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

  • E. Oikonomou, A. Vaid, G. Holste*, A. Coppi, R. McNamara, C. Baloescu, H. Krumholz, Z. Wang, D. Apakama, G. Nadkarni, R. Khera
    “Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound: a multi-center study”
    Lancet Digital Health, 2024. [Paper] [Code]
  • W. Zheng*, S. Sharan*, Z. Fan*, K. Wang*, Y. Xi*, and Z. Wang
    “Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression Search”
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024. [Paper] [Code]
  • H. Yang*, Y. Liang, X. Guo, L. Wu, and Z. Wang
    “Pruning Before Training May Improve Generalization, Provably”
    Journal of Machine Learning Research (JMLR), 2024. [Paper] [Code]
  • H. Yang*, Z. Jiang*, R. Zhang, Y. Liang, and Z. Wang
    “Neural Networks with Sparse Activation Induced by Large Bias: Tighter Analysis with Bias-Generalized NTK”
    Journal of Machine Learning Research (JMLR), 2024. [Paper] [Code]
  • D. Xu*, Y. Yuan, M. Mardani, S. Liu, J. Song, Z. Wang, and A. Vahdat
    “AGG: Amortized Generative 3D Gaussians for Single Image to 3D”
    Transactions on Machine Learning Research (TMLR), 2024. [Paper] [Code]
  • G. Holste*, M. Lin, R. Zhou, F. Wang, L. Liu, Q. Yan, S. Tassel, K. Kovacs, E. Chew, Z. Lu, Z. Wang, and Y. Peng
    “Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling”
    npj Digital Medicine, 2024. [Paper] [Code]
  • G. Holste*, Y. Zhou, S. Wang, A. Jaiswal, M. Lin, S. Zhuge, Y. Yang, D. Kim, T. Nguyen-Mau, M. Tran, J. Jeong, W. Park, J. Ryu, F. Hong, A. Verma, Y. Yamagishi, C. Kim, H. Seo, M. Kang, L. Celi, Z. Lu, R. Summers, G. Shih, Z. Wang, and Y. Peng
    “Towards Long-tailed, Multi-label Disease Classification from Chest X-ray”
    Medical Image Analysis, 2024. [Paper] [Code]
  • G. Li, D. Hoang*, K. Bhardwaj, M. Lin, Z. Wang, and R. Marculescu
    “Zero-Shot Neural Architecture Search: Challenges, Solutions, and Opportunities”
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024. [Paper] [Code]
  • E. Oikonomou, G. Holste*, N. Yuan, A. Coppi, R. McNamara, N. Haynes, A. Vora, E. Velazquez, F. Li, V. Menon, S. Kapadia, T. Gill, G. Nadkarni, H. Krumholz, Z. Wang, D. Ouyang, and R. Khera
    “A Multimodality Video-Based AI Biomarker for Aortic Stenosis Development and Progression”
    JAMA Cardiology, 2024. [Paper] [Code]
  • W. Chen*, X. Gong*, J. Wu*, Y. Wei, H. Shi, Z. Yan, Y. Yang, and Z. Wang
    “Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics”
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023. [Paper] [Code]
  • Z. Jiang*, G. Zheng, Y. Cheng, A. Awadallah, and Z. Wang
    “CR-MoE: Consistent Routed Mixture-of-Experts for Scaling Contrastive Learning”
    Transactions on Machine Learning Research (TMLR), 2023. [Paper] [Code]
  • 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]
  • X. Yang, Z. Wang, S. Hu, C. Kim, S. Yu, M. Pajic, R. Manohar, Y. Chen, and H. Li
    “Neuro-Symbolic Computing: Advancements and Challenges in Hardware-Software Co-Design”
    IEEE Transactions on Circuits and Systems II (TCAS-II), 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, Z. Wang, and 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]
  • 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]
  • 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), 2021. (IEEE SPS Young Author Best Paper Award, 2024) [Paper] [Code]
  • 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]
  • 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

  • Z. Li*, T. Chen*, L. Li, B. Li, and Z. Wang
    "Sparse Transfer Learning Accelerates and Enhances Certified Robustness”
    AAAI Conference on Artificial Intelligence (AAAI), 2025. [Paper] [Code]
  • Z. Fan*, K. Wang*, K. Wen, Z. Zhu*, D. Xu*, and Z. Wang
    "LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS"
    Advances in Neural Information Processing Systems (NeurIPS), 2024. (Spotlight) [Paper] [Code]
