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Aaron Koolyk

dblp:180/1445 · DBLP profile ↗
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3ranked-venue papers
1as first author
2since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorTheory of computation · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
3 papers
Information theory · 50% Algorithmic game theory and mechanism design · 50%
Artificial intelligence
1 paper
Learning theory · 100%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information theory › estimation theory
entropy estimation
1.222023
Dimension-Free Empirical Entropy Estimation · IEEE Trans. Inf. Theory 2023
Dimension-free empirical entropy estimation · NeurIPS 2021
Algorithmic game theory and mechanism design › social choice
computational social choice
0.312017
Convergence and Quality of Iterative Voting Under Non-Scoring Rules · IJCAI 2017
Algorithmic game theory and mechanism design › game dynamics
equilibrium convergence
0.312017
Convergence and Quality of Iterative Voting Under Non-Scoring Rules · IJCAI 2017
Algorithmic game theory and mechanism design › social choice › strategic voting
iterative voting
0.312017
Convergence and Quality of Iterative Voting Under Non-Scoring Rules · IJCAI 2017
Algorithmic game theory and mechanism design › social choice › computational social choice
voting rules
0.312017
Convergence and Quality of Iterative Voting Under Non-Scoring Rules · IJCAI 2017
Machine learning › Learning theory › statistical learning theory › finite-sample analysis
finite-sample bounds
0.112021
Dimension-free empirical entropy estimation · NeurIPS 2021
Machine learning › Learning theory › statistical estimation › minimax estimation
minimax rates
0.112021
Dimension-free empirical entropy estimation · NeurIPS 2021

Methods — techniques the papers use, named apart from their topics

moment assumptions · 1.7information theory · 1.0minimax rates · 0.7game-theoretic equilibrium analysis · 0.3empirical analysis · 0.3
YearPublicationVenuePosition
2023 Dimension-Free Empirical Entropy Estimation
abstract
We seek an entropy estimator for discrete distributions with fully empirical accuracy bounds. As stated, this goal is infeasible without some prior assumptions on the distribution. We discover that a certain information moment assumption renders the problem feasible. We argue that the moment assumption is natural and, in some sense, minimalistic — weaker than finite support or tail decay conditions. Under the moment assumption, we provide the first finite-sample entropy estimates for infinite alphabets, nearly recovering the known minimax rates. Moreover, we demonstrate that our empirical bounds are significantly sharper than the state-of-the-art bounds, for various natural distributions and non-trivial sample regimes. Along the way, we give a dimension-free analogue of the Cover-Thomas result on entropy continuity (with respect to total variation distance) for finite alphabets, which may be of independent interest. Additionally, we resolve all of the open problems posed by Jürgensen and Matthews, 2010.
Doron Cohen 0002, Aryeh Kontorovich, Aaron Koolyk, Geoffrey Wolfer
IEEE Trans. Inf. Theory3
2021 Dimension-free empirical entropy estimation
abstract
We seek an entropy estimator for discrete distributions with fully empirical accuracy bounds. As stated, this goal is infeasible without some prior assumptions on the distribution. We discover that a certain information moment assumption renders the problem feasible. We argue that the moment assumption is natural and, in some sense, {\em minimalistic} --- weaker than finite support or tail decay conditions. Under the moment assumption, we provide the first finite-sample entropy estimates for infinite alphabets, nearly recovering the known minimax rates. Moreover, we demonstrate that our empirical bounds are significantly sharper than the state-of-the-art bounds, for various natural distributions and non-trivial sample regimes. Along the way, we give a dimension-free analogue of the Cover-Thomas result on entropy continuity (with respect to total variation distance) for finite alphabets, which may be of independent interest.
Doron Cohen 0002, Aryeh Kontorovich, Aaron Koolyk, Geoffrey Wolfer
NeurIPS3
2017 Convergence and Quality of Iterative Voting Under Non-Scoring Rules
abstract
Iterative voting is a social choice mechanism that assumes all voters are strategic, and allows voters to change their stated preferences as the vote progresses until an equilibrium is reached (at which point no player wishes to change their vote). Previous research established that this process converges to an equilibrium for the plurality and veto voting methods and for no other scoring rule. We consider iterative voting for non-scoring rules, examining the major ones, and show that none of them converge when assuming (as most research has so far) that voters pursue a best response strategy. We investigate other potential voter strategies, with a more heuristic flavor (since for most of these voting rules, calculating the best response is NP-hard); we show that they also do not converge. We then conduct an empirical analysis of the iterative voting winners for these non-scoring rules, and compare the winner quality of various strategies.
Aaron Koolyk, Tyrone Strangway, Omer Lev, Jeffrey S. Rosenschein
IJCAI1