| 2025 | ALT | Agnostic Private Density Estimation for GMMs via List Global Stability. | Mohammad Afzali, Hassan Ashtiani, Christopher Liaw |
| 2025 | COLT | Simplifying Adversarially Robust PAC Learning With Tolerance. | Hassan Ashtiani, Vinayak Pathak, Ruth Urner |
| 2024 | ALT | Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples. | Mohammad Afzali, Hassan Ashtiani, Christopher Liaw |
| 2024 | COLT | Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity. | Alireza Fathollah Pour, Hassan Ashtiani, Shahab Asoodeh |
| 2023 | ALT | Adversarially Robust Learning with Tolerance. | Hassan Ashtiani, Vinayak Pathak, Ruth Urner |
| 2023 | ICML | Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models. | Jamil Arbas, Hassan Ashtiani, Christopher Liaw |
| 2022 | COLT | Private and polynomial time algorithms for learning Gaussians and beyond. | Hassan Ashtiani, Christopher Liaw |
| 2021 | ALT | On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians. | Ishaq Aden-Ali, Hassan Ashtiani, Gautam Kamath |
| 2020 | AISTATS | On the Sample Complexity of Learning Sum-Product Networks. | Ishaq Aden-Ali, Hassan Ashtiani |
| 2020 | ICML | Black-box Certification and Learning under Adversarial Perturbations. | Hassan Ashtiani, Vinayak Pathak, Ruth Urner |
| 2018 | AAAI | Sample-Efficient Learning of Mixtures. | Hassan Ashtiani, Shai Ben-David, Abbas Mehrabian |
| 2015 | UAI | Representation Learning for Clustering: A Statistical Framework. | Hassan Ashtiani, Shai Ben-David |