Structured Signal Proc. & Learning
The goal of this project involves developing statistical signal processing methods in the context of structured information, a problem arising in many applications. Structure can be in the form of linear/non-linear/Boolean sensing operators, or in the form of signal structures such as sparsity, graph/group structures or low-rank information.
Sparse Signal Processing with Linear and Non-Linear Observations: A Unified Shannon Theoretic Approach (http://arxiv.org/pdf/1304.0682.pdf)
Necessary and Sufficient Conditions and a Provably Efficient Algorithm for Separable Topic Discovery (http://arxiv.org/pdf/1508.05565v2.pdf)
Learning Mixed Membership Mallows Models from Pairwise Comparisons (http://arxiv.org/pdf/1504.00757.pdf)
Learning Immune-Defectives Graph through Group Tests (http://arxiv.org/pdf/1503.00555.pdf)
Minimax Optimal Sparse Signal Recovery with Poisson Statistics (http://arxiv.org/pdf/1501.05200.pdf)
Efficient Minimax Signal Detection on Graphs ( http://arxiv.org/pdf/1411.6203.pdf )