Ankur Moitra

ORCID

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 120

Top Venues

Massachusetts Institute of Technology, Cambridge, MA, USA (PHD)

Education History

Massachusetts Institute of Technology, Cambridge, MA, USA

PHD

Publications

Byron Chin, Ankur Moitra, Elchanan Mossel, Colin Sandon
The power of an adversary in Glauber dynamics. COLT (2024)
Noah Golowich, Ankur Moitra
Edit Distance Robust Watermarks for Language Models. IACR Cryptol. ePrint Arch. 2024 (2024)
Noah Golowich, Ankur Moitra, Dhruv Rohatgi
Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning. CoRR (2024)
Noah Golowich, Ankur Moitra
Edit Distance Robust Watermarks for Language Models. CoRR (2024)
Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang
Structure learning of Hamiltonians from real-time evolution. CoRR (2024)
Noah Golowich, Ankur Moitra
The Role of Inherent Bellman Error in Offline Reinforcement Learning with Linear Function Approximation. CoRR (2024)
Noah Golowich, Ankur Moitra, Dhruv Rohatgi
Exploring and Learning in Sparse Linear MDPs without Computationally Intractable Oracles. STOC (2024)
Noah Golowich, Ankur Moitra
Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions. COLT (2024)
Noah Golowich, Ankur Moitra
Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions. CoRR (2024)
Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang
Learning Quantum Hamiltonians at Any Temperature in Polynomial Time. STOC (2024)
Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang
High-Temperature Gibbs States are Unentangled and Efficiently Preparable. CoRR (2024)
Noah Golowich, Ankur Moitra, Dhruv Rohatgi
On Learning Parities with Dependent Noise. CoRR (2024)
Saachi Jain, Hannah Lawrence, Ankur Moitra, Aleksander Madry
Distilling Model Failures as Directions in Latent Space. ICLR (2023)
Allen Liu, Ankur Moitra
Robustly Learning General Mixtures of Gaussians. J. ACM 70 (3) (2023)
Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau
Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems. CoRR (2023)
Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau
A New Approach to Learning Linear Dynamical Systems. CoRR (2023)
Chirag Pabbaraju, Dhruv Rohatgi, Anish Prasad Sevekari, Holden Lee, Ankur Moitra, Andrej Risteski
Provable benefits of score matching. NeurIPS (2023)
Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang
Learning quantum Hamiltonians at any temperature in polynomial time. CoRR (2023)
Chirag Pabbaraju, Dhruv Rohatgi, Anish Prasad Sevekari, Holden Lee, Ankur Moitra, Andrej Risteski
Provable benefits of score matching. CoRR (2023)
Noah Golowich, Ankur Moitra, Dhruv Rohatgi
Planning and Learning in Partially Observable Systems via Filter Stability. STOC (2023)
Ankur Moitra, Dhruv Rohatgi
Provably Auditing Ordinary Least Squares in Low Dimensions. ICLR (2023)
Noah Golowich, Ankur Moitra, Dhruv Rohatgi
Exploring and Learning in Sparse Linear MDPs without Computationally Intractable Oracles. CoRR (2023)
Zongchen Chen, Kuikui Liu, Nitya Mani, Ankur Moitra
Strong Spatial Mixing for Colorings on Trees and its Algorithmic Applications. FOCS (2023)
Zongchen Chen, Kuikui Liu, Nitya Mani, Ankur Moitra
Strong spatial mixing for colorings on trees and its algorithmic applications. CoRR (2023)
Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau
Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems. ICML (2023)
Allen Liu, Ankur Moitra
Robust Voting Rules from Algorithmic Robust Statistics. SODA (2023)
Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau
A New Approach to Learning Linear Dynamical Systems. STOC (2023)
Boaz Barak, Ankur Moitra
Noisy tensor completion via the sum-of-squares hierarchy. Math. Program. 193 (2) (2022)
Noah Golowich, Ankur Moitra, Dhruv Rohatgi
Learning in Observable POMDPs, without Computationally Intractable Oracles. CoRR (2022)
Noah Golowich, Ankur Moitra, Dhruv Rohatgi
Learning in Observable POMDPs, without Computationally Intractable Oracles. NeurIPS (2022)
