Serena Wang

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Andrew CotterGoogle ResearchVerified email at google.com
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Heinrich JiangResearch Scientist, Google ResearchVerified email at google.com
Seungil YouCTO at Kakao mobilityVerified email at caltech.edu
Harikrishna NarasimhanGoogle ResearchVerified email at google.com
Wenshuo GuoPh.D. in computer science, UC BerkeleyVerified email at eecs.berkeley.edu
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteVerified email at ttic.edu
Blake WoodworthGeorge Washington UniversityVerified email at gwu.edu
Nathan SrebroProfessor, TTIC and University of ChicagoVerified email at ttic.edu
Lydia T. LiuAssistant Professor of Computer Science, Princeton UniversityVerified email at princeton.edu
Dr. Tolani A. BrittonAssociate Professor, University of California, BerkeleyVerified email at berkeley.edu
Rediet AbebeHarvard Society of FellowsVerified email at fas.harvard.edu
Yixin WangUniversity of MichiganVerified email at umich.edu
Peng DingUniversity of California, BerkeleyVerified email at berkeley.edu
Ghassen JerfelResearch @Waymo. Ex-Google Brain, UC Berkeley, Duke, Princeton.Verified email at alumni.princeton.edu
Katherine HellerGoogle ResearchVerified email at gatsby.ucl.ac.uk
Clara FannjiangGenentech, Prescient DesignVerified email at gene.com
Yi-An MaUC San DiegoVerified email at ucsd.edu
Michal LukasikGoogleVerified email at google.com
Sara HookerHead of Cohere For AIVerified email at cohere.com

Serena Wang

UC Berkeley

Verified email at berkeley.edu - Homepage

machine learning robustness optimization algorithms and society causal inference


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Optimization with non-differentiable constraints with applications to fairness, recall, churn, and other goals A Cotter, H Jiang, M Gupta, S Wang, T Narayan, S You, K Sridharan Journal of Machine Learning Research 20 (172), 1-59, 2019	151	2019
Robust optimization for fairness with noisy protected groups S Wang, W Guo, H Narasimhan, A Cotter, M Gupta, M Jordan Advances in neural information processing systems 33, 5190-5203, 2020	116	2020
Pairwise fairness for ranking and regression H Narasimhan, A Cotter, M Gupta, S Wang Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5248-5255, 2020	111	2020
Training well-generalizing classifiers for fairness metrics and other data-dependent constraints A Cotter, M Gupta, H Jiang, N Srebro, K Sridharan, S Wang, B Woodworth, ... International Conference on Machine Learning, 1397-1405, 2019	111	2019
Variational refinement for importance sampling using the forward kullback-leibler divergence G Jerfel, S Wang, C Wong-Fannjiang, KA Heller, Y Ma, MI Jordan Uncertainty in Artificial Intelligence, 1819-1829, 2021	32	2021
Deontological ethics by monotonicity shape constraints S Wang, M Gupta International conference on artificial intelligence and statistics, 2043-2054, 2020	30	2020
Shape constraints for set functions A Cotter, M Gupta, H Jiang, E Louidor, J Muller, T Narayan, S Wang, ... International conference on machine learning, 1388-1396, 2019	28*	2019
Reimagining the machine learning life cycle to improve educational outcomes of students LT Liu, S Wang, T Britton, R Abebe Proceedings of the National Academy of Sciences 120 (9), e2204781120, 2023	17*	2023
Multi-Source Causal Inference Using Control Variates under Outcome Selection Bias W Guo, SL Wang, P Ding, Y Wang, M Jordan Transactions on Machine Learning Research, 2022	14*	2022
Robust distillation for worst-class performance: on the interplay between teacher and student objectives S Wang, H Narasimhan, Y Zhou, S Hooker, M Lukasik, AK Menon Uncertainty in Artificial Intelligence, 2237-2247, 2023	10*	2023
Approximate heavily-constrained learning with lagrange multiplier models H Narasimhan, A Cotter, Y Zhou, S Wang, W Guo Advances in Neural Information Processing Systems 33, 8693-8703, 2020	9	2020
Quit when you can: efficient evaluation of ensembles by optimized ordering S Wang, M Gupta, S You ACM Journal on Emerging Technologies in Computing Systems (JETC) 17 (4), 1-20, 2021	6*	2021
Regularization strategies for quantile regression T Narayan, S Wang, K Canini, M Gupta arXiv preprint arXiv:2102.05135, 2021	2	2021
On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry S Wang, S Bates, P Aronow, M Jordan International Conference on Artificial Intelligence and Statistics, 1522-1530, 2024		2024
Information Elicitation in Agency Games S Wang, MI Jordan, K Ligett, RP McAfee arXiv preprint arXiv:2402.14005, 2024		2024
Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry S Wang, S Bates, PM Aronow, MI Jordan arXiv preprint arXiv:2305.14595, 2023		2023
Finding Equilibrium in Multi-Agent Games with Payoff Uncertainty W Guo, M Curmei, S Wang, B Recht, MI Jordan arXiv preprint arXiv:2007.05647, 2020		2020
Proxy Fairness. MR Gupta, A Cotter, MM Fard, S Wang CoRR, 2018		2018
Expected Pinball Loss For Quantile Regression And Inverse CDF Estimation T Narayan, SL Wang, KR Canini, M Gupta Transactions on Machine Learning Research, 0

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