Aravind Gollakota

According to our database, Aravind Gollakota authored at least 11 papers between 2020 and 2023.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2023

An Efficient Tester-Learner for Halfspaces.

Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

CoRR, 2023

A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity.

Aravind Gollakota, Adam R. Klivans, Pravesh K. Kothari

Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023

Tester-Learners for Halfspaces: Universal Algorithms.

Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Agnostically Learning Single-Index Models using Omnipredictors.

Aravind Gollakota, Parikshit Gopalan, Adam R. Klivans, Konstantinos Stavropoulos

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Ambient Diffusion: Learning Clean Distributions from Corrupted Data.

Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alex Dimakis, Adam R. Klivans

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

On the Hardness of PAC-learning Stabilizer States with Noise.

Aravind Gollakota, Daniel Liang

Quantum, 2022

Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks.

Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2020

Packing Tree Degree Sequences.

Aravind Gollakota, Will Hardt, István Miklós

Graphs Comb., 2020

The Polynomial Method is Universal for Distribution-Free Correlational SQ Learning.

Aravind Gollakota, Sushrut Karmalkar, Adam R. Klivans

CoRR, 2020

Statistical-Query Lower Bounds via Functional Gradients.

Surbhi Goel, Aravind Gollakota, Adam R. Klivans

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent.

Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans

Proceedings of the 37th International Conference on Machine Learning, 2020