Hang Zhang

IEEE Trans. Signal Process., 2024

LLM-based Weak Supervision Framework for Query Intent Classification in Video Search.

[BibT_eX]

[DOI]

CoRR, 2024

2023

The Phase Transition Phenomenon of Shuffled Regression.

[BibT_eX]

[DOI]

CoRR, 2023

Optimal Estimator for Linear Regression with Shuffled Labels.

[BibT_eX]

[DOI]

CoRR, 2023

Greed is good: correspondence recovery for unlabeled linear regression.

[BibT_eX]

[DOI]

Proceedings of the Uncertainty in Artificial Intelligence, 2023

One-Step Estimator for Permuted Sparse Recovery.

[BibT_eX]

[DOI]

Muralikrishnna G. Sethuraman

Proceedings of the International Conference on Machine Learning, 2023

A Density Evolution Framework for Recovery of Covariance and Causal Graphs from Compressed Measurements.

[BibT_eX]

[DOI]

Proceedings of the 59th Annual Allerton Conference on Communication, 2023

Improved Bound on Generalization Error of Compressed KNN Estimator.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

The Benefits of Diversity: Permutation Recovery in Unlabeled Sensing From Multiple Measurement Vectors.

[BibT_eX]

[DOI]

Martin Slawski

IEEE Trans. Inf. Theory, 2022

Structure Learning in Graphical Models from Indirect Observations.

[BibT_eX]

[DOI]

Muralikrishnna G. Sethuraman

CoRR, 2022

Design of Compressed Sensing Systems via Density-Evolution Framework for Structure Recovery in Graphical Models.

[BibT_eX]

[DOI]

CoRR, 2022

2021

A General Framework for the Design of Compressive Sensing using Density Evolution.

[BibT_eX]

[DOI]

Proceedings of the IEEE Information Theory Workshop, 2021

Sparse Recovery with Shuffled Labels: Statistical Limits and Practical Estimators.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Symposium on Information Theory, 2021

2020

Optimal Estimator for Unlabeled Linear Regression.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Permutation Recovery from Multiple Measurement Vectors in Unlabeled Sensing.

[BibT_eX]

[DOI]

Martin Slawski

Proceedings of the IEEE International Symposium on Information Theory, 2019

Compressive Sensing with a Multiple Convex Sets Domain.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Symposium on Information Theory, 2019

Analysis of Sparse-integer Measurement Matrices in Compressive Sensing.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2019

Recovering Noisy-Pseudo-Sparse Signals From Linear Measurements via l∞.

[BibT_eX]

[DOI]

Proceedings of the 57th Annual Allerton Conference on Communication, 2019

2018

Sparse Recovery of Sign Vectors under Uncertain Sensing Matrices.

[BibT_eX]

[DOI]

Proceedings of the IEEE Information Theory Workshop, 2018

2017

Compressive sensing with energy constraint.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE Information Theory Workshop, 2017

Recovery of sign vectors in quadratic compressed sensing.

[BibT_eX]

[DOI]