Our group actively publishes in the fields of machine learning, computer vision, and interdisciplinary data science. Below are a list of recent and selected papers. A mark * denotes the author to be a VITA student or Dr. Wang's mentee. An up-to-date full paper list can be found here.

Journal Paper

  • T. Chen*, Y. Cheng, Z. Gan, J. Wang, L. Wang, J. Liu, and Z. Wang
    “Adversarial Feature Augmentation and Normalization for Visual Recognition”
    Transactions on Machine Learning Research (TMLR), 2022. [Paper] [Code]
  • S. Mohseni, H. Wang*, Z. Yu, C. Xiao, Z. Wang, J. Yadawa
    “Taxonomy of Machine Learning Safety: A Survey and Primer”
    ACM Computing Surveys (CSUR), 2022. [Paper]
  • T. Chen*, S. Liu, S. Chang, L. Amini, and Z. Wang
    “Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning”
    Transactions on Machine Learning Research (TMLR), 2022. [Paper] [Code]
  • (α-β) T. Chen*, X. Chen*, W. Chen*, H. Heaton, J. Liu, and Z. Wang, W. Yin
    “Learning to Optimize: A Primer and A Benchmark”
    Journal of Machine Learning Research (JMLR), 2022. [Paper] [Code]
  • T. Chen*, K. Zhou, K. Duan, W. Zheng*, P. Wang*, X. Hu, and Z. Wang
    “Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study”
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022. [Paper] [Code]
  • Z. Chen, Y. Jiang*, D. Liu, Z. Wang
    “CERL: A Unified Optimization Framework for Light Enhancement with Realistic Noise”
    IEEE Transactions on Image Processing (TIP), 2022. [Paper] [Code]
  • C. Li, W. Chen*, Y. Gu, T. Chen*, Y. Fu, Z. Wang, and Y. Lin
    “DANCE: DAta-Network Co-optimization for Efficient Segmentation Model Training and Inference”
    ACM Transactions on Design Automation of Electronic Systems (TODAES), 2022. [Paper] [Code]
  • X. Chen*, Y. Zhao, Y. Wang, P. Xu, H. You, C. Li, Y. Fu, Y. Lin, and Z. Wang
    “SmartDeal: Re-Modeling Deep Network Weights for Efficient Inference and Training”
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021. [Paper] [Code]
  • T. Hu*, F. Gama, T. Chen*, W. Zheng*, Z. Wang, A. Ribeiro, and B. Sadler
    “Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks”
    IEEE Transactions on Signal and Information Processing over Networks (TSIPN), 2021. [Paper] [Code]
  • H. Wang*, T. Chen*, Z. Wang, and K. Ma
    “Troubleshooting Image Segmentation Models with Human-In-The-Loop”
    Springer Machine Learning, 2021. [Paper] [Code]
  • S. Yang*, Z. Wang, J. Jiu, and Z. Guo
    “Controllable Sketch-to-Image Translation for Robust Face Synthesis”
    IEEE Transactions on Image Processing (TIP), 2021. [Paper] [Code]
  • Z. Wu*, Z. Wang, Y. Yuan*, J. Zhang, Z. Wang, and H. Jin
    “Black-Box Diagnosis and Calibration on GAN Intra-Mode Collapse: A Pilot Study”
    ACM Transactions on Multimedia Computing Communications and Applications (ToMM), 2021. [Paper] [Code]
  • J. Yan, Y. Zhong, Y. Fang, Z. Wang, and K. Ma
    “Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition”
    International Journal of Computer Vision (IJCV), 2021. [Paper] [Code]
  • S. Yang*, Z. Wang, and J. Liu
    “Shape-Matching GAN++: Scale Controllable Dynamic Artistic Text Style Transfer”
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021. [Paper]
  • Z. Wu*, H. Wang*, Z. Wang, H. Jin, and Z. Wang
    “Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset”
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020. [Paper] [Code]
  • Y. Jiang*, X. Gong*, D. Liu, Y. Cheng, C. Fang, X. Shen, J. Yang, P. Zhou, and Z. Wang
    “EnlightenGAN: Deep Light Enhancement without Paired Supervision”
    IEEE Transactions on Image Processing (TIP), 2020. [Paper] [Code]
  • M. Karimi, D. Wu, Z. Wang, and Y. Shen
    “Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts”
    Journal of Chemical Information and Modeling (JCIM), 2020. [Paper] [Code]
  • S. Li, W. Ren, F. Wang, I. Araujo*, E. K. Tokuda*, R. Hirata, R. Cesar, Z. Wang, and X. Cao
    “A Comprehensive Benchmark Analysis of Single Image Deraining: Current Challenges and Future Perspectives”
    International Journal of Computer Vision (IJCV), 2020. [Paper]
  • Y. Yuan*, W. Yang, W. Ren, J Liu, W. J. Scheirer, and Z. Wang, et. al.
