Aravind Gollakota

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 20

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Publications

Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
An Efficient Tester-Learner for Halfspaces. ICLR (2024)
Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
Tester-Learners for Halfspaces: Universal Algorithms. NeurIPS (2023)
Aravind Gollakota, Adam R. Klivans, Pravesh K. Kothari
A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity. STOC (2023)
Aravind Gollakota, Parikshit Gopalan, Adam R. Klivans, Konstantinos Stavropoulos
Agnostically Learning Single-Index Models using Omnipredictors. NeurIPS (2023)
Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
Tester-Learners for Halfspaces: Universal Algorithms. CoRR (2023)
Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alex Dimakis, Adam R. Klivans
Ambient Diffusion: Learning Clean Distributions from Corrupted Data. NeurIPS (2023)
Aravind Gollakota, Parikshit Gopalan, Adam R. Klivans, Konstantinos Stavropoulos
Agnostically Learning Single-Index Models using Omnipredictors. CoRR (2023)
Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
An Efficient Tester-Learner for Halfspaces. CoRR (2023)
Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alexandros G. Dimakis, Adam R. Klivans
Ambient Diffusion: Learning Clean Distributions from Corrupted Data. CoRR (2023)
Aravind Gollakota, Daniel Liang
On the Hardness of PAC-learning Stabilizer States with Noise. Quantum 6 (2022)
Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. CoRR (2022)
Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. NeurIPS (2022)
Aravind Gollakota, Adam R. Klivans, Pravesh K. Kothari
A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity. CoRR (2022)
Aravind Gollakota, Daniel Liang
On the Hardness of PAC-learning stabilizer States with Noise. CoRR (2021)
Surbhi Goel, Aravind Gollakota, Adam R. Klivans
Statistical-Query Lower Bounds via Functional Gradients. CoRR (2020)
Aravind Gollakota, Will Hardt, István Miklós
Packing Tree Degree Sequences. Graphs Comb. 36 (3) (2020)
Surbhi Goel, Aravind Gollakota, Adam R. Klivans
Statistical-Query Lower Bounds via Functional Gradients. NeurIPS (2020)
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)
Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent. CoRR (2020)
Aravind Gollakota, Sushrut Karmalkar, Adam R. Klivans
The Polynomial Method is Universal for Distribution-Free Correlational SQ Learning. CoRR (2020)