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2020 – today
- 2023
[j3]Cyrus Cousins
, Chloe Wohlgemuth, Matteo Riondato:
Bavarian: Betweenness Centrality Approximation with Variance-aware Rademacher Averages. ACM Trans. Knowl. Discov. Data 17(6): 78:1-78:47 (2023)- [c16]Cyrus Cousins, Bhaskar Mishra, Enrique Areyan Viqueira, Amy Greenwald:
Learning Properties in Simulation-Based Games. AAMAS 2023: 272-280 - [i11]Cyrus Cousins, Justin Payan, Yair Zick:
Into the Unknown: Assigning Reviewers to Papers with Uncertain Affinities. CoRR abs/2301.10816 (2023) - [i10]Cyrus Cousins, Vignesh Viswanathan, Yair Zick:
Dividing Good and Better Items Among Agents with Submodular Valuations. CoRR abs/2302.03087 (2023) - 2022
- [j2]Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin, Matteo Riondato:
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining. ACM Trans. Knowl. Discov. Data 16(6): 124:1-124:29 (2022) - [c15]Cyrus Cousins:
Uncertainty and the Social Planner's Problem: Why Sample Complexity Matters. FAccT 2022: 2004-2015 - [i9]Cyrus Cousins, Bhaskar Mishra, Enrique Areyan Viqueira, Amy Greenwald:
Computational and Data Requirements for Learning Generic Properties of Simulation-Based Games. CoRR abs/2208.06400 (2022) - [i8]Bhaskar Mishra, Cyrus Cousins, Amy Greenwald:
Regret Pruning for Learning Equilibria in Simulation-Based Games. CoRR abs/2211.16670 (2022) - 2021
- [b1]Cyrus Cousins:
Bounds and Applications of Concentration of Measure in Fair Machine Learning and Data Science. Brown University, USA, 2021 - [c14]Enrique Areyan Viqueira, Cyrus Cousins, Amy Greenwald:
Learning Competitive Equilibria in Noisy Combinatorial Markets. AAMAS 2021: 1446-1448 - [c13]Alessio Mazzetto, Cyrus Cousins, Dylan Sam, Stephen H. Bach, Eli Upfal:
Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees. ICML 2021: 7534-7543 - [c12]Cyrus Cousins, Chloe Wohlgemuth, Matteo Riondato:
Bavarian: Betweenness Centrality Approximation with Variance-Aware Rademacher Averages. KDD 2021: 196-206 - [c11]Cyrus Cousins:
An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning. NeurIPS 2021: 16610-16621 - [c10]Shahrzad Haddadan, Yue Zhuang, Cyrus Cousins, Eli Upfal:
Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time Bounds. NeurIPS 2021: 25760-25772 - [i7]Enrique Areyan Viqueira, Cyrus Cousins, Amy Greenwald:
Learning Competitive Equilibria in Noisy Combinatorial Markets. CoRR abs/2101.09551 (2021) - [i6]Cyrus Cousins:
An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning. CoRR abs/2104.14504 (2021) - [i5]Shahrzad Haddadan, Yue Zhuang, Cyrus Cousins, Eli Upfal:
Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time Bounds. CoRR abs/2111.07372 (2021) - 2020
- [c9]Enrique Areyan Viqueira, Cyrus Cousins, Amy Greenwald:
Improved Algorithms for Learning Equilibria in Simulation-Based Games. AAMAS 2020: 79-87 - [c8]Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin, Matteo Riondato:
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining. KDD 2020: 2165-2174 - [c7]Cyrus Cousins, Matteo Riondato:
Sharp uniform convergence bounds through empirical centralization. NeurIPS 2020 - [i4]Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin, Matteo Riondato:
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining. CoRR abs/2006.09085 (2020) - [i3]Cyrus Cousins, Shahrzad Haddadan, Eli Upfal:
Making mean-estimation more efficient using an MCMC trace variance approach: DynaMITE. CoRR abs/2011.11129 (2020)
2010 – 2019
- 2019
- [j1]Cyrus Cousins, Matteo Riondato:
CaDET: interpretable parametric conditional density estimation with decision trees and forests. Mach. Learn. 108(8-9): 1613-1634 (2019) - [c6]Enrique Areyan Viqueira, Amy Greenwald, Cyrus Cousins, Eli Upfal:
Learning Simulation-Based Games from Data. AAMAS 2019: 1778-1780 - [c5]Enrique Areyan Viqueira, Cyrus Cousins, Yasser Mohammad, Amy Greenwald:
Empirical Mechanism Design: Designing Mechanisms from Data. UAI 2019: 1094-1104 - [i2]Enrique Areyan Viqueira, Cyrus Cousins, Eli Upfal, Amy Greenwald:
Learning Equilibria of Simulation-Based Games. CoRR abs/1905.13379 (2019) - 2018
- [c4]Carsten Binnig, Benedetto Buratti, Yeounoh Chung, Cyrus Cousins, Tim Kraska, Zeyuan Shang, Eli Upfal, Robert C. Zeleznik, Emanuel Zgraggen:
Towards Interactive Curation & Automatic Tuning of ML Pipelines. DEEM@SIGMOD 2018: 1:1-1:4 - [i1]Clayton Sanford, Cyrus Cousins, Eli Upfal:
Uniform Convergence Bounds for Codec Selection. CoRR abs/1812.07568 (2018) - 2017
- [c3]Carsten Binnig, Fuat Basik, Benedetto Buratti, Ugur Çetintemel, Yeounoh Chung, Andrew Crotty, Cyrus Cousins, Dylan Ebert, Philipp Eichmann, Alex Galakatos, Benjamin Hättasch, Amir Ilkhechi, Tim Kraska, Zeyuan Shang, Isabella Tromba, Arif Usta, Prasetya Utama, Eli Upfal, Linnan Wang, Nathaniel Weir, Robert C. Zeleznik, Emanuel Zgraggen:
Towards Interactive Data Exploration. BIRTE (Revised Selected Papers) 2017: 177-190 - [c2]Cyrus Cousins, Eli Upfal:
The k-Nearest Representatives Classifier: A Distance-Based Classifier with Strong Generalization Bounds. DSAA 2017: 1-10 - [c1]Cyrus Cousins, Christopher M. Pietras, Donna K. Slonim:
Scalable FRaC Variants: Anomaly Detection for Precision Medicine. IPDPS Workshops 2017: 253-262
Coauthor Index
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last updated on 2023-05-24 21:41 CEST by the dblp team
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