mechanism design problem aims to design a mechanism which incentivizes the sellers to…

Computer Science and Game Theory · Computer Science 2017-04-03 Pooya Jalaly , Eva Tardos

We explore a multiple-stage variant of the min-max robust selection problem with budgeted uncertainty that includes queries. First, one queries a subset of items and gets the exact values of their uncertain parameters. Given this…

Optimization and Control · Mathematics 2025-01-07 Xiaoyu Chen , Marc Goerigk , Michael Poss

We initiate the study of bribery problem in the context of gerrymandering and reverse gerrymandering. In our most general problem, the input is a set of voters having votes over a set of alternatives, a graph on the voters, a partition of…

Computer Science and Game Theory · Computer Science 2019-09-05 Palash Dey

Real-world experiments involve batched & delayed feedback, non-stationarity, multiple objectives & constraints, and (often some) personalization. Tailoring adaptive methods to address these challenges on a per-problem basis is infeasible,…

Machine Learning · Computer Science 2024-11-11 Ethan Che , Daniel R. Jiang , Hongseok Namkoong , Jimmy Wang

Facing a heavy task, any single person can only make a limited contribution and team cooperation is needed. As one enjoys the benefit of the public goods, the potential benefits of the project are not always maximized and may be partly…

Physics and Society · Physics 2017-06-28 Li-Xin Zhong , Wen-Juan Xu , Yun-Xin He , Chen-Yang Zhong , Rong-Da Chen , Tian Qiu , Yong-Dong Shi , Fei Ren

When confronted with a host of issues, groups often save time and energy by compiling many issues into a single bundle when making decisions. This reduces the time and cost of group decision-making, but it also leads to suboptimal outcomes…

Physics and Society · Physics 2023-07-27 Matthew I. Jones , Matthew Chervenak , Nicholas A. Christakis

We introduce the heterogeneous voter model (HVM), in which each agent has its own intrinsic rate to change state, reflective of the heterogeneity of real people, and the partisan voter model (PVM), in which each agent has an innate and…

Physics and Society · Physics 2010-08-04 Naoki Masuda , N. Gibert , S. Redner

Shortlisting of candidates--selecting a group of "best" candidates--is a special case of multiwinner elections. We provide the first in-depth study of the computational complexity of strategic voting for shortlisting based on the perhaps…

Multiagent Systems · Computer Science 2019-08-15 Robert Bredereck , Andrzej Kaczmarczyk , Rolf Niedermeier

Crowdsourcing platforms offer a way to label data by aggregating answers of multiple unqualified workers. We introduce a \textit{simple} and \textit{budget efficient} crowdsourcing method named Proxy Crowdsourcing (PCS). PCS collects…

Computer Science and Game Theory · Computer Science 2018-06-19 Gal Cohensius , Omer Ben Porat , Reshef Meir , Ofra Amir

We describe the PArticipatory BUdgeting LIBrary website (in short, Pabulib), which can be accessed via http://pabulib.org/, and which is a library of participatory budgeting data. In particular, we describe the file format (.pb) that is…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-12-14 Dariusz Stolicki , Stanisław Szufa , Nimrod Talmon

Machine learning models are often personalized with information that is protected, sensitive, self-reported, or costly to acquire. These models use information about people but do not facilitate nor inform their consent. Individuals cannot…

Machine Learning · Computer Science 2023-10-13 Hailey Joren , Chirag Nagpal , Katherine Heller , Berk Ustun

The study of phase transition phenomenon of NP complete problems plays an important role in understanding the nature of hard problems. In this paper, we follow this line of research by considering the problem of counting solutions of…

Artificial Intelligence · Computer Science 2011-02-25 Minghao Yin , Ping Huang
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