Sloan Nietert

According to our database, Sloan Nietert authored at least 10 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Robust Distribution Learning with Local and Global Adversarial Corruptions.

Sloan Nietert, Ziv Goldfeld, Soroosh Shafiee

CoRR, 2024

Robust Distribution Learning with Local and Global Adversarial Corruptions (extended abstract).

Sloan Nietert, Ziv Goldfeld, Soroosh Shafiee

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

Robust Estimation under the Wasserstein Distance.

Sloan Nietert, Rachel Cummings, Ziv Goldfeld

CoRR, 2023

Outlier-Robust Wasserstein DRO.

Sloan Nietert, Ziv Goldfeld, Soroosh Shafiee

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Learning in Stackelberg Games with Non-myopic Agents.

Nika Haghtalab, Thodoris Lykouris, Sloan Nietert, Alex Wei

CoRR, 2022

Learning in Stackelberg Games with Non-myopic Agents.

Nika Haghtalab, Thodoris Lykouris, Sloan Nietert, Alexander Wei

Proceedings of the EC '22: The 23rd ACM Conference on Economics and Computation, Boulder, CO, USA, July 11, 2022

Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances.

Sloan Nietert, Ziv Goldfeld, Ritwik Sadhu, Kengo Kato

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis.

Sloan Nietert, Ziv Goldfeld, Rachel Cummings

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Smooth p-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications.

Sloan Nietert, Ziv Goldfeld, Kengo Kato

Proceedings of the 38th International Conference on Machine Learning, 2021

Learning with Comparison Feedback: Online Estimation of Sample Statistics.

Michela Meister, Sloan Nietert

Proceedings of the Algorithmic Learning Theory, 2021