Vikrant Singhal

According to our database, Vikrant Singhal authored at least 12 papers between 2016 and 2024.

Collaborative distances:

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

2024

Not All Learnable Distribution Classes are Privately Learnable.

Mark Bun, Gautam Kamath, Argyris Mouzakis, Vikrant Singhal

CoRR, 2024

2023

A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions.

Vikrant Singhal

CoRR, 2023

A Bias-Variance-Privacy Trilemma for Statistical Estimation.

Gautam Kamath, Argyris Mouzakis, Matthew Regehr, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman

CoRR, 2023

Private Distribution Learning with Public Data: The View from Sample Compression.

Shai Ben-David, Alex Bie, Clément L. Canonne, Gautam Kamath, Vikrant Singhal

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

2022

Private Estimation with Public Data.

Alex Bie, Gautam Kamath, Vikrant Singhal

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

New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma.

Gautam Kamath, Argyris Mouzakis, Vikrant Singhal

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

A Private and Computationally-Efficient Estimator for Unbounded Gaussians.

Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

Privately Learning Subspaces.

Vikrant Singhal, Thomas Steinke

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Private Mean Estimation of Heavy-Tailed Distributions.

Gautam Kamath, Vikrant Singhal, Jonathan R. Ullman

Proceedings of the Conference on Learning Theory, 2020

2019

Differentially Private Algorithms for Learning Mixtures of Separated Gaussians.

Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Privately Learning High-Dimensional Distributions.

Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman

Proceedings of the Conference on Learning Theory, 2019

2016

Deterministic and probabilistic binary search in graphs.

Ehsan Emamjomeh-Zadeh, David Kempe, Vikrant Singhal

Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, 2016