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Publication search results
found 35 matches
- 2024
- Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps. COLT 2024: 2840-2886 - Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Exploring and Learning in Sparse Linear MDPs without Computationally Intractable Oracles. STOC 2024: 183-193 - Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps. CoRR abs/2402.15409 (2024) - Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning. CoRR abs/2404.03774 (2024) - Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
On Learning Parities with Dependent Noise. CoRR abs/2404.11325 (2024) - Noah Golowich, Elad Hazan, Zhou Lu, Dhruv Rohatgi, Y. Jennifer Sun:
Online Control in Population Dynamics. CoRR abs/2406.01799 (2024) - Dhruv Rohatgi, Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu, Ankur Moitra, Andrej Risteski:
Towards characterizing the value of edge embeddings in Graph Neural Networks. CoRR abs/2410.09867 (2024) - 2023
- Ankur Moitra, Dhruv Rohatgi:
Provably Auditing Ordinary Least Squares in Low Dimensions. ICLR 2023 - Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Feature Adaptation for Sparse Linear Regression. NeurIPS 2023 - Chirag Pabbaraju, Dhruv Rohatgi, Anish Prasad Sevekari, Holden Lee, Ankur Moitra, Andrej Risteski:
Provable benefits of score matching. NeurIPS 2023 - Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Planning and Learning in Partially Observable Systems via Filter Stability. STOC 2023: 349-362 - Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Feature Adaptation for Sparse Linear Regression. CoRR abs/2305.16892 (2023) - Chirag Pabbaraju, Dhruv Rohatgi, Anish Prasad Sevekari, Holden Lee, Ankur Moitra, Andrej Risteski:
Provable benefits of score matching. CoRR abs/2306.01993 (2023) - Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Exploring and Learning in Sparse Linear MDPs without Computationally Intractable Oracles. CoRR abs/2309.09457 (2023) - 2022
- Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Learning in Observable POMDPs, without Computationally Intractable Oracles. NeurIPS 2022 - Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Lower Bounds on Randomly Preconditioned Lasso via Robust Sparse Designs. NeurIPS 2022 - Dhruv Rohatgi, Vasilis Syrgkanis:
Robust Generalized Method of Moments: A Finite Sample Viewpoint. NeurIPS 2022 - Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Planning in Observable POMDPs in Quasipolynomial Time. CoRR abs/2201.04735 (2022) - Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Distributional Hardness Against Preconditioned Lasso via Erasure-Robust Designs. CoRR abs/2203.02824 (2022) - Ankur Moitra, Dhruv Rohatgi:
Provably Auditing Ordinary Least Squares in Low Dimensions. CoRR abs/2205.14284 (2022) - Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Learning in Observable POMDPs, without Computationally Intractable Oracles. CoRR abs/2206.03446 (2022) - 2021
- Dhruv Rohatgi, John C. Urschel, Jake Wellens:
Regarding Two Conjectures on Clique and Biclique Partitions. Electron. J. Comb. 28(4) (2021) - Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
On the Power of Preconditioning in Sparse Linear Regression. FOCS 2021: 550-561 - Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
On the Power of Preconditioning in Sparse Linear Regression. CoRR abs/2106.09207 (2021) - Dhruv Rohatgi, Vasilis Syrgkanis:
Robust Generalized Method of Moments: A Finite Sample Viewpoint. CoRR abs/2110.03070 (2021) - 2020
- Constantinos Daskalakis, Dhruv Rohatgi, Emmanouil Zampetakis:
Truncated Linear Regression in High Dimensions. NeurIPS 2020 - Constantinos Daskalakis, Dhruv Rohatgi, Emmanouil Zampetakis:
Constant-Expansion Suffices for Compressed Sensing with Generative Priors. NeurIPS 2020 - Dhruv Rohatgi:
Near-Optimal Bounds for Online Caching with Machine Learned Advice. SODA 2020: 1834-1845 - Constantinos Daskalakis, Dhruv Rohatgi, Manolis Zampetakis:
Constant-Expansion Suffices for Compressed Sensing with Generative Priors. CoRR abs/2006.04237 (2020) - Constantinos Daskalakis, Dhruv Rohatgi, Manolis Zampetakis:
Truncated Linear Regression in High Dimensions. CoRR abs/2007.14539 (2020)
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