  • H. Hu*, Z. Fan*, T. Wu, Y. Xi*, S. Lee*, G. Pavlakos, and Z. Wang
    "Expressive Gaussian Human Avatars from Monocular RGB Video"
    Advances in Neural Information Processing Systems (NeurIPS), 2024. [Paper] [Code]
  • R. Cai*, Y. Ro, G. Kim, P. Wang*, B. Bejnordi, A. Akella, and Z. Wang
    "Read-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design"
    Advances in Neural Information Processing Systems (NeurIPS), 2024. [Paper] [Code]
  • Z. Zhang*, R. Chen*, S. Liu*, Z. Yao, O. Ruwase, B. Chen, X. Wu, and Z. Wang
    "Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding"
    Advances in Neural Information Processing Systems (NeurIPS), 2024. [Paper] [Code]
  • Z. Fan*, J. Zhang, W. Cong*, P. Wang*, R. Li, K. Wen, S. Zhou, A Kadambi, Z. Wang, D. Xu, B. Ivanovic, M. Pavone, and Y. Wang
    “Large Spatial Model: End-to-end Unposed Images to Semantic 3D”
    Advances in Neural Information Processing Systems (NeurIPS), 2024. [Paper] [Code]
  • H. Yang*, B. Kailkhura, Z. Wang, and Y. Liang
    “Training Dynamics of Transformers to Recognize Word Co-occurrence via Gradient Flow Analysis"
    Advances in Neural Information Processing Systems (NeurIPS), 2024. [Paper] [Code]
  • H. Liang, Y. Yin, D. Xu*, H. Liang*, Z. Wang, K. Plataniotis, Y. Zhao, and Y. Wei
    “Diffusion4D: Fast Spatial-temporal Consistent 4D generation via Video Diffusion Models"
    Advances in Neural Information Processing Systems (NeurIPS), 2024. [Paper] [Code]
  • H. Lu, Y. Zhou, S. Liu*, Z. Wang, M. Mahoney, and Y. Yang
    “AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models"
    Advances in Neural Information Processing Systems (NeurIPS), 2024. [Paper] [Code]
  • X. Zhao, G. Sun, R. Cai*, Y. Zhou, P. Li, P. Wang*, B. Tan, Y. He, L. Chen, Y. Liang, B. Chen, B. Yuan, H. Wang, A. Li, Z. Wang, and T. Chen*
    “Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild”
    Advances in Neural Information Processing Systems, Track on Datasets and Benchmarks (NeurIPS D & B), 2024. [Paper] [Code]
  • Z. Zhu*, Z. Fan*, Y. Jiang*, and Z. Wang
    “FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting”
    European Conference on Computer Vision (ECCV), 2024. [Paper] [Code]
  • S. Zhou, Z. Fan*, D. Xu*, H. Chang, P. Chari, T. Bharadwaj, S. You, Z. Wang, and A. Kadambi
    “DreamScene360: Unconstrained Text-to-3D Scene Generation with Panoramic Gaussian Splatting”
    European Conference on Computer Vision (ECCV), 2024. [Paper] [Code]
  • R. Li, Z. Fan*, B. Wang, P. Wang*, Z. Wang, and X. Wu
    “VersatileGaussian: Real-time Neural Rendering for Versatile Tasks using Gaussian Splatting”
    European Conference on Computer Vision (ECCV), 2024. [Paper] [Code]
  • Q. Li, J. Hong*, C. Xie, J. Tan, R. Xin, J. Hou, X. Yin, Z. Wang, D. Hendrycks, Z. Wang, B. Li, B. He, and D. Song
    “LLM-PBE: Assessing Data Privacy in Large Language Models”
    International Conference on Very Large Data Bases (VLDB), 2024. (Best Paper Finalist) [Paper] [Code]
  • L. Sun*, N. Bhatt*, J. Liu*, Z. Fan*, Z. Wang, T. Humphreys, and U. Topcu
    “MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements”
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024. [Paper] [Code]
  • R. Cai*, S. Muralidharan, G. Heinrich, H. Yin, Z. Wang, J. Kautz, and P. Molchanov
    “Flextron: Many-in-One Flexible Large Language Model”
    International Conference on Machine Learning (ICML), 2024. (Oral) [Paper] [Code]
  • R. Cai*, Y. Tian, Z. Wang, and B. Chen
    “LoCoCo: Dropping In Convolutions for Long Context Compression”
    International Conference on Machine Learning (ICML), 2024. [Paper] [Code]
  • L. Yin*, A. Jaiswal*, S. Liu*, S. Kundu, and Z. Wang
    “Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs Difficult Downstream Tasks in LLMs”
    International Conference on Machine Learning (ICML), 2024. [Paper] [Code]
  • L. Yin*, Y. Wu, Z. Zhang*, C. Hsieh, Y. Wang, Y. Jia, G. Li, A. Jaiswal*, M. Pechenizkiy, Y. Liang, M. Bendersky, Z. Wang, and S. Liu*
