Cyrus Cousins

Orcid: 0000-0002-1691-0282

According to our database1, Cyrus Cousins authored at least 30 papers between 2017 and 2024.

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Bibliography

2024
To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models.
CoRR, 2024

2023
Bavarian: Betweenness Centrality Approximation with Variance-aware Rademacher Averages.
ACM Trans. Knowl. Discov. Data, July, 2023

Dividing Good and Better Items Among Agents with Submodular Valuations.
CoRR, 2023

Dividing Good and Great Items Among Agents with Bivalued Submodular Valuations.
Proceedings of the Web and Internet Economics - 19th International Conference, 2023

The Good, the Bad and the Submodular: Fairly Allocating Mixed Manna Under Order-Neutral Submodular Preferences.
Proceedings of the Web and Internet Economics - 19th International Conference, 2023

Into the Unknown: Assigning Reviewers to Papers with Uncertain Affinities.
Proceedings of the Algorithmic Game Theory - 16th International Symposium, 2023

Percentile Criterion Optimization in Offline Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Properties in Simulation-Based Games.
Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

Revisiting Fair-PAC Learning and the Axioms of Cardinal Welfare.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining.
ACM Trans. Knowl. Discov. Data, 2022

Regret Pruning for Learning Equilibria in Simulation-Based Games.
CoRR, 2022

Computational and Data Requirements for Learning Generic Properties of Simulation-Based Games.
CoRR, 2022

Uncertainty and the Social Planner's Problem: Why Sample Complexity Matters.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

2021
Bounds and Applications of Concentration of Measure in Fair Machine Learning and Data Science.
PhD thesis, 2021

Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time Bounds.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees.
Proceedings of the 38th International Conference on Machine Learning, 2021

Learning Competitive Equilibria in Noisy Combinatorial Markets.
Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

2020
Making mean-estimation more efficient using an MCMC trace variance approach: DynaMITE.
CoRR, 2020

Sharp uniform convergence bounds through empirical centralization.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Improved Algorithms for Learning Equilibria in Simulation-Based Games.
Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

2019
CaDET: interpretable parametric conditional density estimation with decision trees and forests.
Mach. Learn., 2019

Learning Equilibria of Simulation-Based Games.
CoRR, 2019

Empirical Mechanism Design: Designing Mechanisms from Data.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Learning Simulation-Based Games from Data.
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

2018
Uniform Convergence Bounds for Codec Selection.
CoRR, 2018

Towards Interactive Curation & Automatic Tuning of ML Pipelines.
Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning, 2018

2017
Scalable FRaC Variants: Anomaly Detection for Precision Medicine.
Proceedings of the 2017 IEEE International Parallel and Distributed Processing Symposium Workshops, 2017

The k-Nearest Representatives Classifier: A Distance-Based Classifier with Strong Generalization Bounds.
Proceedings of the 2017 IEEE International Conference on Data Science and Advanced Analytics, 2017



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