Hassan Ashtiani


Associate Professor
Department of Computing and Software
Faculty of Engineering
McMaster University
Faculty Affiliate
The Vector Institute

Email: zokaeiam@mcmaster.ca
Phone: +1-905-525-9140 ext. 27234
Office: ITB 246 		Photo
[Publications] [Bio] [Students]

Research

Broadly speaking, my research interests revolve around Machine Learning, Artificial Intelligence, Statistics, and Theoretical Computer Science. Some of the research directions that I am currently focusing on are

  • Differentially Private Machine Learning
  • Statistically/Computationally Efficient Distribution Learning
  • Learning in Presence of Adversarial Perturbations or Data Poisoning
  • Unsupervised Domain Alignment and Learning under Distribution Shift
  • Modern Generalization Bounds for Supervised Learning

    • Highlighted Publications [Full List]

    1. Agnostic Private Density Estimation for GMMs via List Global Stability [paper]
      Mohammad Afzali, Hassan Ashtiani, Christopher Liaw
      Preprint

    2. Sample-Efficient Private Learning of Mixtures of Gaussians [paper]
      Hassan Ashtiani, Mahbod Majid, Shyam Narayanan
      NeurIPS 2024 (Spotlight)

    3. Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity [paper]
      Alireza F. Pour, Hassan Ashtiani, Shahab Asoodeh
      COLT 2024

    4. On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences [paper]
      Alireza Fathollah Pour, Hassan Ashtiani
      NeurIPS 2023

    5. Polynomial time and private learning of unbounded Gaussian Mixture Models [paper]
      Jamil Arbas, Hassan Ashtiani, Christopher Liaw
      ICML 2023

    6. Adversarially Robust Learning with Tolerance [paper]
      Hassan Ashtiani, Vinayak Pathak, Ruth Urner
      ALT 2023

    7. Private and polynomial time algorithms for learning Gaussians and beyond [paper]
      Hassan Ashtiani, Christopher Liaw
      COLT 2022

    8. Near-optimal Sample Complexity Bounds for Robust Learning of Gaussian Mixtures via Compression Schemes [paper], [arXiv]
      Hassan Ashtiani, Shai Ben-David, Nick Harvey, Chris Liaw, Abbas Mehrabian, Yaniv Plan
      Journal of the ACM, 2020

    9. Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes [paper]
      Hassan Ashtiani, Shai Ben-David, Nick Harvey, Chris Liaw, Abbas Mehrabian, Yaniv Plan
      NeurIPS (NIPS) 2018, Oral Presentation (Best Paper Award)