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

  • M. Lin, T. Li, Y. Yang, G. Holste*, Y. Ding, S. Tassel, K. Kovacs, G. Shih, Z. Wang, Z. Lu, F. Wang, and Y. Peng
    “Improving Model Fairness in Image-based Computer-Aided Diagnosis”
    Nature Communications, 2023. [Paper] [Code]
  • Q. Wu*, X. Chen*, Y. Jiang*, and Z. Wang
    “Chasing Better Deep Image Priors between Over- and Under-Parameterization”
    Transactions on Machine Learning Research (TMLR), 2023. [Paper] [Code]
  • G. Holste*, E. Oikonomou, B. Mortazavi, A. Coppi, K. Faridi, E. Miller, J. Forrest, R. McNamara, L. Ohno-Machado, N. Yuan, A. Gupta, D. Ouyang, H. Krumholz, Z. Wang, and R. Khera
    “Severe Aortic Stenosis Detection by Deep Learning Applied to Echocardiography”
    European Heart Journal (EHJ), 2023. [Paper] [Code]
  • W. Zheng*, H. Yang, J. Cai, P. Wang*, X. Jiang, S. Du, Y. Wang, and Z. Wang
    “Integrating the Traffic Science with Representation Learning for City-Wide Network Congestion Prediction”
    Elsevier Information Fusion, 2023. [Paper] [Code]
  • W. Zheng*, E. Huang, N. Rao, S. Katariya, Z. Wang, and K. Subbian
    “You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction”
    Transactions on Machine Learning Research (TMLR), 2023. [Paper] [Code]
  • Z. Li*, T. Chen*, L. Li, B. Li, and Z. Wang
    “Can Pruning Improve Certified Robustness of Neural Networks?”
    Transactions on Machine Learning Research (TMLR), 2023. [Paper] [Code]
  • H. Wang*, J. Hong, J. Zhou, and Z. Wang
    “How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts”
    Transactions on Machine Learning Research (TMLR), 2023. [Paper] [Code]
  • P. Narayanan, X. Hu, Z. Wu*, M. Thielke, J. Rogers, A. Harrison, J. D’Agostino, J. Brown, L. Quang, J. Uplinger, H. Kwon, and Z. Wang
    “A Multi-Purpose Real Haze Benchmark with Quantifiable Haze Levels and Ground Truth”
    IEEE Transactions on Image Processing (TIP), 2023. [Paper] [Code]
  • T. Chen*, Z. Zhang*, J. Wu, R. Huang, S. Liu, S. Chang, and Z. Wang
    “Can You Win Everything with A Lottery Ticket?”
    Transactions on Machine Learning Research (TMLR), 2022. [Paper] [Code]
  • Y. Han*, G. Holste*, Y. Ding, A. Tewfik, Y. Peng, and Z. Wang
    “Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays”
    IEEE Transactions on Medical Imaging (TMI), 2022. [Paper] [Code]
  • 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. (Featured Certification) [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]
  • 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]
  • 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]
  • 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

  • A. Jaiswal*, S. Liu*, T. Chen*, and Z. Wang
    "The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter”
    Advances in Neural Information Processing Systems (NeurIPS), 2023. [Paper] [Code]
  • Z. Zhang*, Y. Sheng, T. Zhou, T. Chen*, L. Zheng, R. Cai*, Z. Song, Y. Tian, C. Ré, C. Barrett, Z. Wang, and B. Chen
    "H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models”
    Advances in Neural Information Processing Systems (NeurIPS), 2023. [Paper] [Code]
  • D. Hoang*, S. Kundu, S. Liu*, and Z. Wang
    "Don’t Just Prune by Magnitude! Your Mask Topology is A Secret Weapon”
    Advances in Neural Information Processing Systems (NeurIPS), 2023. [Paper] [Code]
  • H. Wang*, Z. Jiang*, Y. You*, Y. Han*, G. Liu, J. Srinivasa, R. Kompella, Z. Wang
    "Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling”
    Advances in Neural Information Processing Systems (NeurIPS), 2023. [Paper] [Code]
  • Z. Wang, Y. Jiang*, Y. Lu, Y. Shen, P. He, W. Chen, Z. Wang, M. Zhou
    "In-Context Learning Unlocked for Diffusion Models”