    “Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study”
    IEEE Transactions on Image Processing (TIP), 2020. [Paper]
  • M. Karimi, D. Wu, Z. Wang and Y. Shen
    “DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks”
    Oxford Bioinformatics, 2019. [Paper] [Code]
  • R. G. VidalMata, ... Y. Yuan*, J. Wu*, Z. Wang, ... et. al.
    “Bridging the Gap Between Computational Photography and Visual Recognition”
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020. [Paper][Code]
  • Y. Wang*, J. Shen*, T. Hu*, T. Nguyen, R. Baraniuk, Z. Wang, and Y. Lin
    “Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference”
    IEEE Journal of Selected Topics in Signal Processing (JSTSP), 2020. [Paper]
  • B. Li*, W. Ren, D. Fu, D. Tao, D. Feng, W. Zeng, and Z. Wang
    “Benchmarking Single Image Dehazing and Beyond”
    IEEE Transactions on Image Processing (TIP), vol. 28, no. 1, pp. 492-505, 2019. [Paper] [Project Page]

Conference Paper

  • D. Xu*, Y. Jiang*, P. Wang*, Z. Fan*, H. Shi, and Z. Wang
    “SinNeRF: Training Neural Radiance Field on Complex Scenes from a Single Image”
    European Conference on Computer Vision (ECCV), 2022. [Paper] [Code]
  • Z. Fan*, Y. Jiang*, P. Wang*, X. Gong*, D. Xu*, and Z. Wang
    “Unified Implicit Neural Stylization”
    European Conference on Computer Vision (ECCV), 2022. [Paper] [Code]
  • X. Chen*, T. Chen*, Y. Cheng, W. Chen, A. Awadallah, and Z. Wang
    “Scalable Learning to Optimize: A Learned Optimizer Can Train Big Models”
    European Conference on Computer Vision (ECCV), 2022. [Paper] [Code]
  • H. Liang*, H. Fan*, Z. Fan*, Y. Wang*, T. Chen*, Y. Cheng, and Z. Wang
    “Point Cloud Domain Adaptation via Masked Local 3D Structure Prediction”
    European Conference on Computer Vision (ECCV), 2022. [Paper] [Code]
  • Z. Jiang*, T. Chen*, X. Chen*, Y. Cheng, L. Zhou, L. Yuan, A. Awadallah, and Z. Wang
    “DnA: Improving Few-shot Transfer Learning with Low-Rank Decomposition and Alignment”
    European Conference on Computer Vision (ECCV), 2022. [Paper] [Code]
  • Y. Jiang*, B. Wronski, B. Mildenhall, J. Barron, Z. Wang, and T. Xue
    “Fast and High Quality Image Denoising via Malleable Convolution”
    European Conference on Computer Vision (ECCV), 2022. [Paper] [Code]
  • W. Chen*, X. Du, F. Yang, L. Beyer, X. Zhai, T. Lin, H. Chen, J. Li, X. Song, Z. Wang, and D. Zhou
    “A Simple Single-Scale Vision Transformer for Object Detection and Instance Segmentation”
    European Conference on Computer Vision (ECCV), 2022. [Paper] [Code]
  • Z. Mao, A. Jaiswal*, Z. Wang, and S. Chan
    “Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and A New Physics-Inspired Transformer Model”
    European Conference on Computer Vision (ECCV), 2022. [Paper] [Code]
  • D. Xu*, H. Poghosyan, S. Navasardyan, Y. Jiang*, H. Shi, and Z. Wang
    “ReCoRo: Region-Controllable Robust Light Enhancement by User-Specified Imprecise Masks”
    ACM International Conference on Multimedia (ACM MM), 2022. [Paper] [Code]
  • Z. Wu*, Z. Ren, Y. Wu, Z. Wang, and G. Hua
    “TxVAD: Improved Video Action Detection by Transformers”
    ACM International Conference on Multimedia (ACM MM), 2022. [Paper] [Code]
  • Z. Wan, D. Xu*, Z. Wang, J. Wang, and J. Luo
    “Cloud2Sketch: Augmenting Clouds with Imaginary Sketches”
    ACM International Conference on Multimedia (ACM MM), 2022. [Paper] [Code]
  • H. Wang*, A. Zhang, Y. Zhu, S. Zheng, M. Li, A. Smola, and Z. Wang
    “Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition”
    International Conference on Machine Learning (ICML), 2022. (Long Talk) [Paper] [Code]
  • H. Wang*, A. Zhang, S. Zheng, X. Shi, M. Li, and Z. Wang
