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Qing Qu
1301 Beal AvenueAnn Arbor, MI 48109-2122

A review paper on nonconvex optimization is released

The paper, titled From Symmetry to Geometry: Tractable Nonconvex Problems, reviews recent advances on nonconvex optimization from a geometric perspective and landscape studies. This is a joint work with Yuqian Zhang and John Wright.

New paper submission

The paper, titled Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization, is submitted. This is a joint work with Chong You, Zhihui Zhu, and Yi Ma.

Invited to be a speaker and organizer for Efficient Tensor Representations for Learning and Computational Complexity

The workshop will be held from May 17 – 21, 2021 at the Institute for Pure and Applied Mathematics (IPAM) situated on the UCLA campus. This workshop is part of a semester long program on Tensor Methods and Emerging Applications to the Physical and Data Sciences.

A new review paper has been submitted to IEEE Signal Processing Magazine

The paper, titled Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications, reviews recent work on nonconvex optimization methods for finding the sparsest vectors in linear subspaces. This is a joint work with Zhihui Zhu, Xiao Li, Manolis Tsakiris, John Wright and Rene Vidal.

Two papers have been accepted at ICLR’20, with one oral presentation (top 1.85%)

Our paper, titled Geometric Analysis of Nonconvex Optimization Landscapes for Overcomplete Learning has been accepted at ICLR’20 as oral (top 1.85%). Our paper, titled Short-and-Sparse Deconvolution – A Geometric Approach has been accepted at ICLR’20 as poster (acceptance rate 26.5%).

One paper has been accepted at NeurIPS’19 as spotlight (top 3%)

Our paper, titled A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution, has been accepted at NeurIPS’19 as spotlight (top 3%). This is a joint work with Xiao Li and Zhihui Zhu.

I have been recognized as one of the best reviewers at NeurIPS’19, and invited as a mentor to the first new in ML workshop at NeurIPS