    Advances in Neural Information Processing Systems (NeurIPS), 2023. (Spotlight) [Paper] [Code]
  • Z. Wang, Y. Jiang*, H. Zheng, P. Wang*, P. He, Z. Wang, W. Chen, M. Zhou
    "Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models”
    Advances in Neural Information Processing Systems (NeurIPS), 2023. [Paper] [Code]
  • L. Yin*, G. Li, M. Fang, L. Shen, T. Huang, Z. Wang, V. Menkovski, X. Ma, M. Pechenizkiy, and S. Liu*
    "Dynamic Sparsity Is Channel-Level Sparsity Learner”
    Advances in Neural Information Processing Systems (NeurIPS), 2023. [Paper] [Code]
  • W. Cong*, H. Liang*, P. Wang*, Z. Fan*, T. Chen*, M. Varma*, Y. Wang*, and Z. Wang
    "Enhancing NeRF akin to Enhancing LLMs: Generalizable NeRF Transformer with Mixture-of-View-Experts”
    IEEE International Conference on Computer Vision (ICCV), 2023. [Paper] [Code]
  • A. Jaiswal*, X. Zhang, S. Chan, and Z. Wang
    "Physics-Driven Turbulence Image Restoration with Stochastic Refinement”
    IEEE International Conference on Computer Vision (ICCV), 2023. [Paper] [Code]
  • Y. Han*, P. Wang*, S. Kundu, Y. Ding, and Z. Wang
    "Vision HGNN: An Image is More than a Graph of Nodes”
    IEEE International Conference on Computer Vision (ICCV), 2023. (Oral) [Paper] [Code]
  • T. Chen*, X. Chen*, X. Du, A. Rashwan, F. Yang, H. Chen, Z. Wang, and Y. Li
    "AdaMV-MoE: Adaptive Multi-Task Vision Mixture-of-Experts”
    IEEE International Conference on Computer Vision (ICCV), 2023. [Paper] [Code]
  • C. Li, B. Feng, Z. Fan*, P. Pan, and Z. Wang
    "StegaNeRF: Embedding Invisible Information within Neural Radiance Fields”
    IEEE International Conference on Computer Vision (ICCV), 2023. [Paper] [Code]
  • X. Xu, Z. Wang, G. Zhang, K. Wang, and H. Shi
    "Versatile Diffusion: Text, Images and Variations All in One Diffusion Model”
    IEEE International Conference on Computer Vision (ICCV), 2023. [Paper] [Code]
  • Y. Zhang, R. Cai*, T. Chen*, G. Zhang, H. Zhang, P. Chen, S. Chang, Z. Wang, and S. Liu
    "Robust Mixture-of-Expert Training for Convolutional Neural Networks”
    IEEE International Conference on Computer Vision (ICCV), 2023. (Oral) [Paper] [Code]
  • L. Khachatryan, A. Movsisyan, V. Tadevosyan, R. Henschel, Z. Wang, S. Navasardyan, and H. Shi
    "Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators”
    IEEE International Conference on Computer Vision (ICCV), 2023. (Oral) [Paper] [Code]
  • G. Holste*, Z. Jiang*, A. Jaiswal*, M. Hanna, S. Minkowitz, A. Legasto, J. Escalon, S. Steinberger, M. Bittman, T. Shen, Y. Ding, R. Summers, G. Shih, Y. Peng, and Z. Wang
    “How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?”
    Medical Image Computing and Computer Assisted Interventions (MICCAI), 2023. [Paper] [Code]
  • W. Chen*, W. Huang, and Z. Wang
    “No Free Lunch in Neural Architectures? A Joint Analysis of Expressivity, Convergence, and Generalization”
    International Conference on Automated Machine Learning (AutoML-Conf), 2023. [Paper] [Code]
  • X. Chen*, T. Chen*, W. Chen, A. Awadallah, Z. Wang, and Y. Cheng
    “DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models”
    Annual Meeting of the Association for Computational Linguistics (ACL), 2023. (Long) [Paper][Code]
  • A. Jaiswal*, S. Liu*, T. Chen*, Y. Ding, and Z. Wang
    “Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models”
    International Conference on Machine Learning (ICML), 2023. (Oral) [Paper] [Code]
  • A. Jaiswal*, S. Liu*, T. Chen*, Y. Ding, and Z. Wang
    “Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication”
    International Conference on Machine Learning (ICML), 2023. [Paper] [Code]
  • R. Cai*, Z. Zhang*, and Z. Wang
    “Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights?”
