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Hongyang Zhang

hongyanz AT ttic.edu

Postdoc fellow

Toyota Technological Institute at Chicago (TTIC)

Personal Site

Biography

I am a Postdoc fellow at Toyota Technological Institute at Chicago (TTIC), hosted by Avrim Blum and Greg Shakhnarovich. Before joining TTIC, I completed my Ph.D. degree in 2019 with wonderful 4-year study at Machine Learning Department, Carnegie Mellon University. I was fortunate to be co-advised by Maria-Florina Balcan and David P. Woodruff. Before joining CMU, I graduated from Peking University in 2015, working with Zhouchen Lin and Chao Zhang. My research interests broadly include theories and applications of machine learning and algorithms, such as adversarial defenses and attacks, non-convex/convex optimization, deep learning, low-rank subspace recovery, noise-tolerant active learning, property testing, and compressed sensing.

Interests

  • Machine Learning
  • AI Security
  • Optimization
  • Theoretical Computer Science

Education

  • Ph.D. in Machine Learning, 2015-2019

    Carnegie Mellon University

  • MSc in Intelligence Science and Technology, 2012-2015

    Peking University

Curriculum Vitae

 
 
 
 
 

Postdoc Fellow

Toyota Technological Institute at Chicago (TTIC)

Aug 2019 – Present Chicago

Research on:

  • Machine Leanring
  • AI Security and Interpretability
  • Optimization
 
 
 
 
 

Visiting Researcher

Simons Institute for the Theory of Computing

Aug 2018 – Dec 2018 Berkeley

Research on:

  • Machine Leanring
 
 
 
 
 

Intern

Petuum Inc.

May 2018 – Aug 2018 Pittsburgh

Research on:

  • Machine Leanring
 
 
 
 
 

Visiting Researcher

IBM Research, Almaden

Jun 2017 – Aug 2017 San Jose

Research on:

  • Theoretical Computer Science
  • Machine Leanring

Publications @ZERO Lab

On the Applications of Robust PCA in Image and Video Processing. PIEEE, 2018.

We survey the applications of RPCA in computer vision
Thierry Bouwmans , Sajid Javed , Hongyang Zhang , Zhouchen Lin , Ricardo Otazo
PDF

Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis. TIT, 2017.

In this paper, we prove that the range space of an m × n matrix with rank r can be exactly recovered from a few coefficients with …
Hongyang Zhang , Zhouchen Lin , Chao Zhang
PDF

Fast Compressive Phase Retrieval under Bounded Noise. AAAI, 2017.

We study the problem of recovering a t-sparse vector ±x0 in R^n from m quadratic equations yi = (a^T_i x)^2 with noisy measurements …
PDF supp

Relations among Some Low Rank Subspace Recovery Models. Neural Computation, 2015.

We discover that once a solution to one of the models is obtained, we can obtain the solutions to other models in closed-form …
Hongyang Zhang , Zhouchen Lin , Chao Zhang , Junbin Gao
PDF DOI

Exact Recoverability of Robust PCA via Outlier Pursuit with Tight Recovery Bounds. AAAI, 2015.

We have investigated the exact recovery problem of R-PCA via Outlier Pursuit.
Hongyang Zhang , Zhouchen Lin , Chao Zhang , Edward Chang
PDF supp

Robust Latent Low Rank Representation for Subspace Clustering. Neurocomputing, 2014.

We propose choosing the sparest solution in the solution set.
Hongyang Zhang , Zhouchen Lin , Chao Zhang , Junbin Gao
PDF Code DOI

A Counterexample for The Validaity of Using Nuclear Norm as A Comvex Surrogate of Rank. ECML/PKDD, 2013.

We conclude that even for rank minimization problems as simple as noiseless LatLRR, replacing rank with nuclear norm is not valid and …
Hongyang Zhang , Zhouchen Lin , Chao Zhang
PDF

Academic Acativities

Reviewer to Journals

Journal of Machine Learning Research
Machine Learning, Proceedings of the IEEE
IEEE Journal of Selected Topics in Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Signal Processing
IEEE Transactions on Cybernetics
IEEE Signal Processing Letters
IEEE Access
Neurocomputing.

Chair/Reviewer to Conferences

AAAI 2016, ICML 2016, NIPS 2016, IJCAI 2017 (PC member), STOC 2017, NIPS 2017 (PC member), AAAI 2018 (PC member), STOC 2018, ISIT 2018, ICML 2018 (PC member), COLT 2018, NeurIPS 2018 (PC member), APPROX 2018, ACML 2018 (PC member), AISTATS 2019 (PC member), ITCS 2019, NeurIPS 2019 (PC member), AAAI 2020 (PC member)