Tianlong Chen (陈天龙)

What does not kill you makes you stronger

Hello World!

I am currently a third-year Ph.D. student of Electrical and Computer Engineering (DICE) at VITA, The University of Texas at Austin, advised by Dr. Zhangyang (Atlas) Wang. My research interests include AutoML, Adversarial Robustness, Self-Supervision and Graph Neural Networks. I am a recipient of IBM PhD Fellowship. [Resume] [Google Scholar] [Publication]


Education

  • [Aug. 2020 - Present] Ph.D. student in Electrical and Computer Engineering, DICE, The University of Texas at Austin
  • [Aug. 2018 - Aug. 2020] Ph.D. student in Computer Science, Texas A&M University
  • [Aug. 2013 - Jun. 2017] B.S.c. in Applied Mathematics, School of the Gifted Young, University of Science and Technology of China
  • [Aug. 2015 - Jun. 2017] B.Eng. (Dual) in Computer Science, School of the Gifted Young, University of Science and Technology of China

Publication

[*equal contribution]

2021

[ICML’21] A Unified Lottery Ticket Hypothesis for Graph Neural Networks
T. Chen*, Y. Sui*, X. Chen, A. Zhang, and Z. Wang. [Paper] [Code] [Abstract]

[ICML’21] Efficient Lottery Ticket Finding: Less Data is More
Z. Zhang*, X. Chen*, T. Chen*, and Z. Wang. [Paper] [Code] [Abstract]

[ICML’21 Long Talk] Graph Contrastive Learning Automated
Y. You, T. Chen, Y. Shen, and Z. Wang. [Paper] [Code] [Abstract]

[ICML’21 Long Talk] Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm
M. Zhu, T. Chen, and Z. Wang. [Paper] [Code] [Abstract]

[ICML’21] Self-Damaging Contrastive Learning
Z. Jiang, T. Chen, B. Mortazavi, and Z. Wang. [Paper] [Code] [Abstract]

[CVPR’21] The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision
T. Chen, J. Frankle, S. Chang, S. Liu, Y. Zhang, Z. Wang, and M. Carbin. [Paper] [Code] [Abstract] [Project]

[CVPR’21] Troubleshooting Blind Image Quality Models in the Wild
Z. Wang, H. Wang, T. Chen, Z. Wang, and K. Ma. [Paper] [Code] [Abstract]

[ICLR’21] Robust Overfitting may be mitigated by properly learned smoothening
T. Chen*, Z. Zhang*, S. Liu, S. Chang, and Z. Wang. [Paper] [Code] [Abstract]

[ICLR’21] Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning
T. Chen*, Z. Zhang*, S. Liu, S. Chang, and Z. Wang. [Paper] [Code] [Abstract]

[ICLR’21] GANs Can Play Lottery Tickets Too
X. Chen, Z. Zhang, Y. Sui, and T. Chen. [Paper] [Code] [Abstract]

[ICLR’21 Spotlight Oral] Undistillable: Making A Nasty Teacher That CANNOT teach students
H. Ma, T. Chen, T. Hu, C. You, X. Xie, and Z. Wang. [Paper] [Code] [Abstract]

[ICLR’21] Learning A Minimax Optimizer: A Pilot Study
J. Shen, X. Chen, H. Heaton, T. Chen, J. Liu, W. Yin, and Z. Wang. [Paper] [Code] [Abstract]

[ICASSP’21] VGAI: End-to-End Learning of Vision-Based Decentralized Controllers for Robust Swarms
T. Hu, F. Gama, T. Chen, Z. Wang, A. Ribeiro, and B. Sadler. [Paper] [Code] [Abstract]

[AAAIW’21 Oral] AR-Stock: Deep Augmented Relational Stock Prediction
T. Wei, Y. You, and T. Chen. [Paper] [Code] [Abstract]

2020

[NeurIPS’20 Spotlight Oral] Training Stronger Baselines for Learning to Optimize
T. Chen*, W. Zhang*, J. Zhou, S. Chang, S. Liu, L. Amini, and Z. Wang. [Paper] [Code] [Abstract]

[NeurIPS’20] The Lottery Ticket Hypothesis for Pre-trained BERT Networks
T. Chen, J. Frankle, S. Chang, S. Liu, Y. Zhang, Z. Wang, and M. Carbin. [Paper] [Code] [Abstract] [Project]

[NeurIPS’20] Once-for-All Adversarial Training: In-Situ Trade off between Robustness and Accuracy for Free
H. Wang*, T. Chen*, S. Gui, T. Hu, J. Liu, and Z. Wang. [Paper] [Code] [Abstract]