    “Removing Batch Normalization Boosts Adversarial Training”
    International Conference on Machine Learning (ICML), 2022. [Paper] [Code]
  • P. Wang*, W. Zheng*, T. Chen*, and Z. Wang
    “Neural Implicit Dictionary Learning via Mixture-of-Expert Training”
    International Conference on Machine Learning (ICML), 2022. [Paper] [Code]
  • A. Jaiswal*, H. Ma, T. Chen*, Y. Ding, and Z. Wang
    “Training Your Sparse Neural Network Better with Any Mask”
    International Conference on Machine Learning (ICML), 2022. [Paper] [Code]
  • T. Chen*, H. Zhang, Z. Zhang*, S. Chang, S. Liu, P. Chen, and Z. Wang
    “Linearity Grafting: How Neuron Pruning Helps Certifiable Robustness”
    International Conference on Machine Learning (ICML), 2022. [Paper] [Code]
  • T. Chen*, X. Chen*, X. Ma, Y. Wang, and Z. Wang
    “Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets”
    International Conference on Machine Learning (ICML), 2022. [Paper] [Code]
  • T. Chen*, Z. Zhang*, S. Liu, Y. Zhang, S. Chang, and Z. Wang
    “Data-Efficient Double-Win Lottery Tickets from Robust Pre-training”
    International Conference on Machine Learning (ICML), 2022. [Paper] [Code]
  • W. Redman, T Chen*, Z. Wang, and A. Dogra,
    “Universality of Winning Tickets: A Renormalization Group Perspective”
    International Conference on Machine Learning (ICML), 2022. [Paper] [Code]
  • R. Ardywibowo, Z. Huo, Z. Wang, B. Mortazavi, S. Huang, and X. Qian
    “VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty”
    International Conference on Machine Learning (ICML), 2022. [Paper] [Code]
  • D. Hoang*, K. Zhou, T. Chen*, X. Hu, and Z. Wang
    “AutoCoG: A Unified Data-Model Co-Search Framework for Graph Neural Networks”
    International Conference on Automated Machine Learning (AutoML-Conf), 2022. [Paper] [Code]
  • J. Hong, Z. Wang, and J. Zhou
    “Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descent”
    ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2022. [Paper] [Code]
  • T. Chen*, Z. Zhang*, Y. Cheng, A. Awadallah, and Z. Wang
    “The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. [Paper] [Code]
  • T. Chen*, P. Wang*, Z. Fan*, and Z. Wang
    “Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. [Paper] [Code]
  • Z. Fan*, T. Chen*, P. Wang*, and Z. Wang
    “CADTransformer: Panoptic Symbol Spotting Transformer for CAD Drawing”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. (Oral) [Paper] [Code]
  • T. Chen*, Z. Zhang*, Y. Zhang, S. Chang, S. Liu, and Z. Wang
    “Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. [Paper] [Code]
  • X. Sun, A. Hassani, Z. Wang, G. Huang, and H. Shi
    “DiSparse: Disentangled Sparsification for Multitask Model Compression”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. [Paper] [Code]
  • Z. Chen, Y. Chen, J. Liu, X. Xu, V. Goel, Z. Wang, H. Shi, and X. Wang
    “VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. [Paper] [Code]
  • H. Ma, H. Zhao, Z. Lin, A. Kale, Z. Wang, T. Yu, J. Gu, S. Choudhary, and X. Xie
    “EI-CLIP: Entity-aware Interventional Contrastive Learning for E-commerce Cross-modal Retrieval”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. [Paper] [Code]
  • W. Zheng*, T. Chen*, T. Hu*, and Z. Wang
    “Symbolic Learning to Optimize: Towards Interpretability and Scalability”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
  • X. Chen*, J. Zhang*, and Z. Wang
    “Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
  • T. Huang*, T. Chen*, S. Liu, S. Chang, L. Amini, and Z. Wang
    “Optimizer Amalgamation”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
  • T. Chen*, Z. Zhang*, P. Wang, S. Balachandra*, H. Ma, Z. Wang, and Z. Wang
    “Sparsity Winning Twice: Better Robust Generalization from More Efficient Training”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
  • P. Wang*, W. Zheng*, T. Chen*, and Z. Wang
    “Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
  • W. Chen*, W Huang, X. Du, X. Song, Z. Wang, and D. Zhou
    “Auto-Scaling Vision Transformers without Training”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
  • S. Yu*, T. Chen*, J. Shen*, H. Yuan, J. Tian, S. Yang, J. Liu, and Z. Wang
    “Unified Visual Transformer Compression”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
  • M. Lu*, X. Luo*, T. Chen*, W. Chen*, D. Liu, and Z. Wang
    “Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, And No Retraining”
    International Conference on Learning Representations (ICLR), 2022. (Spotlight Oral) [Paper] [Code]
  • W. Zheng*, E. Huang, N. Rao, S. Katariya, Z. Wang, and K. Subbian
    “Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
  • S. Ding, T. Chen*, and Z. Wang
    “Audio Lottery: Speech Recognition Made Ultra-Lightweight, Noise-Robust, and Transferable”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
  • J. Hong, H. Wang*, Z. Wang, and J. Zhou
    “Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
  • S. Liu, T. Chen*, Z. Atashgahi, X. Chen*, G. Sokar, E. Mocanu, M. Pechenizkiy, Z. Wang, and D. Mocanu
    “Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
  • S. Liu, T. Chen*, X. Chen*, L. Shen, D. Mocanu, Z. Wang, and M. Pechenizkiy
    “The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
  • Y. You*, Y. Cao, T. Chen*, Z. Wang, and Y. Shen
    “Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
  • R. Ardywibowo, S. Boluki, Z. Wang, B. Mortazavi, S. Huang, and X. Qian
    “VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition”
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2022. [Paper] [Code]
  • S. Bibikar, H. Vikalo, Z. Wang, and X. Chen* (X. C. as corresponding author)
    “Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better”
    AAAI Conference on Artificial Intelligence (AAAI), 2022. [Paper] [Code]
  • Y. You*, T. Chen*, Z. Wang and Y. Shen
    “Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations”
    International Conference on Web Search and Data Mining (WSDM), 2022. [Paper] [Code]
  • Y. Jiang*, S. Chang, and Z. Wang
    “TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up”
    Advances in Neural Information Processing Systems (NeurIPS), 2021. [Paper] [Code]
  • H. Wang*, C. Xiao, J. Kossaifi, Z. Yu, A. Anandkumar, and Z. Wang
    “AugMax: Adversarial Composition of Random Augmentations for Robust Training”
    Advances in Neural Information Processing Systems (NeurIPS), 2021. [Paper] [Code]
  • T. Chen*, Y. Cheng, Z. Gan, J. Liu, and Z. Wang
    “Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective”
    Advances in Neural Information Processing Systems (NeurIPS), 2021. [Paper] [Code]
  • X. Chen*, Y. Cheng, S. Wang, Z. Gan, J. Liu, and Z. Wang
    “The Elastic Lottery Ticket Hypothesis”
    Advances in Neural Information Processing Systems (NeurIPS), 2021. [Paper] [Code]
  • T. Chen*, Y. Cheng, Z. Gan, L. Yuan, L. Zhang, and Z. Wang
    “Chasing Sparsity in Vision Transformers: An End-to-End Exploration”
    Advances in Neural Information Processing Systems (NeurIPS), 2021. [Paper] [Code]
  • W. Zheng*, Q. Guo, H. Yang, P. Wang*, and Z. Wang
    “Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems”
    Advances in Neural Information Processing Systems (NeurIPS), 2021. [Paper] [Code]
  • X. Chen*, T. Chen*, Z. Zhang*, and Z. Wang