    International Conference on Machine Learning (ICML), 2023. [Paper] [Code]
  • P. Wang*, R. Panda, and Z. Wang
    “Data Efficient Neural Scaling Law via Model Reusing”
    International Conference on Machine Learning (ICML), 2023. [Paper] [Code]
  • W. Zheng*, S. Sharan*, A. Jaiswal*, K. Wang*, Y. Xi*, D. Xu*, and Z. Wang
    “Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation”
    International Conference on Machine Learning (ICML), 2023. [Paper] [Code]
  • X. Chen*, N. Vadori, T. Chen*, and Z. Wang
    “Learning to Optimize Differential Games”
    International Conference on Machine Learning (ICML), 2023. [Paper] [Code]
  • T. Huang, L. Yin*, Z. Zhang*, L. Shen, M. Fang, M. Pechenizkiy, Z. Wang, and S. Liu*
    “Are Large Kernels Better Teachers than Transformers for ConvNets?”
    International Conference on Machine Learning (ICML), 2023. [Paper] [Code]
  • Y. Ro, Z. Wang, V. Chidambaram, and A. Akella
    “Lowering the Pre-training Tax for Gradient-based Subset Training: A Lightweight Distributed Pre-Training Toolkit”
    International Conference on Machine Learning (ICML), 2023. [Paper] [Code]
  • J. Liu, X. Chen*, Z. Wang, W. Yin, and H. Cai
    “Towards Constituting Mathematical Structures for Learning to Optimize”
    International Conference on Machine Learning (ICML), 2023. [Paper] [Code]
  • D. Xu*, Y. Jiang*, P. Wang*, Z. Fan*, Y. Wang*, and Z. Wang
    "NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360◦ Views”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. (Highlight) [Paper] [Code]
  • Y. Jiang*, P. Hedman, B. Mildenhall, D. Xu*, J. Barron, Z. Wang, and T. Xue
    "AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. [Paper] [Code]
  • X. Gong*, S. Mohan, N. Dhingra, J. Bazin, Y. Li, Z. Wang, and R. Ranjan
    "MMG-Ego4D: Multimodal Generalization in Egocentric Action Recognition”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. [Paper] [Code]
  • H. Lu*, H. Tunanyan, K. Wang, S. Navasardyan, Z. Wang, and H. Shi
    "Specialist Diffusion: Plug-and-Play Sample-Efficient Fine-Tuning of Text-to-Image Diffusion Models to Learn Any Unseen Style”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. [Paper] [Code]
  • D. Hoang*, S. Liu*, R. Marculescu, and Z. Wang
    "Revisiting Pruning at Initialization Through the Lens of Ramanujan Graph”
    International Conference on Learning Representations (ICLR), 2023. (Oral) [Paper] [Code]
  • S. Liu*, T. Chen*, Z. Zhang*, X. Chen*, T. Huang, A. Jaiswal*, and Z. Wang
    "Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!”
    International Conference on Learning Representations (ICLR), 2023. (Spotlight) [Paper] [Code]
  • T. Chen*, Z. Zhang*, A. Jaiswal*, S. Liu*, and Z. Wang
    "Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers”
    International Conference on Learning Representations (ICLR), 2023. (Spotlight) [Paper] [Code]
  • P. Wang*, R. Panda, L. Hennigen, P. Greengard, L. Karlinsky, R. Feris, D. Cox, Z. Wang, and Y. Kim
    "Learning to Grow Pretrained Models for Efficient Transformer Training”
    International Conference on Learning Representations (ICLR), 2023. (Spotlight) [Paper] [Code]
  • S. Yu, J. Hong, H. Wang*, Z. Wang, and J. Zhou
    "Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection”
    International Conference on Learning Representations (ICLR), 2023. (Spotlight) [Paper] [Code]
  • S. Liu*, T. Chen*, X. Chen*, X. Chen*, Q. Xiao, B. Wu, T. Karkkainen, M. Pechenizkiy, D. Mocanu, and Z. Wang
    "More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity”
    International Conference on Learning Representations (ICLR), 2023. [Paper] [Code]
  • M. Varma*, P. Wang*, X. Chen*, T. Chen*, S. Venugopalan, and Z. Wang
    "Is Attention All That NeRF Needs?”
    International Conference on Learning Representations (ICLR), 2023. [Paper] [Code]
  • Z. Fan*, P. Wang*, Y. Jiang*, X. Gong*, D. Xu*, and Z. Wang
    "NeRF-SOS: Any-View Self-supervised Object Segmentation on Complex Scenes”
    International Conference on Learning Representations (ICLR), 2023. [Paper] [Code]
  • Z. Jiang*, Y. Chen, M. Liu, D. Chen, X. Dai, L. Yuan, Z. Liu, and Z. Wang
    "Layer Grafted Pre-training: Bridging Contrastive Learning and Masked Image Modeling For Label-Efficient Representations”
    International Conference on Learning Representations (ICLR), 2023. [Paper] [Code]
  • T. Chen*, C. Gong, D. Diaz, X. Chen*, J. Wells, Q. Liu, Z. Wang, A. Ellington, A. Dimakis, and A. Klivans
    "HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing”
    International Conference on Learning Representations (ICLR), 2023. [Paper] [Code]
  • P. Wang*, S. Yang, Y. Liu, Z. Wang, and P. Li
    "Equivariant Hypergraph Diffusion Neural Operators”
    International Conference on Learning Representations (ICLR), 2023. [Paper] [Code]
  • Y. You*, T. Chen*, Z. Wang, and Y. Shen
    "Graph Domain Adaptation via Theory-Grounded Spectral Regularization”
    International Conference on Learning Representations (ICLR), 2023. [Paper] [Code]
  • J. Yang, X. Chen*, T. Chen*, Z. Wang, and Y. Liang
    "M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation”
    International Conference on Learning Representations (ICLR), 2023. [Paper] [Code]
  • H. Fan, Z. Wang, Y. Yang, and M. Kankanhalli
    "Continuous-Discrete Convolution for (3+1)D Geometry-Sequence Modeling in Proteins”
    International Conference on Learning Representations (ICLR), 2023. [Paper] [Code]
  • H. Yang*, and Z. Wang
    "On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks”
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. [Paper] [Code]
  • J. Yang, T. Chen*, M. Zhu*, F. He, D. Tao, Y. Liang, and Z. Wang
    "Learning to Generalize Provably in Learning to Optimize”
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. [Paper] [Code]
  • H. Heaton, X. Chen*, Z. Wang, and W. Yin
    "Safeguarded Learned Convex Optimization”
    AAAI Conference on Artificial Intelligence (AAAI), 2023. [Paper] [Code]
  • J. Hong, H. Wang*, Z. Wang, and J. Zhou
    "Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning”
    AAAI Conference on Artificial Intelligence (AAAI), 2023. [Paper] [Code]
  • Z. Kong, H. Ma, G. Yuan, M. Sun, Y. Xie, P. Dong, X. Meng, X. Shen, H. Tang, M. Qin, T. Chen*, X. Ma, X. Xie, Z. Wang, and Y. Wang
    "Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training”
    AAAI Conference on Artificial Intelligence (AAAI), 2023. [Paper] [Code]
  • T. Huang, T. Chen*, M. Fang, V. Menkovski, J. Zhao, L. Yin, Y. Pei, D. Mocanu, Z. Wang, M. Pechenizkiy, and S. Liu*
    "You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained Graph Tickets”
    Learning on Graphs Conference (LoG), 2022. (Oral & Best Paper Award) [Paper] [Code]
  • Y. Han*, E. Huang, W. Zheng*, N. Rao, Z. Wang, and K. Subbian
    “Search Behavior Prediction: A Hypergraph Perspective”
    ACM International Conference on Web Search and Data Mining (WSDM), 2023. [Paper] [Code]
  • W. Chen*, W. Huang, X. Gong*, B. Hanin, and Z. Wang
    “Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis”
    Advances in Neural Information Processing Systems (NeurIPS), 2022. [Paper] [Code]
  • D. Xu*, P. Wang*, Y. Jiang*, Z. Fan*, and Z. Wang
    “Signal Processing for Implicit Neural Representations”
    Advances in Neural Information Processing Systems (NeurIPS), 2022. [Paper] [Code]
  • H. Liang*, Z. Fan*, R. Sarkar, Z. Jiang*, T. Chen*, K. Zou, Y. Cheng, C. Hao, and Z. Wang
    “M3ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design”
    Advances in Neural Information Processing Systems (NeurIPS), 2022. [Paper] [Code]
  • Z. Jiang*, X. Chen*, X. Huang, X. Du, D. Zhou, and Z. Wang
    “Back Razor: Memory-Efficient Transfer Learning by Self-Sparsified Backpropogation”
    Advances in Neural Information Processing Systems (NeurIPS), 2022. [Paper] [Code]
  • R. Cai*, Z. Zhang*, T. Chen*, X. Chen*, and Z. Wang
    “Randomized Channel Shuffling: Minimal-Overhead Backdoor Attack Detection without Clean Datasets”
    Advances in Neural Information Processing Systems (NeurIPS), 2022. [Paper] [Code]
  • S. Sharan*, W. Zheng*, K. Hsu, J. Xiong, A. Chen, and Z. Wang
    “Symbolic Distillation for Learned TCP Congestion Control”
    Advances in Neural Information Processing Systems (NeurIPS), 2022. [Paper] [Code]
  • A. Jaiswal*, P. Wang*, T. Chen*, J. Rousseau, Y. Ding, and Z. Wang
    “Old can be Gold: Better Gradient Flow can make Vanilla-GCNs Great Again”
    Advances in Neural Information Processing Systems (NeurIPS), 2022. [Paper] [Code]
  • H. Wang*, J. Hong, A. Zhang, J. Zhou, and Z. Wang
    “Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork”
    Advances in Neural Information Processing Systems (NeurIPS), 2022. [Paper] [Code]
  • J. Wu*, Y. Liang, F. Han, H. Akbari, Z. Wang, and C. Yu
    “Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization”
    Advances in Neural Information Processing Systems (NeurIPS), 2022. [Paper] [Code]
  • M. Varma*, X. Chen*, Z. Zhang*, T. Chen*, S. Venugopalan, and Z. Wang
    “Sparse Winning Tickets are Data-Efficient Image Recognizers”
    Advances in Neural Information Processing Systems (NeurIPS), 2022. [Paper] [Code]
  • T. Wei, Y. You*, T. Chen*, Y. Shen, J. He, and Z. Wang
    “Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative”
    Advances in Neural Information Processing Systems (NeurIPS), 2022. [Paper] [Code]
  • K. Duan, Z. Liu, P. Wang*, W. Zheng*, K. Zhou, T. Chen*, X. Hu, and Z. Wang
    “A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking”
    Advances in Neural Information Processing Systems, Track on Datasets and Benchmarks (NeurIPS Benchmark), 2022. [Paper] [Code]
  • 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]
  • 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*, Z. Fan*, 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) [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”
    ACM 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. (Long) [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) [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) [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
    “ShiftAddNet: A Hardware-Inspired Deep Network”
    Advances in Neural Information Processing Systems (NeurIPS), 2020. [Paper] [Code]
  • Y. Fu, H. You, Y. Zhao, Y. Wang, C. Li, K. Gopalakrishnan, Z. Wang, and Y. Lin
    “FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training”
    Advances in Neural Information Processing Systems (NeurIPS), 2020. [Paper] [Code]
  • H. Wang*, S. Gui, H. Yang, J. Liu, and Z. Wang
    “GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework”
    European Conference on Computer Vision (ECCV), 2020. (Spotlight) [Paper][Code]
  • S. Yang*, Z. Wang, J. Liu, and Z. Guo
    “Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches”
    European Conference on Computer Vision (ECCV), 2020. [Paper] [Code]
  • C. Li, T. Chen*, H. You, Z. Wang, and Y Lin
    “HALO: Hardware-Aware Learning to Optimize”
    European Conference on Computer Vision (ECCV), 2020. [Paper] [Code]
  • Z. Huo, A. PakBin, X. Chen*, N. Hurley, Y. Yuan*, X. Qian, Z. Wang, S. Huang, and B. Mortazavi
    “Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery”
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. [Paper] [Code]
  • W. Chen*, Z. Yu, Z. Wang, and A. Anandkumar
    “Automated Synthetic-to-Real Generalization”
    International Conference on Machine Learning (ICML), 2020. [Paper] [Code]
  • X. Chen*, W. Chen*, T. Chen*, Y. Yuan*, C. Gong, K. Chen, and Z. Wang
    “Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training”
    International Conference on Machine Learning (ICML), 2020. [Paper] [Code]
  • Y. You*, T. Chen*, Z. Wang, and Y. Shen
    “When Does Self-Supervision Help Graph Convolutional Networks?”
