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Responsible disclosure limitation is an iterative exercise in risk assessment and mitigation. From time to time, as disclosure risks grow and evolve and as data users' needs change, agencies must consider redesigning the disclosure…

The Naive Bayesian classifier is a popular classification method employing the Bayesian paradigm. The concept of having conditional dependence among input variables sounds good in theory but can lead to a majority vote style behaviour.…

Machine Learning · Computer Science 2024-12-10 Sanjay Vishwakarma , Srinjoy Ganguly

In constructing an econometric or statistical model, we pick relevant features or variables from many candidates. A coalitional game is set up to study the selection problem where the players are the candidates and the payoff function is a…

Machine Learning · Statistics 2021-10-07 Xingwei Hu

The Gibbard-Satterthwaite theorem states that no unanimous and non-dictatorial voting rule is strategyproof. We revisit voting rules and consider a weaker notion of strategyproofness called not obvious manipulability that was proposed by…

Computer Science and Game Theory · Computer Science 2022-06-15 Haris Aziz , Alexander Lam

This paper critically examines arguments against independence, a measure of group fairness also known as statistical parity and as demographic parity. In recent discussions of fairness in computer science, some have maintained that…

Computers and Society · Computer Science 2021-01-11 Tim Räz

We present a simple proof of a well-known axiomatic characterization of state-salient decision rules, using Weak Dominance Criterion and Global Independence of Irrelevant Alternatives. Subsequently we provide a simple axiomatic…

Optimization and Control · Mathematics 2025-03-11 Somdeb Lahiri

In real-world elections where voters cast preference ballots, voters often provide only a partial ranking of the candidates. Despite this empirical reality, prior social choice literature frequently analyzes fairness criteria under the…

General Economics · Economics 2024-08-08 Adam Graham-Squire , Matthew I. Jones , David McCune

In approval-based committee (ABC) voting, the goal is to choose a subset of predefined size of the candidates based on the voters' approval preferences over the candidates. While this problem has attracted significant attention in recent…

Computer Science and Game Theory · Computer Science 2023-12-15 Martin Bullinger , Chris Dong , Patrick Lederer , Clara Mehler

We propose a novel method for selective classification (SC), a problem which allows a classifier to abstain from predicting some instances, thus trading off accuracy against coverage (the fraction of instances predicted). In contrast to…

Machine Learning · Computer Science 2021-10-26 Aditya Gangrade , Anil Kag , Venkatesh Saligrama

Arrow's Impossibility Theorem states that any constitution which satisfies Independence of Irrelevant Alternatives (IIA) and Unanimity and is not a Dictator has to be non-transitive. In this paper we study quantitative versions of Arrow…

Probability · Mathematics 2009-10-05 Elchanan Mossel

When making simultaneous decisions, our preference for the outcomes on one subset can depend on the outcomes on a disjoint subset. In referendum elections, this gives rise to the separability problem, where a voter must predict the outcome…

Combinatorics · Mathematics 2020-06-08 Andrew Beveridge , Ian Calaway

Integrity of elections is vital to democratic systems, but it is frequently threatened by malicious actors. The study of algorithmic complexity of the problem of manipulating election outcomes by changing its structural features is known as…

Computer Science and Game Theory · Computer Science 2020-07-21 Andrew Estornell , Sanmay Das , Edith Elkind , Yevgeniy Vorobeychik

We consider an odd-sized "jury", which votes sequentially between two states of Nature (say A and B, or Innocent and Guilty) with the majority opinion determining the verdict. Jurors have private information in the form of a signal in…

Theoretical Economics · Economics 2021-10-12 Steve Alpern , Bo Chen

Learning causal relations from observational data is a fundamental problem with wide-ranging applications across many fields. Constraint-based methods infer the underlying causal structure by performing conditional independence tests.…

Machine Learning · Computer Science 2026-03-24 Marc Franquesa Monés , Jiaqi Zhang , Caroline Uhler

It is common that a jury must grade a set of candidates in a cardinal scale such as {1,2,3,4,5} or an ordinal scale such as {Great, Good, Average, Bad }. When the number of candidates is very large such as hotels (BOOKING), restaurants…

Computer Science and Game Theory · Computer Science 2023-02-24 Rida Laraki , Estelle Varloot

In voting contexts, some new candidates may show up in the course of the process. In this case, we may want to determine which of the initial candidates are possible winners, given that a fixed number $k$ of new candidates will be added. We…

Artificial Intelligence · Computer Science 2015-02-17 Yann Chevaleyre , Jérôme Lang , Nicolas Maudet , Jérôme Monnot , Lirong Xia

In the context of computational social choice, we study voting methods that assign a set of winners to each profile of voter preferences. A voting method satisfies the property of positive involvement (PI) if for any election in which a…

Computer Science and Game Theory · Computer Science 2021-06-23 Wesley H. Holliday , Eric Pacuit

In an approval-based committee election, the task is to select a committee of up to $k$ candidates from a set of $m$ candidates based on the preferences of $n$ voters, each of whom approves a subset of the candidates. A central open…

Computer Science and Game Theory · Computer Science 2026-05-08 Patrick Becker , Matthias Greger , Dominik Peters

We introduce isotonic conditional laws (ICL) which extend the classical notion of conditional laws by the additional requirement that there exists an isotonic relationship between the random variable of interest and the conditioning random…

Statistics Theory · Mathematics 2024-03-13 Sebastian Arnold , Johanna Ziegel

We clarify what fairness guarantees we can and cannot expect to follow from unconstrained machine learning. Specifically, we characterize when unconstrained learning on its own implies group calibration, that is, the outcome variable is…

Machine Learning · Computer Science 2019-01-28 Lydia T. Liu , Max Simchowitz , Moritz Hardt
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