    “Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity”
    International Conference on Machine Learning (ICML), 2024. [Paper] [Code]
  • R. Chen*, T. Zhao, A. Jaiswal*, N. Shah, and Z. Wang
    “LLaGA: Large Language and Graph Assistant”
    International Conference on Machine Learning (ICML), 2024. [Paper] [Code]
  • J. Hong*, J. Duan, C. Zhang, Z. Li*, C. Xie, K. Lieberman, J. Diffenderfer, B. Bartoldson, A. Jaiswal*, K. Xu, B. Kailkhura, D. Hendrycks, D. Song, Z. Wang, and B. Li
    “Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression”
    International Conference on Machine Learning (ICML), 2024. [Paper] [Code]
  • Z. Li*, S. Liu*, T. Chen*, A. Jaiswal*, Z. Zhang*, D. Wang, R. Krishnamoorthi, S. Chang, Z. Wang
    “Sparse Cocktail: Every Sparse Pattern Every Sparse Ratio All At Once”
    International Conference on Machine Learning (ICML), 2024. [Paper] [Code]
  • J. Zhao, Z. Zhang*, B. Chen, Z. Wang, A. Anandkumar, and Y. Tian
    “GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection”
    International Conference on Machine Learning (ICML), 2024. (Oral) [Paper] [Code]
  • H. Dong, X. Yang, Z. Zhang*, Z. Wang, Y. Chi, and B. Chen
    “Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference”
    International Conference on Machine Learning (ICML), 2024. [Paper] [Code]
  • Y. Zhang, P. Li, J. Hong*, J. Li, Y. Zhang, W. Zheng*, P. Chen, J. Lee, W. Yin, M. Hong, Z. Wang, S. Liu, and T. Chen*
    “Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark”
    International Conference on Machine Learning (ICML), 2024. [Paper] [Code]
  • P. Wang*, D. Xu*, Z. Fan*, D. Wang, S. Mohan, F. Iandola, R. Ranjan, Y. Li, Q. liu, Z. Wang, and V. Chandra
    "Taming Mode Collapse in Score Distillation for Text-to-3D Generation”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. [Paper] [Code]
  • M. Varma, P. Wang*, Z. Fan*, Z. Wang, H. Su, and R. Ramamoorthi
    "Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. [Paper] [Code]
  • S. Zhou, H. Chang, S. Jiang, Z. Fan*, Z. Zhu*, D. Xu*, P. Chari, S. You, Z. Wang, and A. Kadambi
    "Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. (Highlight) [Paper] [Code]
  • V. Goel, E. Peruzzo, Y. Jiang*, D. Xu*, X. Xu, N. Sebe, T. Darrell, Z. Wang, H. Shi
    "PAIR Diffusion: A Comprehensive Multimodal Object-Level Image Editor”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. [Paper] [Code]
  • M. Ohanyan, H. Manukyan, Z. Wang, S. Navasardyan, and H. Shi
    "Zero-Painter: Training-Free Layout Control for Text-to-Image Synthesis”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. [Paper] [Code]
  • X. Xu, J. Guo, Z. Wang, G. Huang, I. Essa, and H. Shi
    "Prompt-Free Diffusion: Taking 'Text' out of Text-to-Image Diffusion Models”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. [Paper] [Code]
  • M. D'Incà, E. Peruzzo, M. Mancini, D. Xu*, V. Goel, X. Xu, Z. Wang, H. Shi, and N. Sebe
    "OpenBias: Open-set Bias Detection in Generative Models”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. (Highlight) [Paper] [Code]
  • Z. Zhang*, S. Liu*, R. Chen*, B. Kailkhura, B. Chen, and Z. Wang
    "Q-Hitter: A Better Token Oracle for Efficient LLM Inference via Sparse-Quantized KV Cache”
    Conference on Machine Learning and Systems (MLSys), 2024. [Paper] [Code]
  • Y. Yang, N. Bhatt*, T. Ingebrand, W. Ward, S. Carr, Z. Wang, and U. Topcu
    "Fine-Tuning Language Models Using Formal Methods Feedback”
    Conference on Machine Learning and Systems (MLSys), 2024. [Paper] [Code]
  • A. Jaiswal*, Z. Gan, X. Du, B. Zhang, Z. Wang, and Y. Yang
    "Compressing LLMs: The Truth is Rarely Pure and Never Simple”
    International Conference on Learning Representations (ICLR), 2024. [Paper] [Code]
  • J. Hong*, J. Wang, C. Zhang, Z. LI*, B. Li, and Z. Wang
    "DP-OPT: Make Large Language Model Your Differentially-Private Prompt Engineer”
