Serena Wang, Stephen Bates, P. M. Aronow, Michael I. Jordan. Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry. To appear, International Conference on Artificial Intelligence and Statistics (AISTATS), oral presentation, 2024. [arXiv]
Serena Wang, Harikrishna Narasimhan, Yichen Zhou, Sara Hooker, Michal Lukasik, Aditya Krishna Menon. Robust Distillation for Worst-class Performance: On the Interplay Between Teacher and Student Objectives. Conference on Uncertainty in Artificial Intelligence (UAI), 2023. [paper][arXiv]
Lydia Liu*, Serena Wang*, Tolani Britton†, Rediet Abebe†. Reimagining the Machine Learning Life Cycle to Improve Educational Outcomes of Students. Proceedings of the National Academy of Sciences (PNAS), 2023. (*equal contribution, †equal contribution)[paper][arXiv]
Taman Narayan, Serena Wang, Kevin Canini, Maya R. Gupta. Expected Pinball Loss For Quantile Regression And Inverse CDF Estimation. To appear, Transactions on Machine Learning Research (TMLR) 2023. [arXiv]
Wenshuo Guo*, Serena Wang*, Peng Ding, Yixin Wang, Michael I. Jordan. Multi-Source Causal Inference Using Control Variates under Outcome Selection Bias. Transactions on Machine Learning Research (TMLR), 2022. (*equal contribution) [paper][arXiv] [code]
Ghassen Jerfel*, Serena Wang*, Clara Wong-Fannjiang, Katherine A. Heller, Yian Ma, Michael I. Jordan. Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence. Conference on Uncertainty in Artificial Intelligence (UAI), 2021. (*equal contribution) [paper]
Serena Wang, Maya R. Gupta, Seungil You. Quit When You Can: Efficient Evaluation of Ensembles with Ordering Optimization. ACM Journal on Emerging Technologies in Computing Systems (JETC), 2021. [paper]
Serena Wang*, Wenshuo Guo*, Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Michael I. Jordan. Robust Optimization for Fairness with Noisy Protected Groups. 34th Conference on Neural Information Processing Systems (NeurIPS), 2020. (*equal contribution) [paper]
Harikrishna Narasimhan, Andrew Cotter, Yichen Zhou, Serena Wang, Wenshuo Guo. Approximate Heavily-Constrained Learning with Lagrange Multiplier Models. 34th Conference on Neural Information Processing Systems (NeurIPS), 2020. [paper]
Serena Wang, Maya R. Gupta. Deontological Ethics By Monotonicity Shape Constraints. Proceedings of The 23nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. [paper] [tensorflow tutorial] [video]
Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Serena Wang. Pairwise Fairness for Ranking and Regression. Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), 2020. [paper]
Andrew Cotter, Heinrich Jiang, Maya R. Gupta, Serena Wang, Taman Narayan, Seungil You, Karthik Sridharan. Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals. Journal of Machine Learning Research (JMLR), 2019. [paper]
Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth, Seungil You. Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. Proceedings of the 36th International Conference on Machine Learning (ICML), 2019. [paper]
Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Erez Louidor, James Muller, Taman Narayan, Serena Wang, Tao Zhu. Shape Constraints for Set Functions. Proceedings of the 36th International Conference on Machine Learning (ICML), 2019. [paper]
Wenshuo Guo, Mihaela Curmei, Serena Wang, Benjamin Recht, Michael I. Jordan. Finding Equilibrium in Multi-Agent Games with Payoff Uncertainty. ICML Workshop on Theoretical Foundations of Reinforcement Learning, 2020. [arXiv] [video]
Serena Wang. Predicting the Performance of Automatically Scalable Computation (ASC). Harvard University Undergraduate Thesis, 2016. [paper]
Penn State Center for Socially Responsible AI Young Achievers Symposium. Bridging Gaps Between Metrics and Goals to Improve Societal Impacts of ML, April 20, 2023 [link]
Simons Institute, AI and Humanity Summer Cluster Talk. Reimagining the Machine Learning Life Cycle in Education, July 15, 2022 [link]
Stanford Biomedical Data Science Seminar, Invited Talk. Out of Scope, Out of Mind: Expanding Frontiers for Fair ML, April 28, 2022
University of Chicago Computational Social Science Workshop, Invited Talk. Out of Scope, Out of Mind: Expanding Frontiers for Fair ML, April 21, 2022
Torrance Festival of Ideas, University of Georgia, Invited Talk. Lost in Translation: Reimagining the Machine Learning Life Cycle in Education, April 19, 2022 [link]
MIT 6.882: Ethical Machine Learning in Human Deployments, Joint Guest Lecture with Lydia T. Liu. Lost in Translation: Reimagining the Machine Learning Life Cycle in Education, April 15, 2022
UC Berkeley EE290: Design of Societal Scale Systems: Games, Incentives, Adaptation and Learning, Joint Guest Lecture with Lydia T. Liu. Lost in Translation: Reimagining the Machine Learning Life Cycle in Education, April 4, 2022
MIT Everyday AI Make-a-Thon, Joint Workshop with Lydia T. Liu. Reimagining the Machine Learning Life Cycle in Education, March 25, 2022
Apple Fairness Reading Group, Invited Talk. Out of Scope, Out of Mind: Expanding Frontiers for Fair ML, March 23, 2022
University of Cambridge Ethics in Maths Society, Invited Talk. Out of Scope, Out of Mind: Expanding Frontiers for Fairness Paradigms in ML. February 18, 2022.
University of Oxford Algorithms at Work Discussion Group, Invited Talk. Out of Scope, Out of Mind: Expanding Frontiers for Fairness Paradigms in ML. February 17, 2022.
Berkeley Institute of Data Science Machine Learning and Science Forum, Invited Speaker. ML for Social Good in Practice: from Robust Optimization to Broader Impacts in Education. October 25, 2021. [link]
UNC Charlotte Ethics Center Series on Biased AI, Invited Speaker. Promises and Pitfalls of Machine Learning for Education. October 28, 2021. [link]
Google International Women’s Day Summit, Invited Speaker. Constrained Optimization for Policy Goals. 2019. [link]
Grace Hopper Celebration, Workshop Co-presenter with Maya Gupta. The Power of Monotonicity to Make ML Make Sense: Tensorflow Lattice. 2018. [link]
Open Data Science Conference (ODSC) West, Workshop Co-presenter with Maya Gupta and Seungil You. The Power of Monotonicity to Make ML Make Sense: Tensorflow Lattice. 2017. [link]