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Ashwin Pananjady

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publications

preprints in submission/under revision

  1. Accurate, provable, and fast nonlinear tomographic reconstruction: A variational inequality approach
    with Mengqi Lou, Kabir Verchand, and Sara Fridovich-Keil
  2. Estimating stationary mass, frequency by frequency
    with Milind Nakul and Vidya Muthukumar
  3. Computationally efficient reductions between some statistical models
    with Mengqi Lou and Guy Bresler
  4. Hyperparameter tuning via trajectory predictions: Stochastic prox-linear methods in matrix sensing
    with Mengqi Lou and Kabir Verchand
  5. Modeling and correcting bias in sequential evaluation
    with Jingyan Wang

journal publications

  1. A dual accelerated method for online distributed averaging: From consensus to distributed policy evaluation
    with Sheng Zhang and Justin Romberg
    IEEE Transactions on Automatic Control, April 2025
  2. Just Wing It: Near-optimal estimation of missing mass in a Markovian sequence
    with Vidya Muthukumar and Andrew Thangaraj
    Journal of Machine Learning Research, Oct 2024
  3. Alternating minimization for generalized rank one matrix sensing: Sharp predictions from a random initialization
    with Kabir Chandrasekher and Mengqi Lou
    Information and Inference: A Journal of the IMA, Sep 2024
  4. Do algorithms and barriers for sparse principal component analysis extend to other structured settings?
    with Guanyi Wang and Mengqi Lou
    IEEE Transactions on Signal Processing, Jul 2024
  5. Optimal and instance-dependent guarantees for Markovian linear stochastic approximation
    with Wenlong Mou, Martin J. Wainwright, and Peter L. Bartlett
    Mathematical Statistics and Learning, Apr 2024
  6. Accelerated and instance-optimal policy evaluation with linear function approximation
    with Tianjiao Li and Guanghui Lan
    SIAM Journal on Mathematics of Data Science (SIMODS), Mar 2023
  7. Sharp global convergence guarantees for iterative nonconvex optimization with random data
    with Kabir Chandrasekher and Christos Thrampoulidis
    Annals of Statistics, Feb 2023
    Runner-up, Best Paper Prize for Young Researchers in Continuous Optimization, Mathematical Optimization Society
  8. Optimal oracle inequalities for solving projected fixed-point equations, with applications to policy evaluation
    with Wenlong Mou and Martin J. Wainwright
    Mathematics of Operations Research, Dec 2022
  9. Max-affine regression: Parameter estimation for Gaussian designs
    with Avishek Ghosh, Adityanand Guntuboyina, and Kannan Ramchandran
    IEEE Transactions on Information Theory, Mar 2022
  10. Isotonic regression with unknown permutations: Statistics, computation, and adaptation
    with Richard J. Samworth
    Annals of Statistics, Feb 2022
  11. Is temporal difference learning optimal? An instance-dependent analysis
    with Koulik Khamaru, Feng Ruan, Martin J. Wainwright, and Michael I. Jordan
    SIAM Journal on Mathematics of Data Science (SIMODS), Oct 2021
    ICML 2020 workshop on theoretical foundations of reinforcement learning
  12. Single-index models in the high signal regime
    with Dean P. Foster
    IEEE Transactions on Information Theory, June 2021
  13. Instance-dependent $\ell_\infty$-bounds for policy evaluation in tabular reinforcement learning
    with Martin J. Wainwright
    IEEE Transactions on Information Theory, Jan 2021
  14. Towards Optimal Estimation of Bivariate Isotonic Matrices with Unknown Permutations
    with Cheng Mao and Martin J. Wainwright
    Annals of Statistics, Dec 2020
  15. Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems
    with Dhruv Malik, Kush Bhatia, Koulik Khamaru, Peter L. Bartlett, and Martin J. Wainwright
    Journal of Machine Learning Research, Jan 2020
  16. Worst-case vs average-case design for estimation from partial pairwise comparisons
    with Cheng Mao, Vidya Muthukumar, Martin J. Wainwright, and Thomas A. Courtade
    Annals of Statistics, April 2020
    NIPS 2017 workshop on learning on distributions, functions, graphs and groups (oral)
  17. Existence of Stein kernels under spectral gap, and discrepancy bounds
    with Thomas A. Courtade and Max Fathi
    Annales de l’Institut Henri Poincare, May 2019
  18. Quantitative stability of the Entropy Power Inequality
    with Thomas A. Courtade and Max Fathi
    IEEE Transactions on Information Theory, Aug 2018
  19. Linear regression with shuffled data: Statistical and computational limits of permutation recovery
    with Martin J. Wainwright and Thomas A. Courtade
    IEEE Transactions on Information Theory, May 2018
  20. The effect of local decodability constraints on variable-length compression
    with Thomas A. Courtade
    IEEE Transactions on Information Theory, April 2018
  21. Optimally approximating the coverage lifetime of wireless sensor networks
    with Vivek Kumar Bagaria and Rahul Vaze
    IEEE/ACM Transactions on Networking, Feb 2017
  22. On the complexity of making a distinguished vertex  minimum or maximum degree by vertex deletion
    with Sounaka Mishra and N. Safina Devi
    Journal of Discrete Algorithms, July 2015

