Publications

Alan Mishler, Mohsen Ghassemi, Niccolò Dalmasso, Luhao Zhang, Vamsi K. Potluru, Tucker Balch, Manuela Veloso (2025). Auditing and Enforcing Conditional Fairness via Optimal Transport. Proceedings of the AAAI Conference on Artificial Intelligence. [Authors 1-4 contributed equally].

Harit Vishwakarma, Alan Mishler, Thomas Cook, Niccolò Dalmasso, Natraj Raman, Sumitra Ganesh (2024). Monty Hall and Score Optimization in Conformal Prediction to Improve LLM performance in Multi-choice Question Answering.

Thomas Cook, Alan Mishler, Aaditya Ramdas (2024). Semiparametric Efficient Inference in Adaptive Experiments. Proceedings of the Third Conference on Causal Learning and Reasoning.

Zikai Xiong, Niccolò Dalmasso, Alan Mishler, Vamsi K. Potluru, Tucker Balch, Manuela Veloso (2024). FairWASP: Fast and Optimal Fair Wasserstein Pre-processing. Proceedings of the AAAI Conference on Artificial Intelligence.

Akshaj Kumar Veldanda, Alan Mishler, Ivan Brugere, Sanghamitra Dutta, Siddharth Garg (2024). Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access. Transactions on Machine Learning Research [Authors 2-4 contributed equally].

Akshaj Kumar Veldanda, Alan Mishler, Ivan Brugere, Sanghamitra Dutta, Jiahao Chen, Siddharth Garg (2023). Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale with MinDiff Loss. Transactions on Machine Learning Research [Authors 2-4 contributed equally].

Alan Mishler, Mohsen Ghassemi, Alec Koppel, Sumitra Ganesh (2023). Active Learning with Missing Not At Random Outcomes. NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World.

Raphael Sonabend, Florian Pfisterer, Alan Mishler, Moritz Schauer, Lukas Burk, Sumantrak Mukherjee, Sebastian Vollmer (2022). Flexible Group Fairness Metrics for Survival Analysis.

Alan Mishler, Niccolò Dalmasso (2022). Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings.

Alan Mishler, Edward H. Kennedy (2022). FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT).