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Aravind Gollakota |
Aravind Gollakota |
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I am an ML research engineer at Apple, working broadly on principled methods for reliable machine learning, including uncertainty quantification and related topics. My background is in machine learning theory and theoretical computer science. I received my PhD in CS at UT Austin in 2023 under Adam Klivans, and my undergraduate degree in math and CS at Cornell University in 2017.
(authors in alphabetical order unless indicated by *)
- When does a predictor know its own loss?
Aravind Gollakota, Parikshit Gopalan, Aayush Karan, Charlotte Peale, Udi Wieder
Submitted, 2025
[arxiv] - Provable Uncertainty Decomposition via Higher-Order Calibration
Gustaf Ahdritz, Aravind Gollakota, Parikshit Gopalan, Charlotte Peale, Udi Wieder
International Conference on Learning Representations (ICLR) 2025, to appear (Spotlight presentation)
[arxiv] - An Efficient Tester-Learner for Halfspaces
Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
International Conference on Learning Representations (ICLR) 2024
[arxiv] - Agnostically Learning Single-Index Models using Omnipredictors
Aravind Gollakota, Parikshit Gopalan, Adam R. Klivans, Konstantinos Stavropoulos
Neural Information Processing Systems (NeurIPS) 2023
[arxiv] - Ambient Diffusion: Learning Clean Distributions from Corrupted Data
Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alexandros G. Dimakis, Adam R. Klivans (*)
Neural Information Processing Systems (NeurIPS) 2023
[arxiv] - Tester-Learners for Halfspaces: Universal Algorithms
Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
Neural Information Processing Systems (NeurIPS) 2023 (Oral presentation)
[arxiv] - A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity
Aravind Gollakota, Adam R. Klivans, Pravesh K. Kothari
Symposium on Theory of Computing (STOC) 2023 (invited to SIAM Journal of Computing special issue)
[arxiv] [video] - Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks
Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka
Neural Information Processing Systems (NeurIPS) 2022 (Oral presentation)
[arxiv] [video] - On the Hardness of PAC-learning Stabilizer States with Noise
Aravind Gollakota, Daniel Liang
Quantum 6, 2022
[arxiv] - The Polynomial Method is Universal for Distribution-Free Correlational SQ Learning
Aravind Gollakota, Sushrut Karmalkar, Adam R. Klivans
Technical note, 2020
[arxiv] - Statistical-Query Lower Bounds via Functional Gradients
Surbhi Goel, Aravind Gollakota, Adam R. Klivans
Neural Information Processing Systems (NeurIPS) 2020
[arxiv] [video] - Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent
Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans
International Conference on Machine Learning (ICML) 2020
[arxiv] [video] - Packing Tree Degree Sequences
Aravind Gollakota, Will Hardt, István Miklós
Graphs and Combinatorics 36, 2020
[arxiv]