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ORCIDDhruv Rohatgi
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2020 – today
- 2023
[c10]Ankur Moitra, Dhruv Rohatgi:
Provably Auditing Ordinary Least Squares in Low Dimensions. ICLR 2023- [c9]Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Planning and Learning in Partially Observable Systems via Filter Stability. STOC 2023: 349-362 - [i14]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Feature Adaptation for Sparse Linear Regression. CoRR abs/2305.16892 (2023) - [i13]Chirag Pabbaraju, Dhruv Rohatgi, Anish Prasad Sevekari, Holden Lee, Ankur Moitra, Andrej Risteski:
Provable benefits of score matching. CoRR abs/2306.01993 (2023) - [i12]Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Exploring and Learning in Sparse Linear MDPs without Computationally Intractable Oracles. CoRR abs/2309.09457 (2023) - 2022
- [c8]Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Learning in Observable POMDPs, without Computationally Intractable Oracles. NeurIPS 2022 - [c7]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Lower Bounds on Randomly Preconditioned Lasso via Robust Sparse Designs. NeurIPS 2022 - [c6]Dhruv Rohatgi, Vasilis Syrgkanis:
Robust Generalized Method of Moments: A Finite Sample Viewpoint. NeurIPS 2022 - [i11]Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Planning in Observable POMDPs in Quasipolynomial Time. CoRR abs/2201.04735 (2022) - [i10]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Distributional Hardness Against Preconditioned Lasso via Erasure-Robust Designs. CoRR abs/2203.02824 (2022) - [i9]Ankur Moitra, Dhruv Rohatgi:
Provably Auditing Ordinary Least Squares in Low Dimensions. CoRR abs/2205.14284 (2022) - [i8]Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Learning in Observable POMDPs, without Computationally Intractable Oracles. CoRR abs/2206.03446 (2022) - 2021
- [j2]Dhruv Rohatgi, John C. Urschel, Jake Wellens:
Regarding Two Conjectures on Clique and Biclique Partitions. Electron. J. Comb. 28(4) (2021) - [c5]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
On the Power of Preconditioning in Sparse Linear Regression. FOCS 2021: 550-561 - [i7]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
On the Power of Preconditioning in Sparse Linear Regression. CoRR abs/2106.09207 (2021) - [i6]Dhruv Rohatgi, Vasilis Syrgkanis:
Robust Generalized Method of Moments: A Finite Sample Viewpoint. CoRR abs/2110.03070 (2021) - 2020
- [c4]Constantinos Daskalakis, Dhruv Rohatgi, Emmanouil Zampetakis:
Truncated Linear Regression in High Dimensions. NeurIPS 2020 - [c3]Constantinos Daskalakis, Dhruv Rohatgi, Emmanouil Zampetakis:
Constant-Expansion Suffices for Compressed Sensing with Generative Priors. NeurIPS 2020 - [c2]Dhruv Rohatgi:
Near-Optimal Bounds for Online Caching with Machine Learned Advice. SODA 2020: 1834-1845 - [i5]Constantinos Daskalakis, Dhruv Rohatgi, Manolis Zampetakis:
Constant-Expansion Suffices for Compressed Sensing with Generative Priors. CoRR abs/2006.04237 (2020) - [i4]Constantinos Daskalakis, Dhruv Rohatgi, Manolis Zampetakis:
Truncated Linear Regression in High Dimensions. CoRR abs/2007.14539 (2020)
2010 – 2019
- 2019
- [j1]Dhruv Rohatgi:
Off-Diagonal Ordered Ramsey Numbers of Matchings. Electron. J. Comb. 26(2): 2 (2019) - [c1]Dhruv Rohatgi:
Conditional Hardness of Earth Mover Distance. APPROX-RANDOM 2019: 12:1-12:17 - [i3]Dhruv Rohatgi:
Conditional Hardness of Earth Mover Distance. CoRR abs/1909.11068 (2019) - [i2]Dhruv Rohatgi:
Near-Optimal Bounds for Online Caching with Machine Learned Advice. CoRR abs/1910.12172 (2019) - 2018
- [i1]Dhruv Rohatgi:
Sliding window order statistics in sublinear space. CoRR abs/1807.04400 (2018)
Coauthor Index
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last updated on 2023-09-27 22:42 CEST by the dblp team
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