    International Conference on Machine Learning (ICML), 2020. [Paper] [Code]
  • R. Oftadeh, J. Shen*, Z. Wang, and D. Shell
    “Eliminating the Invariance on the Loss Landscape of Linear Autoencoders”
    International Conference on Machine Learning (ICML), 2020. [Paper] [Code]
  • Y. Fu, W. Chen*, H. Wang*, H. Li, Y. Lin, and Z. Wang
    “AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks”
    International Conference on Machine Learning (ICML), 2020. [Paper] [Code]
  • R. Ardywibowo, S. Boluki, X. Gong*, Z. Wang, and X. Qian
    “NADS: Neural Architecture Distribution Search for Uncertainty Awareness”
    International Conference on Machine Learning (ICML), 2020. [Paper] [Code]
  • Y. Zhao, X. Chen*, Y. Wang, C. Li, Y. Xie, Z. Wang, and Y. Lin
    “SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation”
    IEEE/ACM International Symposium on Computer Architecture (ISCA), 2020. [Paper] [Code]
  • T. Chen*, S. Liu, S. Chang, Y. Cheng, L. Amini, and Z. Wang
    “Adversarial Robustness: From Self-Supervised Pretraining to Fine-Tuning”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [Paper] [Code]
  • Z. Jiang*, B. Liu, S. Schulter, Z. Wang, and M. Chandraker
    “Peek-a-boo: Occlusion Reasoning in Indoor Scenes with Plane Representations”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. (Oral) [Paper] [Code]
  • Y. You*, T. Chen*, Z. Wang, and Y. Shen
    “L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [Paper] [Code]
  • T. Hu*, T. Chen*, H. Wang*, and Z. Wang
    "Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference"
    International Conference on Learning Representations (ICLR), 2020. [Paper] [Code]
  • W. Chen*, X. Gong*, X. Liu, Q. Zhang, Y. Li and Z. Wang
    "FasterSeg: Searching for Faster Real-time Semantic Segmentation"
    International Conference on Learning Representations (ICLR), 2020. [Paper] [Code]
  • H. Wang*, T. Chen*, Z. Wang, and K. Ma
    "I am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively"
    International Conference on Learning Representations (ICLR), 2020. [Paper] [Code]
  • H. You, C. Li, P. Xu, Y. Fu, Y. Wang, X. Chen*, R. Baraniuk, Z. Wang, and Y. Lin
    “Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks"
    International Conference on Learning Representations (ICLR), 2020. (Spotlight) [Paper] [Code]
  • J. Shen*, Y. Wang*, P. Xu, Y. Fu, Z. Wang, and Y. Lin
    “Fractional Skipping: Toward Finer-Grained Dynamic Inference”
    AAAI Conference on Artificial Intelligence (AAAI), 2020. [Paper] [Code]
  • S. Mohseni*, M. Pitale, J. Yadawa, and Z. Wang
    “Self-Supervised Learning for Generalizable Out-of-Distribution Detection”
    AAAI Conference on Artificial Intelligence (AAAI), 2020. [Paper] [Code]
  • Z. Jiang*, Y. Wang*, X. Chen*, P. Xu, Y. Zhao, Y. Lin, and Z. Wang
    “E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings”
    Advances in Neural Information Processing Systems (NeurIPS), 2019. [Paper] [Code]
  • S. Gui, H. Wang*, H. Yang, C. Yu, Z. Wang, and J. Liu
    “Model Compression with Adversarial Robustness: A Unified Optimization Framework”
    Advances in Neural Information Processing Systems (NeurIPS), 2019. [Paper] [Code]
  • Y. Cao, T. Chen*, Z. Wang, and Y. Shen
    “Learning to Optimize in Swarms”
    Advances in Neural Information Processing Systems (NeurIPS), 2019. [Paper] [Code]
  • X. Jia, S. Wang*, X. Liang, A. Balagopal, D. Nguyen, M. Yang, Z. Wang, X. Qian, X. Ji, and S. Jiang
