Abhishek Shetty

Orcid: 0009-0009-7127-5961

According to our database1, Abhishek Shetty authored at least 23 papers between 2019 and 2024.

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Bibliography

2024
Smoothed Analysis with Adaptive Adversaries.
J. ACM, June, 2024

Tolerant Algorithms for Learning with Arbitrary Covariate Shift.
CoRR, 2024

Oracle-Efficient Differentially Private Learning with Public Data.
CoRR, 2024

Omnipredictors for Regression and the Approximate Rank of Convex Functions.
CoRR, 2024

Smooth Nash Equilibria: Algorithms and Complexity.
Proceedings of the 15th Innovations in Theoretical Computer Science Conference, 2024

On the Performance of Empirical Risk Minimization with Smoothed Data.
Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023
Progressive Knowledge Distillation: Building Ensembles for Efficient Inference.
CoRR, 2023

Adversarial Resilience in Sequential Prediction via Abstention.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Progressive Ensemble Distillation: Building Ensembles for Efficient Inference.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Smoothed Analysis of Sequential Probability Assignment.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Optimal PAC Bounds without Uniform Convergence.
Proceedings of the 64th IEEE Annual Symposium on Foundations of Computer Science, 2023

The One-Inclusion Graph Algorithm is not Always Optimal.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022
Oracle-Efficient Online Learning for Beyond Worst-Case Adversaries.
CoRR, 2022

Matrix discrepancy from Quantum communication.
Proceedings of the STOC '22: 54th Annual ACM SIGACT Symposium on Theory of Computing, Rome, Italy, June 20, 2022

Oracle-Efficient Online Learning for Smoothed Adversaries.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Distribution Compression in Near-Linear Time.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Fractional Pseudorandom Generators from Any Fourier Level.
Proceedings of the 36th Computational Complexity Conference, 2021

2020
Fractional Pseudorandom Generators from the $k$th Fourier Level.
Electron. Colloquium Comput. Complex., 2020

Smoothed Analysis of Online and Differentially Private Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Effect of Activation Functions on the Training of Overparametrized Neural Nets.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Non-Gaussian component analysis using entropy methods.
Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, 2019

Sampling and Optimization on Convex Sets in Riemannian Manifolds of Non-Negative Curvature.
Proceedings of the Conference on Learning Theory, 2019

Exponential Weights on the Hypercube in Polynomial Time.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019


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