    “You Are Caught Stealing My Winning Lottery Ticket! Making a Lottery Ticket Claim its Ownership”
    Advances in Neural Information Processing Systems (NeurIPS), 2021. [Paper] [Code]
  • Z. Jiang*, T. Chen*, T. Chen, and Z. Wang
    “Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling”
    Advances in Neural Information Processing Systems (NeurIPS), 2021. [Paper] [Code]
  • X. Chen*, J. Liu, Z. Wang, W. Yin
    “Hyperparameter Tuning is All You Need for LISTA”
    Advances in Neural Information Processing Systems (NeurIPS), 2021. [Paper] [Code]
  • J. Wu*, X. Dai, D. Chen, Y. Chen, M. Liu, Y. Yu, Z. Wang, Z. Liu, M. Chen, and L. Yuan
    “Stronger NAS with Weaker Predictors”
    Advances in Neural Information Processing Systems (NeurIPS), 2021. [Paper] [Code]
  • B. Pan, R. Panda, Y. Jiang*, Z. Wang, R. Feris, and A. Oliva
    “IA-RED2: Interpretability-Aware Redundancy Reduction for Vision Transformers”
    Advances in Neural Information Processing Systems (NeurIPS), 2021. [Paper] [Code]
  • S. Liu, T. Chen*, X. Chen*, Z. Atashgahi, L. Yin, H. Kou, L. Shen, M. Pechenizkiy, Z. Wang, and D. Mocanu
    “Sparse Training via Boosting Pruning Plasticity with Neuroregeneration”
    Advances in Neural Information Processing Systems (NeurIPS), 2021. [Paper] [Code]
  • X. Ma, G. Yuan, X. Shen, T. Chen*, X. Chen*, X. Chen*, N. Liu, M. Qin, S. Liu, Z. Wang, and Y. Wang
    “Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?”
    Advances in Neural Information Processing Systems (NeurIPS), 2021. [Paper] [Code]
  • Y. Jiang*, H. Zhang, J. Zhang, Y. Wang, Z. Lin, K. Sunkavalli, S. Chen, S. Amirghods, S. Kong, and Z. Wang
    “SSH: A Self-Supervised Framework for Image Harmonization”
    IEEE International Conference on Computer Vision (ICCV), 2021. [Paper] [Code]
  • X. Gong*, H. Wang, M. Shou, M. Feiszli, Z. Wang, and Z. Yan
    “Searching for Two-Stream Models in Multivariate Space for Video Recognition”
    IEEE International Conference on Computer Vision (ICCV), 2021. [Paper] [Code]
  • Y. Guo, H. Yuan, J. Tan, Z. Wang, S. Yang, and J. Liu
    “GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization”
    IEEE International Conference on Computer Vision (ICCV), 2021. [Paper] [Code]
  • Y. You*, T. Chen*, Y. Shen, and Z. Wang
    “Graph Contrastive Learning Automated”
    International Conference on Machine Learning (ICML), 2021. (Long Talk) [Paper][Code]
  • M. Zhu*, T. Chen*, and Z. Wang
    “Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm”
    International Conference on Machine Learning (ICML), 2021. (Long Talk) [Paper][Code]
  • T. Chen*, Y. Sui, X. Chen*, A. Zhang, and Z. Wang
    “A Unified Lottery Ticket Hypothesis for Graph Neural Networks”
    International Conference on Machine Learning (ICML), 2021. [Paper][Code]
  • Z. Jiang*, T. Chen*, B. Mortazavi, and Z. Wang
    “Self-Damaging Contrastive Learning”
    International Conference on Machine Learning (ICML), 2021. [Paper][Code]
  • Z. Zhang*, X. Chen*, T. Chen*, and Z. Wang
    “Efficient Lottery Ticket Finding: Less Data is More”
    International Conference on Machine Learning (ICML), 2021. [Paper][Code]
  • X. Chen*, Y. Cheng, S. Wang, Z. Gan, Z. Wang, and J. Liu
    “EarlyBERT: Efficient BERT Training via Early-Bird Lottery Tickets”
    Annual Meeting of the Association for Computational Linguistics (ACL), 2021. [Paper][Code]
  • J. Hong, Z. Zhu, S. Yu, Z. Wang, H. Dodge, and J. Zhou
    “Federated Adversarial Debiasing for Fair and Transferable Representations”
    ACM Conference on Knowledge Discovery and Data Mining (KDD), 2021. [Paper] [Code]
  • T. Chen*, J. Frankle, S. Chang, S. Liu, Y. Zhang, M. Carbin, and Z. Wang