    “Cone-Beam Computed Tomography (CBCT) Segmentation by Adversarial Learning Domain Adaptation”
    Medical Image Computing and Computer Assisted Interventions (MICCAI), 2019 [Paper] [Code]
  • R. Ardywibowo, G. Zhao, Z. Wang, B. Mortazavi, S. Huang, and X. Qian,
    “Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models”
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2019 [Paper] [Code]
  • S. Yang*, Z. Wang, Z Wang, N. Xu, J. Liu, and Z. Guo
    “Controllable Artistic Text Style Transfer via Shape-Matching GAN”
    IEEE International Conference on Computer Vision (ICCV), 2019. (Oral) [Paper] [Code]
  • Z. Wu*, K. Suresh, P. Narayanan, H. Xu, H. Kwon, and Z. Wang
    “Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach”
    IEEE International Conference on Computer Vision (ICCV), 2019. [Paper] [Code]
  • X. Gong*, S. Chang, Y. Jiang*, and Z. Wang
    “AutoGAN: Neural Architecture Search for Generative Adversarial Networks”
    IEEE International Conference on Computer Vision (ICCV), 2019. [Paper] [Code]
  • T. Chen*, S. Ding, J. Xie, Y. Yuan*, W. Chen*, Y. Yang, Z. Ren, and Z. Wang
    “ABD-Net: Attentive but Diverse Person Re-Identification”
    IEEE International Conference on Computer Vision (ICCV), 2019. [Paper] [Code]
  • O. Kupyn, T. Martyniuk, J. Wu*, and Z. Wang
    “DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better”
    IEEE International Conference on Computer Vision (ICCV), 2019. [Paper] [Code]
  • E. Ryu, J. Liu, S. Wang*, X. Chen*, Z. Wang, and W. Yin
    “Plug-and-Play Methods Provably Converge with Properly Trained Denoisers”
    International Conference on Machine Learning (ICML), 2019. [Paper] [Code]
  • W. Chen*, Z. Jiang*, Z. Wang, K. Cui, and X. Qian
    “Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-high Resolution Images”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. (Oral) [Paper] [Code]
  • S. Li, I. B. Araujo*, W. Ren, Z. Wang, E. K. Tokuda*, R. Hirata, R. Cesar, J. Zhang, X. Guo, and X. Cao
    “Single Image Deraining: A Comprehensive Benchmark Analysis”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. [Paper] [Code]
  • J. Liu, X. Chen*, Z. Wang, and W. Yin
    “ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA”
    International Conference on Learning Representations (ICLR), 2019. [Paper] [Code]
  • X. Chen*, J. Liu, Z. Wang, and W. Yin
    “Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds”
    Advances in Neural Information Processing Systems (NeurIPS), 2018. (Spotlight) [Paper] [Code]
  • N. Bansal*, X. Chen*, and Z. Wang
    “Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?”
    Advances in Neural Information Processing Systems (NeurIPS), 2018. [Paper] [Code]
  • Z. Wu*, Z. Wang, Z. Wang, and H. Jin
    “Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study”
    European Conference on Computer Vision (ECCV), 2018. [Paper] [Code]
  • M. Sun, I. Baytas, L. Zhan, Z. Wang, and J. Zhou
    “Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases”
    ACM Conference on Knowledge Discovery and Data Mining (KDD), 2018. [Paper] [Code]
  • J. Wu*, Y. Wang*, Z. Wu*, Z. Wang, A. Veeraraghavan, and Y. Lin
    “Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions”
    International Conference on Machine Learning (ICML), 2018. [Paper] [Code]

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