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ORCIDHassan Ashtiani
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2020 – today
- 2024
[c17]Mohammad Afzali, Hassan Ashtiani, Christopher Liaw:
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples. ALT 2024: 47-73- [c16]Alireza Fathollah Pour, Hassan Ashtiani, Shahab Asoodeh:
Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity. COLT 2024: 4240-4275 - [i17]Mohammad Afzali, Hassan Ashtiani, Christopher Liaw:
Agnostic Private Density Estimation via Stable List Decoding. CoRR abs/2407.04783 (2024) - 2023
- [c15]Hassan Ashtiani, Vinayak Pathak, Ruth Urner:
Adversarially Robust Learning with Tolerance. ALT 2023: 115-135 - [c14]Jamil Arbas, Hassan Ashtiani, Christopher Liaw:
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models. ICML 2023: 1018-1040 - [c13]Alireza Fathollah Pour, Hassan Ashtiani:
On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences. NeurIPS 2023 - [i16]Jamil Arbas, Hassan Ashtiani, Christopher Liaw:
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models. CoRR abs/2303.04288 (2023) - [i15]Alireza Fathollah Pour, Hassan Ashtiani:
On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences. CoRR abs/2305.18423 (2023) - [i14]Mohammad Afzali, Hassan Ashtiani, Christopher Liaw:
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples. CoRR abs/2309.03847 (2023) - [i13]Alireza Fathollah Pour, Hassan Ashtiani, Shahab Asoodeh:
Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity. CoRR abs/2312.05645 (2023) - 2022
- [c12]Hassan Ashtiani, Christopher Liaw:
Private and polynomial time algorithms for learning Gaussians and beyond. COLT 2022: 1075-1076 - [c11]Alireza Fathollah Pour, Hassan Ashtiani:
Benefits of Additive Noise in Composing Classes with Bounded Capacity. NeurIPS 2022 - [i12]Hassan Ashtiani, Vinayak Pathak, Ruth Urner:
Adversarially Robust Learning with Tolerance. CoRR abs/2203.00849 (2022) - [i11]Alireza Fathollah Pour, Hassan Ashtiani:
Benefits of Additive Noise in Composing Classes with Bounded Capacity. CoRR abs/2206.07199 (2022) - 2021
- [c10]Ishaq Aden-Ali, Hassan Ashtiani, Gautam Kamath:
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians. ALT 2021: 185-216 - [c9]Ishaq Aden-Ali, Hassan Ashtiani, Christopher Liaw:
Privately Learning Mixtures of Axis-Aligned Gaussians. NeurIPS 2021: 3925-3938 - [i10]Ishaq Aden-Ali, Hassan Ashtiani, Christopher Liaw:
Privately Learning Mixtures of Axis-Aligned Gaussians. CoRR abs/2106.02162 (2021) - [i9]Hassan Ashtiani, Christopher Liaw:
Private and polynomial time algorithms for learning Gaussians and beyond. CoRR abs/2111.11320 (2021) - 2020
- [j1]Hassan Ashtiani, Shai Ben-David, Nicholas J. A. Harvey, Christopher Liaw, Abbas Mehrabian
, Yaniv Plan:
Near-optimal Sample Complexity Bounds for Robust Learning of Gaussian Mixtures via Compression Schemes. J. ACM 67(6): 32:1-32:42 (2020) - [c8]Ishaq Aden-Ali, Hassan Ashtiani:
On the Sample Complexity of Learning Sum-Product Networks. AISTATS 2020: 4508-4518 - [c7]Hassan Ashtiani, Vinayak Pathak, Ruth Urner:
Black-box Certification and Learning under Adversarial Perturbations. ICML 2020: 388-398 - [i8]Hassan Ashtiani, Vinayak Pathak, Ruth Urner:
Black-box Certification and Learning under Adversarial Perturbations. CoRR abs/2006.16520 (2020) - [i7]Ishaq Aden-Ali, Hassan Ashtiani, Gautam Kamath:
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians. CoRR abs/2010.09929 (2020)
2010 – 2019
- 2019
- [c6]Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan, Cheng Soon Ong:
Disentangled behavioural representations. NeurIPS 2019: 2251-2260 - [i6]Ishaq Aden-Ali, Hassan Ashtiani:
On the Sample Complexity of Learning Sum-Product Networks. CoRR abs/1912.02765 (2019) - 2018
- [c5]Hassan Ashtiani, Shai Ben-David, Abbas Mehrabian:
Sample-Efficient Learning of Mixtures. AAAI 2018: 2679-2686 - [c4]Hassan Ashtiani, Shai Ben-David, Nicholas J. A. Harvey, Christopher Liaw, Abbas Mehrabian, Yaniv Plan:
Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes. NeurIPS 2018: 3416-3425 - [i5]Hassan Ashtiani, Abbas Mehrabian:
Some techniques in density estimation. CoRR abs/1801.04003 (2018) - 2017
- [i4]Hassan Ashtiani, Shai Ben-David, Abbas Mehrabian:
Sample-Efficient Learning of Mixtures. CoRR abs/1706.01596 (2017) - [i3]Hassan Ashtiani, Shai Ben-David, Abbas Mehrabian:
Agnostic Distribution Learning via Compression. CoRR abs/1710.05209 (2017) - 2016
- [c3]Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David:
Clustering with Same-Cluster Queries. NIPS 2016: 3216-3224 - [i2]Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David:
Clustering with Same-Cluster Queries. CoRR abs/1606.02404 (2016) - 2015
- [c2]Hassan Ashtiani, Ali Ghodsi:
A Dimension-Independent Generalization Bound for Kernel Supervised Principal Component Analysis. FE@NIPS 2015: 19-29 - [c1]Hassan Ashtiani, Shai Ben-David:
Representation Learning for Clustering: A Statistical Framework. UAI 2015: 82-91 - [i1]Hassan Ashtiani, Shai Ben-David:
Representation Learning for Clustering: A Statistical Framework. CoRR abs/1506.05900 (2015)
Coauthor Index
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last updated on 2024-08-25 20:08 CEST by the dblp team
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