Learning with Comparison Feedback: Online Estimation of Sample Statistics. - Michela Meister; Sloan Nietert (ALT, 2021)

Smooth p-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications. - Sloan Nietert; Ziv Goldfeld; Kengo Kato (ICML, 2021)

Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis. - Sloan Nietert; Ziv Goldfeld; Rachel Cummings (AISTATS, 2022)

Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances. - Sloan Nietert; Ziv Goldfeld; Ritwik Sadhu; Kengo Kato (NeurIPS, 2022)

Learning in Stackelberg Games with Non-myopic Agents. - Nika Haghtalab; Thodoris Lykouris; Sloan Nietert; Alexander Wei 001 (EC, 2022)

Learning in Stackelberg Games with Non-myopic Agents. - Nika Haghtalab; Thodoris Lykouris; Sloan Nietert; Alex Wei (CoRR, 2022)

Outlier-Robust Wasserstein DRO. - Sloan Nietert; Ziv Goldfeld; Soroosh Shafiee (NeurIPS, 2023)

Robust Estimation under the Wasserstein Distance. - Sloan Nietert; Rachel Cummings; Ziv Goldfeld (CoRR, 2023)

Robust Distribution Learning with Local and Global Adversarial Corruptions (extended abstract). - Sloan Nietert; Ziv Goldfeld; Soroosh Shafiee (COLT, 2024)

Robust Distribution Learning with Local and Global Adversarial Corruptions. - Sloan Nietert; Ziv Goldfeld; Soroosh Shafiee (CoRR, 2024)