    International Conference on Learning Representations (ICLR), 2024. (Spotlight) [Paper] [Code]
  • Y. Jiang*, H. Tang, J. Chang, L. Song, Z. Wang, and L. Cao
    "Efficient-3DiM: Learning a Generalizable Single-image Novel-view Synthesizer in One Day”
    International Conference on Learning Representations (ICLR), 2024. [Paper] [Code]
  • W. Chen*, J. Wu*, Z. Wang, and B. Hanin
    "Principled Architecture-aware Scaling of Hyperparameters”
    International Conference on Learning Representations (ICLR), 2024. [Paper] [Code]
  • P. Wang*, S. Yang, S. Li, Z. Wang, and P. Li
    "Polynomial Width is Sufficient for Set Representation with High-dimensional Features”
    International Conference on Learning Representations (ICLR), 2024. [Paper] [Code]
  • X. Chen*, Y. Yang, Z. Wang, and B. Mirzasoleiman
    "Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality”
    International Conference on Learning Representations (ICLR), 2024. [Paper] [Code]
  • Y. You*, R. Zhou, J. Park, H. Xu, C. Tian, Z. Wang, and Y. Shen
    "Latent 3D Graph Diffusion”
    International Conference on Learning Representations (ICLR), 2024. [Paper] [Code]
  • A. Isajanyan, A. Shatveryan, D. Kocharian, Z. Wang, and H. Shi
    "Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community”
    International Conference on Learning Representations (ICLR), 2024. (Spotlight) [Paper] [Code]
  • S. Yu, J. Hong*, H. Zhang, H. Wang*, Z. Wang, and J. Zhou
    "Safe and Robust Watermark Injection with a Single OoD Image”
    International Conference on Learning Representations (ICLR), 2024. [Paper] [Code]
  • D. Sow, S. Lin, Z. Wang, and Y. Liang
    "Doubly Robust Instance-Reweighted Adversarial Training”
    International Conference on Learning Representations (ICLR), 2024. [Paper] [Code]
  • 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
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    IEEE International Conference on Computer Vision (ICCV), 2023. [Paper] [Code]
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    "Robust Mixture-of-Expert Training for Convolutional Neural Networks”
    IEEE International Conference on Computer Vision (ICCV), 2023. (Oral) [Paper] [Code]
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    "Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators”
    IEEE International Conference on Computer Vision (ICCV), 2023. (Oral) [Paper] [Code]
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    “How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?”
    Medical Image Computing and Computer Assisted Interventions (MICCAI), 2023. [Paper] [Code]
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    “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]
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    “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]
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    “Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication”
    International Conference on Machine Learning (ICML), 2023. [Paper] [Code]
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    “Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights?”
    International Conference on Machine Learning (ICML), 2023. [Paper] [Code]
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    “Data Efficient Neural Scaling Law via Model Reusing”
    International Conference on Machine Learning (ICML), 2023. [Paper] [Code]
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    “Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation”
    International Conference on Machine Learning (ICML), 2023. [Paper] [Code]
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    “Learning to Optimize Differential Games”
    International Conference on Machine Learning (ICML), 2023. [Paper] [Code]
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    “Are Large Kernels Better Teachers than Transformers for ConvNets?”