Noah Golowich, Ankur Moitra, Dhruv Rohatgi
Planning in Observable POMDPs in Quasipolynomial Time. CoRR (2022)
Allen Liu, Ankur Moitra
Minimax Rates for Robust Community Detection. FOCS (2022)
Allen Liu, Ankur Moitra
Learning GMMs with Nearly Optimal Robustness Guarantees. COLT (2022)
Allen Liu, Ankur Moitra
Minimax Rates for Robust Community Detection. CoRR (2022)
Allen Liu, Jerry Li, Ankur Moitra
Robust Model Selection and Nearly-Proper Learning for GMMs. NeurIPS (2022)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. FOCS (2022)
Saachi Jain, Hannah Lawrence, Ankur Moitra, Aleksander Madry
Distilling Model Failures as Directions in Latent Space. CoRR (2022)
Diego Cifuentes, Ankur Moitra
Polynomial time guarantees for the Burer-Monteiro method. NeurIPS (2022)
Ankur Moitra, Dhruv Rohatgi
Provably Auditing Ordinary Least Squares in Low Dimensions. CoRR (2022)
Noah Golowich, Ankur Moitra
Can Q-learning be Improved with Advice? COLT (2022)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Kalman filtering with adversarial corruptions. STOC (2022)
Zongchen Chen, Nitya Mani, Ankur Moitra
From algorithms to connectivity and back: finding a giant component in random k-SAT. CoRR (2022)
Ankur Moitra, Elchanan Mossel, Colin P. Sandon
Learning to Sample from Censored Markov Random Fields. COLT (2021)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. FOCS (2021)
Linus Hamilton, Ankur Moitra
A No-go Theorem for Robust Acceleration in the Hyperbolic Plane. NeurIPS (2021)
Ankur Moitra, Elchanan Mossel, Colin Sandon
Learning to Sample from Censored Markov Random Fields. CoRR (2021)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Kalman Filtering with Adversarial Corruptions. CoRR (2021)
Allen Liu, Ankur Moitra
Robust Voting Rules from Algorithmic Robust Statistics. CoRR (2021)
Allen Liu, Ankur Moitra
Learning GMMs with Nearly Optimal Robustness Guarantees. CoRR (2021)
Allen Liu, Ankur Moitra
Settling the robust learnability of mixtures of Gaussians. STOC (2021)
Jerry Li, Allen Liu, Ankur Moitra
Sparsification for Sums of Exponentials and its Algorithmic Applications. CoRR (2021)
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
Robustness meets algorithms. Commun. ACM 64 (5) (2021)
Allen Liu, Ankur Moitra
How to Decompose a Tensor with Group Structure. CoRR (2021)
Noah Golowich, Ankur Moitra
Can Q-Learning be Improved with Advice? CoRR (2021)
Linus Hamilton, Ankur Moitra
No-go Theorem for Acceleration in the Hyperbolic Plane. CoRR (2021)
Ankur Moitra, Elchanan Mossel, Colin Sandon
Spoofing Generalization: When Can't You Trust Proprietary Models? CoRR (2021)
Sitan Chen, Ankur Moitra
Algorithmic foundations for the diffraction limit. STOC (2021)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to Evolvability. CoRR (2020)
Allen Liu, Ankur Moitra
Tensor Completion Made Practical. CoRR (2020)
Sitan Chen, Jerry Li, Ankur Moitra
Learning Structured Distributions From Untrusted Batches: Faster and Simpler. NeurIPS (2020)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability. NeurIPS (2020)
Sitan Chen, Jerry Li, Ankur Moitra
Learning Structured Distributions From Untrusted Batches: Faster and Simpler. CoRR (2020)
Younhun Kim, Frederic Koehler, Ankur Moitra, Elchanan Mossel, Govind Ramnarayan
How Many Subpopulations Is Too Many? Exponential Lower Bounds for Inferring Population Histories. J. Comput. Biol. 27 (4) (2020)
Sitan Chen, Jerry Li, Ankur Moitra
Efficiently learning structured distributions from untrusted batches. STOC (2020)
Allen Liu, Ankur Moitra
Better Algorithms for Estimating Non-Parametric Models in Crowd-Sourcing and Rank Aggregation. COLT (2020)
William Cole Franks, Ankur Moitra
Rigorous Guarantees for Tyler's M-Estimator via Quantum Expansion. COLT (2020)
Ankur Moitra, Elchanan Mossel, Colin Sandon
Parallels Between Phase Transitions and Circuit Complexity? COLT (2020)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. CoRR (2020)