    “The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. [Paper][Code]
  • Z. Wang, H. Wang*, T. Chen*, Z. Wang, and K. Ma
    “Troubleshooting Blind Image Quality Models in the Wild”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. [Paper] [Code]
  • P. Cao, Z. Wang, and K. Ma
    “Debiased Subjective Assessment of Real-World Image Enhancement”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. [Paper] [Code]
  • H. Ma, T. Chen*, T. Hu*, C. You, X. Xie, and Z. Wang
    “Undistillable: Making A Nasty Teacher That CANNOT Teach Students”
    International Conference on Learning Representations (ICLR), 2021. (Spotlight Oral) [Paper][Code]
  • T. Chen*, Z. Zhang*, S. Liu, S. Chang, and Z. Wang
    “Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning”
    International Conference on Learning Representations (ICLR), 2021. [Paper] [Code]
  • T. Chen*, Z. Zhang*, S. Liu, S. Chang, and Z. Wang
    “Robust Overfitting May be Mitigated by Properly Learned Smoothening”
    International Conference on Learning Representations (ICLR), 2021. [Paper] [Code]
  • W. Chen*, X. Gong*, and Z. Wang
    “Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective”
    International Conference on Learning Representations (ICLR), 2021. [Paper] [Code]
  • W. Chen*, Z. Yu, S. Mello, S. Liu, J. Alvarez, Z. Wang, and A. Anandkumar
    “Contrastive Syn-to-Real Generalization”
    International Conference on Learning Representations (ICLR), 2021. [Paper] [Code]
  • T. Meng, X. Chen*, Y. Jiang*, and Z. Wang
    “A Design Space Study for LISTA and Beyond”
    International Conference on Learning Representations (ICLR), 2021. [Paper] [Code]
  • J. Shen*, X. Chen*, H. Heaton, T. Chen*, J. Liu, W. Yin, and Z. Wang
    “Learning A Minimax Optimizer: A Pilot Study”
    International Conference on Learning Representations (ICLR), 2021. [Paper] [Code]
  • J. Shen*, H. Wang*, S. Gui, J. Tan, Z. Wang, and J. Liu
    “UMEC: Unified Model and Embedding Compression for Efficient Recommendation Systems”
    International Conference on Learning Representations (ICLR), 2021. [Paper] [Code]
  • J. Hong, H. Wang*, Z. Wang, and J. Zhou
    “Learning Model-Based Privacy Protection under Budget Constraints”
    AAAI Conference on Artificial Intelligence (AAAI), 2021.[Paper] [Code]
  • T. Chen*, W. Zhang, J. Zhou, S. Chang, S. Liu, L. Amini, and Z. Wang
    “Training Stronger Baselines for Learning to Optimize”
    Advances in Neural Information Processing Systems (NeurIPS), 2020. (Spotlight Oral) [Paper] [Code]
  • H. Wang*, T. Chen*, S. Gui, T. Hu*, J. Liu, and Z. Wang
    “Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free”
    Advances in Neural Information Processing Systems (NeurIPS), 2020. [Paper] [Code]
  • T. Chen*, J. Frankle, S. Chang, S. Liu, Y. Zhang, Z. Wang, and M. Carbin
    “The Lottery Ticket Hypothesis for Pre-trained BERT Networks”
    Advances in Neural Information Processing Systems (NeurIPS), 2020. [Paper] [Code]
  • X. Chen*, Z. Wang, S. Tang, and K. Muandet
    “MATE: Plugging in Model Awareness to Task Embedding for Meta Learning”
    Advances in Neural Information Processing Systems (NeurIPS), 2020. [Paper] [Code]
  • Z. Jiang*, T. Chen*, T. Chen, and Z. Wang
    “Robust Pre-Training by Adversarial Contrastive Learning”
    Advances in Neural Information Processing Systems (NeurIPS), 2020. [Paper] [Code]
  • Y. You*, T. Chen*, Y. Sui, T. Chen, Z. Wang, and Y. Shen
    “Graph Contrastive Learning with Augmentations”
    Advances in Neural Information Processing Systems (NeurIPS), 2020. [Paper] [Code]
  • H. You, X. Chen*, Y. Zhang, C. Li, S. Li, Z. Liu, Z. Wang, and Y. Lin
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