A Dimension-Independent Generalization Bound for Kernel Supervised Principal Component Analysis. - Hassan Ashtiani; Ali Ghodsi 001 (FE@NIPS, 2015)

Representation Learning for Clustering: A Statistical Framework. - Hassan Ashtiani; Shai Ben-David (UAI, 2015)

Clustering with Same-Cluster Queries. - Hassan Ashtiani; Shrinu Kushagra; Shai Ben-David (NIPS, 2016)

Agnostic Distribution Learning via Compression. - Hassan Ashtiani; Shai Ben-David; Abbas Mehrabian (CoRR, 2017)

Sample-Efficient Learning of Mixtures. - Hassan Ashtiani; Shai Ben-David; Abbas Mehrabian (AAAI, 2018)

Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes. - Hassan Ashtiani; Shai Ben-David; Nicholas J. A. Harvey; Christopher Liaw; Abbas Mehrabian; Yaniv Plan (NeurIPS, 2018)

Some techniques in density estimation. - Hassan Ashtiani; Abbas Mehrabian (CoRR, 2018)

Disentangled behavioural representations. - Amir Dezfouli; Hassan Ashtiani; Omar Ghattas; Richard Nock; Peter Dayan; Cheng Soon Ong (NeurIPS, 2019)

On the Sample Complexity of Learning Sum-Product Networks. - Ishaq Aden-Ali; Hassan Ashtiani (AISTATS, 2020)

Black-box Certification and Learning under Adversarial Perturbations. - Hassan Ashtiani; Vinayak Pathak; Ruth Urner (ICML, 2020)

Near-optimal Sample Complexity Bounds for Robust Learning of Gaussian Mixtures via Compression Schemes. - Hassan Ashtiani; Shai Ben-David; Nicholas J. A. Harvey; Christopher Liaw; Abbas Mehrabian; Yaniv Plan (J. ACM, 2020)

On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians. - Ishaq Aden-Ali; Hassan Ashtiani; Gautam Kamath 001 (ALT, 2021)

Privately Learning Mixtures of Axis-Aligned Gaussians. - Ishaq Aden-Ali; Hassan Ashtiani; Christopher Liaw (NeurIPS, 2021)

Private and polynomial time algorithms for learning Gaussians and beyond. - Hassan Ashtiani; Christopher Liaw (COLT, 2022)

Benefits of Additive Noise in Composing Classes with Bounded Capacity. - Alireza Fathollah Pour; Hassan Ashtiani (NeurIPS, 2022)

Adversarially Robust Learning with Tolerance. - Hassan Ashtiani; Vinayak Pathak; Ruth Urner (ALT, 2023)

Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models. - Jamil Arbas; Hassan Ashtiani; Christopher Liaw (ICML, 2023)

On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences. - Alireza Fathollah Pour; Hassan Ashtiani (NeurIPS, 2023)

Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples. - Mohammad Afzali; Hassan Ashtiani; Christopher Liaw (ALT, 2024)

Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity. - Alireza Fathollah Pour; Hassan Ashtiani; Shahab Asoodeh (COLT, 2024)

Sample-Efficient Private Learning of Mixtures of Gaussians. - Hassan Ashtiani; Mahbod Majid; Shyam Narayanan (NeurIPS, 2024)

Agnostic Private Density Estimation via Stable List Decoding. - Mohammad Afzali; Hassan Ashtiani; Christopher Liaw (CoRR, 2024)

Agnostic Private Density Estimation for GMMs via List Global Stability. - Mohammad Afzali; Hassan Ashtiani; Christopher Liaw (ALT, 2025)

Simplifying Adversarially Robust PAC Learning With Tolerance. - Hassan Ashtiani; Vinayak Pathak; Ruth Urner (COLT, 2025)