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ORCIDAravind Gollakota
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Journal Articles
- 2022
[j2]Aravind Gollakota, Daniel Liang
:
On the Hardness of PAC-learning Stabilizer States with Noise. Quantum 6: 640 (2022)- 2020
- [j1]Aravind Gollakota, Will Hardt, István Miklós:
Packing Tree Degree Sequences. Graphs Comb. 36(3): 779-801 (2020)
Conference and Workshop Papers
- 2024
- [c8]Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
An Efficient Tester-Learner for Halfspaces. ICLR 2024 - 2023
- [c7]Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alex Dimakis, Adam R. Klivans:
Ambient Diffusion: Learning Clean Distributions from Corrupted Data. NeurIPS 2023 - [c6]Aravind Gollakota, Parikshit Gopalan, Adam R. Klivans, Konstantinos Stavropoulos:
Agnostically Learning Single-Index Models using Omnipredictors. NeurIPS 2023 - [c5]Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
Tester-Learners for Halfspaces: Universal Algorithms. NeurIPS 2023 - [c4]Aravind Gollakota, Adam R. Klivans, Pravesh K. Kothari:
A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity. STOC 2023: 1657-1670 - 2022
- [c3]Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka:
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. NeurIPS 2022 - 2020
- [c2]Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans:
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent. ICML 2020: 3587-3596 - [c1]Surbhi Goel, Aravind Gollakota, Adam R. Klivans:
Statistical-Query Lower Bounds via Functional Gradients. NeurIPS 2020
Informal and Other Publications
- 2023
- [i10]Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
An Efficient Tester-Learner for Halfspaces. CoRR abs/2302.14853 (2023) - [i9]Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
Tester-Learners for Halfspaces: Universal Algorithms. CoRR abs/2305.11765 (2023) - [i8]Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alexandros G. Dimakis, Adam R. Klivans:
Ambient Diffusion: Learning Clean Distributions from Corrupted Data. CoRR abs/2305.19256 (2023) - [i7]Aravind Gollakota, Parikshit Gopalan, Adam R. Klivans, Konstantinos Stavropoulos:
Agnostically Learning Single-Index Models using Omnipredictors. CoRR abs/2306.10615 (2023) - 2022
- [i6]Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka:
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. CoRR abs/2202.05258 (2022) - [i5]Aravind Gollakota, Adam R. Klivans, Pravesh K. Kothari:
A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity. CoRR abs/2211.13312 (2022) - 2021
- [i4]Aravind Gollakota, Daniel Liang:
On the Hardness of PAC-learning stabilizer States with Noise. CoRR abs/2102.05174 (2021) - 2020
- [i3]Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans:
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent. CoRR abs/2006.12011 (2020) - [i2]Surbhi Goel, Aravind Gollakota, Adam R. Klivans:
Statistical-Query Lower Bounds via Functional Gradients. CoRR abs/2006.15812 (2020) - [i1]Aravind Gollakota, Sushrut Karmalkar, Adam R. Klivans:
The Polynomial Method is Universal for Distribution-Free Correlational SQ Learning. CoRR abs/2010.11925 (2020)
Coauthor Index
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