[NeurIPS’20] Graph Contrastive Learning with Augmentations
Y. You*, T. Chen*, Y. Sui, T. Chen, Z. Wang, and S. Yang. [Paper] [Code] [Abstract]

[NeurIPS’20] Robust Pre-Training by Adversarial Contrastive Learning
Z. Jiang, T. Chen, T. Chen, and Z. Wang. [Paper] [Code] [Abstract]

[InterSpeech’20] AutoSpeech: Neural Architecture Search for Speaker Recognition
S. Ding*, T. Chen*, X. Gong, W. Zha, and Z. Wang. [Paper] [Code] [Abstract]

[ECCV’20] HALO: Hardware-Aware Learning to Optimize
C. Li*, T. Chen*, H. You, Z. Wang, and Y. Lin. [Paper] [Code] [Abstract]

[ICML’20] When Does Self-Supervision Help Graph Convolutional Networks?
Y. You*, T. Chen*, Z. Wang, and Y. Shen. [Paper] [Code] [Abstract]

[ICML’20] Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training
X. Chen, W. Chen, T. Chen, Y. Yuan, C. Gong, K. Chen, and Z. Wang. [Paper] [Code] [Abstract]

[CVPR’20] Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
T. Chen, S. Liu, S. Chang, Y. Cheng, L. Amini, and Z. Wang. [Paper] [Code] [Abstract]

[CVPR’20] L^2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks
Y. You*, T. Chen*, Z. Wang, and Y. Shen. [Paper] [Code] [Abstract]

[ICLR’20] Triple Wins: Boosting Accuracy, Robustness and Efficiency by Enabling Input-Adaptive Inference
T. Hu*, T. Chen*, H. Wang, and Z. Wang. [Paper] [Code] [Abstract]

[ICLR’20] I am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively
H. Wang, T. Chen, Z. Wang, and K. Ma. [Paper] [Code] [Abstract]

[WACV’20] Calibrated Domain-Invariant Learning for Highly Generalizable Large Scale Re-Identification
Y. Yuan, W. Chen, T. Chen, Y. Yang, Z. Ren, Z. Wang and G. Hua. [Paper] [Code] [Abstract]

[LREC’20] Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing
X. Yu, T. Chen, Z. Yu, H. Li, Y. Yang, X. Jiang and A. Jiang. [Paper] [Abstract]

[CVPRW’20] Focus Longer to See Better: Recursively Refined Attention for Fine-Grained Image Classification
P. Shroff, T. Chen, Y. Wei, and Z. Wang. [Paper] [Code] [Abstract]

2019

[NeurIPS’19] Learning to Optimize in Swarms
Y. Cao, T. Chen, Z. Wang, and S. Yang. [Paper] [Code] [Abstract]

[ICCV’19] ABD-Net: Attentive but Diverse Person Re-Identification
T. Chen, S. Ding, J. Xie, Y. Yuan, W. Chen, Y. Yang, Z. Ren, and Z. Wang. [Paper] [Code] [Abstract]

[ICCVW’19] Cross-Model Person Search: A Coarse-to-FineFramework using Bi-directional Text-Image Matching
X. Yu*, T. Chen*, Y. Yang, M. Mugo, and Z. Wang. [Paper] [Abstract]

Technical Reports

[ArXiv Preprint’21] Learning to Optimize: A Primer and A Benchmark
T. Chen, X. Chen, W. Chen, H. Heaton, J. Liu, Z. Wang, and W.Yin. [Paper] [Code] [Abstract]

[ArXiv Preprint’21] Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective
T. Chen, Y. Cheng, Z. Gan, J. Liu, and Z. Wang. [Paper] [Code] [Abstract]

[ArXiv Preprint’21] Adversarial Feature Augmentation and Normalization for Visual Recognition
T. Chen, Y. Cheng, Z. Gan, J. Wang, L. Wang, Z. Wang, and J. Liu. [Paper] [Code] [Abstract]

[ArXiv Preprint’21] Good Students Play Big Lottery Better
H. Ma, T. Chen, T. Hu, C. You, X. Xie, and Z. Wang. [Paper] [Code] [Abstract]

[ArXiv Preprint’20] Can 3D Adversarial Logos Cloak Humans?
Y. Wang, J. Zhou, T.Chen, S. Liu, S. Chang, C. Bajaj, and Z. Wang. [Paper] [Code] [Abstract]

Experience

Research Award

Media Coverage

More About Me

  • I am a big fan of Pokemon. Playing Pokemon Go is one of my daily activities.
  • I also enjoy Hip-Hop and Country music. Air (艾热) is one of my favorite Chinese Hip-Hop stars.