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A New Perspective on Robust -Estimation: Finite Sample Theory and Applications to Dependence-Adjusted Multiple Testing
Wen-Xin Zhou, Koushiki Bose, Jianqing Fan +1
Heavy-tailed errors impair the accuracy of the least squares estimate, which can be spoiled by a single grossly outlying observation. As argued in the seminal work of Peter Huber i…
Inter-Subject Analysis: Inferring Sparse Interactions with Dense Intra-Graphs
Cong Ma, Junwei Lu, Han Liu
We develop a new modeling framework for Inter-Subject Analysis (ISA). The goal of ISA is to explore the dependency structure between different subjects with the intra-subject depen…
Property Testing in High Dimensional Ising models
Matey Neykov, Han Liu
This paper explores the information-theoretic limitations of graph property testing in zero-field Ising models. Instead of learning the entire graph structure, sometimes testing a…
Adaptive Inferential Method for Monotone Graph Invariants
Junwei Lu, Matey Neykov, Han Liu
We consider the problem of undirected graphical model inference. In many applications, instead of perfectly recovering the unknown graph structure, a more realistic goal is to infe…
Homotopy Parametric Simplex Method for Sparse Learning
Haotian Pang, Robert Vanderbei, Han Liu +1
High dimensional sparse learning has imposed a great computational challenge to large scale data analysis. In this paper, we are interested in a broad class of sparse learning appr…