Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent. - Surbhi Goel; Aravind Gollakota; Zhihan Jin; Sushrut Karmalkar; Adam R. Klivans (ICML, 2020)

Statistical-Query Lower Bounds via Functional Gradients. - Surbhi Goel; Aravind Gollakota; Adam R. Klivans (NeurIPS, 2020)

The Polynomial Method is Universal for Distribution-Free Correlational SQ Learning. - Aravind Gollakota; Sushrut Karmalkar; Adam R. Klivans (CoRR, 2020)

Packing Tree Degree Sequences. - Aravind Gollakota; Will Hardt; István Miklós (Graphs Comb., 2020)

Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. - Sitan Chen; Aravind Gollakota; Adam R. Klivans; Raghu Meka (NeurIPS, 2022)

On the Hardness of PAC-learning Stabilizer States with Noise. - Aravind Gollakota; Daniel Liang (Quantum, 2022)

Ambient Diffusion: Learning Clean Distributions from Corrupted Data. - Giannis Daras; Kulin Shah; Yuval Dagan; Aravind Gollakota; Alex Dimakis; Adam R. Klivans (NeurIPS, 2023)

Agnostically Learning Single-Index Models using Omnipredictors. - Aravind Gollakota; Parikshit Gopalan; Adam R. Klivans; Konstantinos Stavropoulos (NeurIPS, 2023)

Tester-Learners for Halfspaces: Universal Algorithms. - Aravind Gollakota; Adam R. Klivans; Konstantinos Stavropoulos; Arsen Vasilyan (NeurIPS, 2023)

A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity. - Aravind Gollakota; Adam R. Klivans; Pravesh K. Kothari (STOC, 2023)

An Efficient Tester-Learner for Halfspaces. - Aravind Gollakota; Adam R. Klivans; Konstantinos Stavropoulos; Arsen Vasilyan (ICLR, 2024)

When Does a Predictor Know Its Own Loss? - Aravind Gollakota; Parikshit Gopalan; Aayush Karan; Charlotte Peale; Udi Wieder (FORC, 2025)

Provable Uncertainty Decomposition via Higher-Order Calibration. - Gustaf Ahdritz; Aravind Gollakota; Parikshit Gopalan; Charlotte Peale; Udi Wieder (ICLR, 2025)