Ankur Moitra, Andrej Risteski
Fast Convergence for Langevin Diffusion with Matrix Manifold Structure. CoRR (2020)
Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Ankur Moitra
Learning Some Popular Gaussian Graphical Models without Condition Number Bounds. NeurIPS (2020)
Sitan Chen, Ankur Moitra
Algorithmic Foundations for the Diffraction Limit. CoRR (2020)
Allen Liu, Ankur Moitra
Settling the Robust Learnability of Mixtures of Gaussians. CoRR (2020)
Allen Liu, Ankur Moitra
Tensor Completion Made Practical. NeurIPS (2020)
Cole Franks, Ankur Moitra
Rigorous Guarantees for Tyler's M-estimator via quantum expansion. CoRR (2020)
Ankur Moitra, Elchanan Mossel, Colin Sandon
The Circuit Complexity of Inference. CoRR (2019)
Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, Alistair Stewart
Robust Estimators in High-Dimensions Without the Computational Intractability. SIAM J. Comput. 48 (2) (2019)
Boaz Barak, Samuel B. Hopkins, Jonathan A. Kelner, Pravesh K. Kothari, Ankur Moitra, Aaron Potechin
A Nearly Tight Sum-of-Squares Lower Bound for the Planted Clique Problem. SIAM J. Comput. 48 (2) (2019)
Linus Hamilton, Ankur Moitra
The Paulsen Problem Made Simple. ITCS (2019)
Ankur Moitra, Alexander S. Wein
Spectral methods from tensor networks. STOC (2019)
Sitan Chen, Jerry Li, Ankur Moitra
Efficiently Learning Structured Distributions from Untrusted Batches. CoRR (2019)
Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Ankur Moitra
Learning Some Popular Gaussian Graphical Models without Condition Number Bounds. CoRR (2019)
Younhun Kim, Frederic Koehler, Ankur Moitra, Elchanan Mossel, Govind Ramnarayan
How Many Subpopulations Is Too Many? Exponential Lower Bounds for Inferring Population Histories. RECOMB (2019)
Sitan Chen, Ankur Moitra
Beyond the low-degree algorithm: mixtures of subcubes and their applications. STOC (2019)
Guy Bresler, Frederic Koehler, Ankur Moitra
Learning restricted Boltzmann machines via influence maximization. STOC (2019)
Sitan Chen, Michelle Delcourt, Ankur Moitra, Guillem Perarnau, Luke Postle
Improved Bounds for Randomly Sampling Colorings via Linear Programming. SODA (2019)
Ankur Moitra
Approximate Counting, the Lovász Local Lemma, and Inference in Graphical Models. J. ACM 66 (2) (2019)
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. SODA (2018)
Allen Liu, Ankur Moitra
Efficiently Learning Mixtures of Mallows Models. CoRR (2018)
Ankur Moitra
Robustness Meets Algorithms (Invited Talk). SWAT (2018)
Ankur Moitra, Alexander S. Wein
Spectral Methods from Tensor Networks. CoRR (2018)
Linus Hamilton, Ankur Moitra
The Paulsen Problem Made Simple. CoRR (2018)
Amelia Perry, Alexander S. Wein, Afonso S. Bandeira, Ankur Moitra
Optimality and Sub-optimality of PCA I: Spiked Random Matrix Models. CoRR (2018)
Sitan Chen, Michelle Delcourt, Ankur Moitra, Guillem Perarnau, Luke Postle
Improved Bounds for Randomly Sampling Colorings via Linear Programming. CoRR (2018)
Sitan Chen, Ankur Moitra
Learning Mixtures of Product Distributions via Higher Multilinear Moments. CoRR (2018)
Guy Bresler, Frederic Koehler, Ankur Moitra, Elchanan Mossel
Learning Restricted Boltzmann Machines via Influence Maximization. CoRR (2018)
Sanjeev Arora, Rong Ge, Yoni Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu
Learning topic models - provably and efficiently. Commun. ACM 61 (4) (2018)
Sitan Chen, Ankur Moitra
Linear Programming Bounds for Randomly Sampling Colorings. CoRR (2018)
Allen Liu, Ankur Moitra
Efficiently Learning Mixtures of Mallows Models. FOCS (2018)
John C. Urschel, Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet
Learning Determinantal Point Processes with Moments and Cycles. ICML (2017)
Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet, John C. Urschel
Rates of estimation for determinantal point processes. COLT (2017)

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Publication Activity (10 Years)

Top Topics

Top Venues

Ankur Moitra