conference publications

  1. Estimating stationary mass, frequency by frequency
    with Milind Nakul and Vidya Muthukumar
    COLT 2025
  2. Accurate, provable, and fast nonlinear tomographic reconstruction: A variational inequality approach
    with Mengqi Lou, Kabir Verchand, and Sara Fridovich-Keil
    Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D) 2025
  3. Computationally efficient reductions between some statistical models
    with Mengqi Lou and Guy Bresler
    ALT 2025 (extended abstract)
    Outstanding paper award
  4. Learning the eye of the beholder: Statistical modeling and estimation for personalized color perception
    with Xuanzhou (Lorraine) Chen, Austin Xu, and Jingyan Wang
    Allerton 2024
  5. One shot inverse reinforcement learning for stochastic linear bandits
    with Etash Guha, Jim James, Krishna Acharya, and Vidya Muthukumar
    UAI 2024
  6. Alternating minimization for generalized rank one matrix sensing: Sharp predictions from a random initialization
    with Kabir Chandrasekher and Mengqi Lou
    ALT 2024 (extended abstract), superseded by journal version
  7. Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning
    with Austin Xu, Andrew McRae, Jingyan Wang, and Mark A. Davenport
    NeurIPS 2023
  8. Sharp analysis of EM for learning mixtures of pairwise differences
    with Abhishek Dhawan and Cheng Mao
    COLT 2023
  9. Modeling and correcting bias in sequential evaluation
    with Jingyan Wang
    EC 2023 (extended abstract)
  10. A dual accelerated method for a class of distributed optimization problems
    with Sheng Zhang and Justin Romberg
    CDC 2022, superseded by journal version
  11. Optimal and instance-dependent guarantees for Markovian linear stochastic approximation
    with Wenlong Mou, Martin J. Wainwright, and Peter L. Bartlett
    COLT 2022 (extended abstract), superseded by journal version
  12. Learning from an exploring demonstrator: Optimal reward estimation for bandits
    with Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, and Vidya Muthukumar
    AISTATS 2022
    ICML 2021 workshop on Human-AI Collaboration in Sequential Decision-Making (spotlight)
  13. Preference learning along multiple criteria: A game-theoretic perspective
    with Kush Bhatia, Peter L. Bartlett, Anca D. Dragan, and Martin J. Wainwright
    NeurIPS 2020
    ICML 2020 workshop on theoretical foundations of reinforcement learning (oral)
  14. Max-affine regression with universal parameter estimation for small-ball designs
    with Avishek Ghosh, Adityanand Guntuboyina, and Kannan Ramchandran
    ISIT 2020
  15. A family of Bayesian Cramer-Rao bounds, and consequences for log-concave priors
    with Efe Aras, Kuan-Yun Lee, and Thomas A. Courtade
    ISIT 2019
  16. Derivative-free methods for policy optimization: Guarantees for linear quadratic systems
    with Dhruv Malik, Kush Bhatia, Koulik Khamaru, Peter L. Bartlett, and Martin J. Wainwright
    AISTATS 2019, superseded by journal version
  17. Breaking the $1/\sqrt{n}$ barrier: Faster rates for permutation-based models in polynomial time
    with Cheng Mao and Martin J. Wainwright
    COLT 2019 (extended abstract), superseded by journal version
  18. Gradient diversity: A key ingredient for scalable distributed learning
    with Dong Yin, Max Lam, Dimitris Papailiopoulos, Kannan Ramchandran, and Peter L. Bartlett
    AISTATS 2018
    NIPS 2017 OPT-ML workshop (oral)
  19. Wasserstein stability of the entropy power inequality for log-concave densities
    with Thomas A. Courtade and Max Fathi
    ISIT 2017, superseded by journal version
  20. Denoising linear models with permuted data
    with Martin J. Wainwright and Thomas A. Courtade
    ISIT 2017
  21. Linear regression with an unknown permutation: Statistical and computational limits
    with Martin J. Wainwright and Thomas A. Courtade
    Allerton 2016, superseded by journal version
  22. Compressing sparse sequences under local decodability constraints
    with Thomas A. Courtade
    ISIT 2015, superseded by journal version
  23. The online disjoint set cover problem and its applications
    with Vivek Kumar Bagaria and Rahul Vaze
    INFOCOM 2015
  24. Maximizing utility among selfish users in social groups
    with Vivek Kumar Bagaria and Rahul Vaze
    NCC 2014
    Best Paper Award, Networks Track

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