Ashwin Pananjady

Orcid: 0000-0003-0824-9815

According to our database1, Ashwin Pananjady authored at least 46 papers between 2013 and 2024.

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Bibliography

2024
Do Algorithms and Barriers for Sparse Principal Component Analysis Extend to Other Structured Settings?
IEEE Trans. Signal Process., 2024

Just Wing It: Optimal Estimation of Missing Mass in a Markovian Sequence.
CoRR, 2024

Efficient reductions between some statistical models.
CoRR, 2024

Alternating minimization for generalized rank one matrix sensing: Sharp predictions from a random initialization.
Proceedings of the International Conference on Algorithmic Learning Theory, 2024

Learning the Eye of the Beholder: Statistical Modeling and Estimation for Personalized Color Perception.
Proceedings of the 60th Annual Allerton Conference on Communication, 2024

2023
Accelerated and Instance-Optimal Policy Evaluation with Linear Function Approximation.
SIAM J. Math. Data Sci., March, 2023

Modeling and Correcting Bias in Sequential Evaluation.
Proceedings of the 24th ACM Conference on Economics and Computation, 2023

Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Sharp analysis of EM for learning mixtures of pairwise differences.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022
Max-Affine Regression: Parameter Estimation for Gaussian Designs.
IEEE Trans. Inf. Theory, 2022

A Dual Accelerated Method for Online Stochastic Distributed Averaging: From Consensus to Decentralized Policy Evaluation.
CoRR, 2022

Optimal and instance-dependent guarantees for Markovian linear stochastic approximation.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

A Dual Accelerated Method for a Class of Distributed Optimization Problems: From Consensus to Decentralized Policy Evaluation.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Instance-Dependent ℓ<sub>∞</sub>-Bounds for Policy Evaluation in Tabular Reinforcement Learning.
IEEE Trans. Inf. Theory, 2021

Single-Index Models in the High Signal Regime.
IEEE Trans. Inf. Theory, 2021

Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis.
SIAM J. Math. Data Sci., 2021

2020
Statistics, computation, and adaptation in high dimensions.
PhD thesis, 2020

Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems.
J. Mach. Learn. Res., 2020

Optimal oracle inequalities for solving projected fixed-point equations.
CoRR, 2020

Isotonic regression with unknown permutations: Statistics, computation, and adaptation.
CoRR, 2020

Preference learning along multiple criteria: A game-theoretic perspective.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Max-affine regression with universal parameter estimation for small-ball designs.
Proceedings of the IEEE International Symposium on Information Theory, 2020

2019
Value function estimation in Markov reward processes: Instance-dependent 𝓁<sub>∞</sub>-bounds for policy evaluation.
CoRR, 2019

Max-Affine Regression: Provable, Tractable, and Near-Optimal Statistical Estimation.
CoRR, 2019

A Family of Bayesian Cramér-Rao Bounds, and Consequences for Log-Concave Priors.
Proceedings of the IEEE International Symposium on Information Theory, 2019

2018
Linear Regression With Shuffled Data: Statistical and Computational Limits of Permutation Recovery.
IEEE Trans. Inf. Theory, 2018

The Effect of Local Decodability Constraints on Variable-Length Compression.
IEEE Trans. Inf. Theory, 2018

Quantitative Stability of the Entropy Power Inequality.
IEEE Trans. Inf. Theory, 2018

Towards Optimal Estimation of Bivariate Isotonic Matrices with Unknown Permutations.
CoRR, 2018

Breaking the 1/√n Barrier: Faster Rates for Permutation-based Models in Polynomial Time.
CoRR, 2018

Breaking the $1/\sqrtn$ Barrier: Faster Rates for Permutation-based Models in Polynomial Time.
Proceedings of the Conference On Learning Theory, 2018

Gradient Diversity: a Key Ingredient for Scalable Distributed Learning.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Optimally Approximating the Coverage Lifetime of Wireless Sensor Networks.
IEEE/ACM Trans. Netw., 2017

Gradient Diversity Empowers Distributed Learning.
CoRR, 2017

Worst-case vs Average-case Design for Estimation from Fixed Pairwise Comparisons.
CoRR, 2017

Existence of Stein Kernels under a Spectral Gap, and Discrepancy Bound.
CoRR, 2017

Denoising linear models with permuted data.
Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017

Wasserstein stability of the entropy power inequality for log-concave random vectors.
Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017

2016
Wasserstein Stability of the Entropy Power Inequality for Log-Concave Densities.
CoRR, 2016

Linear regression with an unknown permutation: Statistical and computational limits.
Proceedings of the 54th Annual Allerton Conference on Communication, 2016

2015
On the complexity of making a distinguished vertex minimum or maximum degree by vertex deletion.
J. Discrete Algorithms, 2015

Compressing sparse sequences under local decodability constraints.
Proceedings of the IEEE International Symposium on Information Theory, 2015

The online disjoint set cover problem and its applications.
Proceedings of the 2015 IEEE Conference on Computer Communications, 2015

2014
Maximizing utility among selfish users in social groups.
Proceedings of the Twentieth National Conference on Communications, 2014

2013
Optimally Approximating the Lifetime of Wireless Sensor Networks.
CoRR, 2013


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