Aravind Gollakota

25 papers A* 9Journal 15Unranked 1
YearRankTypeTitle / Venue / Authors
2025 A* conf
Provable Uncertainty Decomposition via Higher-Order Calibration.
ICLR
Gustaf Ahdritz, Aravind Gollakota, Parikshit Gopalan, Charlotte Peale, Udi Wieder
2025 J jnl
Trace Length is a Simple Uncertainty Signal in Reasoning Models.
CoRR
Siddartha Devic, Charlotte Peale, Arwen Bradley, Sinead Williamson, Preetum Nakkiran, Aravind Gollakota
2025 — conf
When Does a Predictor Know Its Own Loss?
FORC
Aravind Gollakota, Parikshit Gopalan, Aayush Karan, Charlotte Peale, Udi Wieder
2025 J jnl
When does a predictor know its own loss?
CoRR
Aravind Gollakota, Parikshit Gopalan, Aayush Karan, Charlotte Peale, Udi Wieder
2024 A* conf
An Efficient Tester-Learner for Halfspaces.
ICLR
Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
2024 J jnl
Provable Uncertainty Decomposition via Higher-Order Calibration.
CoRR
Gustaf Ahdritz, Aravind Gollakota, Parikshit Gopalan, Charlotte Peale, Udi Wieder
2023 A* conf
A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity.
STOC
Aravind Gollakota, Adam R. Klivans, Pravesh K. Kothari
2023 A* conf
Agnostically Learning Single-Index Models using Omnipredictors.
NeurIPS
Aravind Gollakota, Parikshit Gopalan, Adam R. Klivans, Konstantinos Stavropoulos
2023 J jnl
Agnostically Learning Single-Index Models using Omnipredictors.
CoRR
Aravind Gollakota, Parikshit Gopalan, Adam R. Klivans, Konstantinos Stavropoulos
2023 A* conf
Ambient Diffusion: Learning Clean Distributions from Corrupted Data.
NeurIPS
Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alex Dimakis, Adam R. Klivans
2023 J jnl
Ambient Diffusion: Learning Clean Distributions from Corrupted Data.
CoRR
Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alexandros G. Dimakis, Adam R. Klivans
2023 J jnl
An Efficient Tester-Learner for Halfspaces.
CoRR
Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
2023 A* conf
Tester-Learners for Halfspaces: Universal Algorithms.
NeurIPS
Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
2023 J jnl
Tester-Learners for Halfspaces: Universal Algorithms.
CoRR
Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
2022 J jnl
A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity.
CoRR
Aravind Gollakota, Adam R. Klivans, Pravesh K. Kothari
2022 A* conf
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks.
NeurIPS
Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka
2022 J jnl
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks.
CoRR
Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka
2022 J jnl
On the Hardness of PAC-learning Stabilizer States with Noise.
Quantum
Aravind Gollakota, Daniel Liang
2021 J jnl
On the Hardness of PAC-learning stabilizer States with Noise.
CoRR
Aravind Gollakota, Daniel Liang
2020 J jnl
Packing Tree Degree Sequences.
Graphs Comb.
Aravind Gollakota, Will Hardt, István Miklós
2020 A* conf
Statistical-Query Lower Bounds via Functional Gradients.
NeurIPS
Surbhi Goel, Aravind Gollakota, Adam R. Klivans
2020 J jnl
Statistical-Query Lower Bounds via Functional Gradients.
CoRR
Surbhi Goel, Aravind Gollakota, Adam R. Klivans
2020 A* conf
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent.
ICML
Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans
2020 J jnl
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent.
CoRR
Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans
2020 J jnl
The Polynomial Method is Universal for Distribution-Free Correlational SQ Learning.
CoRR
Aravind Gollakota, Sushrut Karmalkar, Adam R. Klivans