Towards Interactive Data Exploration. - Carsten Binnig; Fuat Basik; Benedetto Buratti; Ugur Çetintemel; Yeounoh Chung; Andrew Crotty; Cyrus Cousins; Dylan Ebert; Philipp Eichmann; Alex Galakatos; Benjamin Hättasch; Amir Ilkhechi; et al. (BIRTE (Revised Selected Papers), 2017)

The k-Nearest Representatives Classifier: A Distance-Based Classifier with Strong Generalization Bounds. - Cyrus Cousins; Eli Upfal (DSAA, 2017)

Scalable FRaC Variants: Anomaly Detection for Precision Medicine. - Cyrus Cousins; Christopher M. Pietras; Donna K. Slonim (IPDPS Workshops, 2017)

Towards Interactive Curation & Automatic Tuning of ML Pipelines. - Carsten Binnig; Benedetto Buratti; Yeounoh Chung; Cyrus Cousins; Tim Kraska; Zeyuan Shang; Eli Upfal; Robert C. Zeleznik; Emanuel Zgraggen (DEEM@SIGMOD, 2018)

Uniform Convergence Bounds for Codec Selection. - Clayton Sanford; Cyrus Cousins; Eli Upfal (CoRR, 2018)

Learning Simulation-Based Games from Data. - Enrique Areyan Viqueira; Amy Greenwald; Cyrus Cousins; Eli Upfal (AAMAS, 2019)

Empirical Mechanism Design: Designing Mechanisms from Data. - Enrique Areyan Viqueira; Cyrus Cousins; Yasser Mohammad; Amy Greenwald (UAI, 2019)

Learning Equilibria of Simulation-Based Games. - Enrique Areyan Viqueira; Cyrus Cousins; Eli Upfal; Amy Greenwald (CoRR, 2019)

CaDET: interpretable parametric conditional density estimation with decision trees and forests. - Cyrus Cousins; Matteo Riondato (Mach. Learn., 2019)

Improved Algorithms for Learning Equilibria in Simulation-Based Games. - Enrique Areyan Viqueira; Cyrus Cousins; Amy Greenwald (AAMAS, 2020)

Sharp uniform convergence bounds through empirical centralization. - Cyrus Cousins; Matteo Riondato (NeurIPS, 2020)

Making mean-estimation more efficient using an MCMC trace variance approach: DynaMITE. - Cyrus Cousins; Shahrzad Haddadan; Eli Upfal (CoRR, 2020)

Learning Competitive Equilibria in Noisy Combinatorial Markets. - Enrique Areyan Viqueira; Cyrus Cousins; Amy Greenwald (AAMAS, 2021)

Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees. - Alessio Mazzetto; Cyrus Cousins; Dylan Sam; Stephen H. Bach; Eli Upfal (ICML, 2021)

An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning. - Cyrus Cousins (NeurIPS, 2021)

Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time Bounds. - Shahrzad Haddadan; Yue Zhuang; Cyrus Cousins; Eli Upfal (NeurIPS, 2021)

Bounds and Applications of Concentration of Measure in Fair Machine Learning and Data Science. - Cyrus Cousins (2021)

Uncertainty and the Social Planner's Problem: Why Sample Complexity Matters. - Cyrus Cousins (FAccT, 2022)

Computational and Data Requirements for Learning Generic Properties of Simulation-Based Games. - Cyrus Cousins; Bhaskar Mishra; Enrique Areyan Viqueira; Amy Greenwald (CoRR, 2022)

Regret Pruning for Learning Equilibria in Simulation-Based Games. - Bhaskar Mishra; Cyrus Cousins; Amy Greenwald (CoRR, 2022)

MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining. - Leonardo Pellegrina; Cyrus Cousins; Fabio Vandin; Matteo Riondato (ACM Trans. Knowl. Discov. Data, 2022)

Revisiting Fair-PAC Learning and the Axioms of Cardinal Welfare. - Cyrus Cousins (AISTATS, 2023)

Learning Properties in Simulation-Based Games. - Cyrus Cousins; Bhaskar Mishra; Enrique Areyan Viqueira; Amy Greenwald (AAMAS, 2023)

Percentile Criterion Optimization in Offline Reinforcement Learning. - Cyrus Cousins; Elita A. Lobo; Marek Petrik; Yair Zick (NeurIPS, 2023)

Into the Unknown: Assigning Reviewers to Papers with Uncertain Affinities. - Cyrus Cousins; Justin Payan; Yair Zick (SAGT, 2023)

The Good, the Bad and the Submodular: Fairly Allocating Mixed Manna Under Order-Neutral Submodular Preferences. - Cyrus Cousins; Vignesh Viswanathan; Yair Zick (WINE, 2023)

Dividing Good and Great Items Among Agents with Bivalued Submodular Valuations. - Cyrus Cousins; Vignesh Viswanathan; Yair Zick (WINE, 2023)

Dividing Good and Better Items Among Agents with Submodular Valuations. - Cyrus Cousins; Vignesh Viswanathan; Yair Zick (CoRR, 2023)

Bavarian: Betweenness Centrality Approximation with Variance-aware Rademacher Averages. - Cyrus Cousins; Chloe Wohlgemuth; Matteo Riondato (ACM Trans. Knowl. Discov. Data, 2023)

To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models. - Cyrus Cousins; I. Elizabeth Kumar; Suresh Venkatasubramanian (AISTATS, 2024)

Fair and Welfare-Efficient Constrained Multi-Matchings under Uncertainty. - Elita A. Lobo; Justin Payan; Cyrus Cousins; Yair Zick (NeurIPS, 2024)

On Welfare-Centric Fair Reinforcement Learning. - Cyrus Cousins; Kavosh Asadi; Elita A. Lobo; Michael Littman 002 (RLJ, 2024)

Algorithms and Analysis for Optimizing Robust Objectives in Fair Machine Learning. - Cyrus Cousins (CoRR, 2024)

Discrimination Induced by Algorithmic Recourse Objectives. - Nicholas Perello; Cyrus Cousins; Yair Zick; Przemyslaw A. Grabowicz (FAccT, 2025)

Deploying Fair and Efficient Course Allocation Mechanisms. - George Bissias; Cyrus Cousins; Paula Navarrete Diaz; Yair Zick (CoRR, 2025)