Publications
- Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples
[paper]
Mohammad Afzali, Hassan Ashtiani, Christopher Liaw
Arxiv Preprint - 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 - Polynomial time and private learning of unbounded Gaussian Mixture Models
[paper]
Jamil Arbas, Hassan Ashtiani, Christopher Liaw
ICML 2023 - Adversarially Robust Learning with Tolerance
[paper]
Hassan Ashtiani, Vinayak Pathak, Ruth Urner
ALT 2023 - Benefits of Additive Noise in Composing Classes with Bounded Capacity
[paper]
Alireza Fathollah Pour, Hassan Ashtiani
NeurIPS 2022 - Private and polynomial time algorithms for learning Gaussians and beyond
[paper]
Hassan Ashtiani, Christopher Liaw
COLT 2022 - Privately Learning Mixtures of Axis-Aligned Gaussians
[paper]
Ishaq Aden-Ali, Hassan Ashtiani, Christopher Liaw
NeurIPS 2021 - On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians
[paper]
Ishaq Aden-Ali, Hassan Ashtiani, Gautam Kamath
ALT 2021 - 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 - Black-box Certification and Learning under Adversarial Perturbations
[paper]
Hassan Ashtiani, Vinayak Pathak, Ruth Urner
ICML 2020 - On the Sample Complexity of Learning Sum-Product Networks
[paper]
Ishaq Aden-Ali and Hassan Ashtiani
AISTATS 2020 - Disentangled behavioral representations
[paper]
Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan, Cheng Soon Ong
NeurIPS 2019 - Online Nearest Neighbor Search Using Hamming Weight Trees
[paper]
Sepehr Eghbali, Hassan Ashtiani, Ladan Tahvildari
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2019 - 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) - Learning Mixtures of High-dimensional Gaussians
[paper]
Hassan Ashtiani, Abbas Mehrabian
Canadian Mathematical Society Notes (Invited Note) - Sample-Efficient Learning of Mixtures
[paper]
Hassan Ashtiani, Shai Ben-David, Abbas Mehrabian
AAAI 2018 - Online Nearest Neighbor Search in Binary Space [paper (full version)]
Sepehr Eghbali, Hassan Ashtiani, Ladan Tahvildari
ICDM 2017 - Clustering with Same-Cluster Queries
[paper, slides, video]
Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David
NIPS 2016, Oral Presenattion - A Dimension-Independent Generalization Bound for Kernel Supervised Principal Component Analysis
[paper]
Hassan Ashtiani, Ali Ghodsi
JMLR Workshop and Conference Proceedings, Volume 44: NIPS Workshop on Feature Extraction: Modern Questions and Challenges, 2015 - Representation Learning for Clustering: A Statistical Framework
[paper, poster, presentation]
Hassan Ashtiani, Shai Ben-David
UAI 2015, Oral Presnetation - Bandit-Based Local Feature Subset Selection
[paper]
Hassan Ashtiani, Majid Nili Ahmadabadi, Babak Nadjar Araabi
Neurocomputing, 2014 - Bandit-Based Structure Learning for Bayesian Network Classifiers
[paper]
Sepehr Eghbali, Hassan Ashtiani, Majid Nili Ahmadabadi, Babak Nadjar Araabi
ICONIP, 2012 - Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward
selection techniques for monthly stream flow prediction [paper]
Noori, R., A. R. Karbassi, A. Moghaddamnia, D. Han, H. Ashtiani, A. Farokhnia, and M. Ghafari Gousheh
Journal of Hydrology, 2011 - A Disaster Invariant Feature for Localization
[paper]
Behdad Soleimani, Hassan Ashtiani, Behrouz Haji Soleimani, Hadi Moradi
IROS, 2010 - Graph signature for self-reconfiguration planning of modules with symmetry
[paper]
Masoud Asadpour, Hassan Ashtiani, Alexander Sprowitz, Auke Ijspeert
IROS, 2009