    International Conference on Machine Learning (ICML), 2023. [Paper] [Code]
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    “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]
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    “Towards Constituting Mathematical Structures for Learning to Optimize”
    International Conference on Machine Learning (ICML), 2023. [Paper] [Code]
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    "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]
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    "AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. [Paper] [Code]
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    "MMG-Ego4D: Multimodal Generalization in Egocentric Action Recognition”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. [Paper] [Code]
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    "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]
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    "Revisiting Pruning at Initialization Through the Lens of Ramanujan Graph”
    International Conference on Learning Representations (ICLR), 2023. (Oral) [Paper] [Code]
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    "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]
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    "Learning to Grow Pretrained Models for Efficient Transformer Training”
    International Conference on Learning Representations (ICLR), 2023. (Spotlight) [Paper] [Code]
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    "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]
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    "More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity”
    International Conference on Learning Representations (ICLR), 2023. [Paper] [Code]
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    "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]
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    "Equivariant Hypergraph Diffusion Neural Operators”
    International Conference on Learning Representations (ICLR), 2023. [Paper] [Code]
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    "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]
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    "Continuous-Discrete Convolution for (3+1)D Geometry-Sequence Modeling in Proteins”
    International Conference on Learning Representations (ICLR), 2023. [Paper] [Code]
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    "On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks”
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. [Paper] [Code]
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    "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]
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    "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]
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    “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]
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    “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”
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    “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]
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    “Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization”
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    “Sparse Winning Tickets are Data-Efficient Image Recognizers”
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    “Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative”
    Advances in Neural Information Processing Systems (NeurIPS), 2022. [Paper] [Code]
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    “A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking”
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    “SinNeRF: Training Neural Radiance Field on Complex Scenes from a Single Image”
    European Conference on Computer Vision (ECCV), 2022. [Paper] [Code]
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    “Unified Implicit Neural Stylization”
    European Conference on Computer Vision (ECCV), 2022. [Paper] [Code]
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    “Scalable Learning to Optimize: A Learned Optimizer Can Train Big Models”
    European Conference on Computer Vision (ECCV), 2022. [Paper] [Code]
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    “Point Cloud Domain Adaptation via Masked Local 3D Structure Prediction”
    European Conference on Computer Vision (ECCV), 2022. [Paper] [Code]
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    “DnA: Improving Few-shot Transfer Learning with Low-Rank Decomposition and Alignment”
    European Conference on Computer Vision (ECCV), 2022. [Paper] [Code]
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    “Fast and High Quality Image Denoising via Malleable Convolution”
    European Conference on Computer Vision (ECCV), 2022. [Paper] [Code]
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    “A Simple Single-Scale Vision Transformer for Object Detection and Instance Segmentation”
    European Conference on Computer Vision (ECCV), 2022. [Paper] [Code]
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    “Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and A New Physics-Inspired Transformer Model”
    European Conference on Computer Vision (ECCV), 2022. [Paper] [Code]
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    “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]
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    “Removing Batch Normalization Boosts Adversarial Training”
    International Conference on Machine Learning (ICML), 2022. [Paper] [Code]
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    “Neural Implicit Dictionary Learning via Mixture-of-Expert Training”
    International Conference on Machine Learning (ICML), 2022. [Paper] [Code]
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    “Training Your Sparse Neural Network Better with Any Mask”
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    “Linearity Grafting: How Neuron Pruning Helps Certifiable Robustness”
    International Conference on Machine Learning (ICML), 2022. [Paper] [Code]
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    “Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets”
    International Conference on Machine Learning (ICML), 2022. [Paper] [Code]
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    “Data-Efficient Double-Win Lottery Tickets from Robust Pre-training”
    International Conference on Machine Learning (ICML), 2022. [Paper] [Code]
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    “VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty”
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    “AutoCoG: A Unified Data-Model Co-Search Framework for Graph Neural Networks”
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    “Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descent”
    ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2022. [Paper] [Code]
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    “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]
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    “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]
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    “CADTransformer: Panoptic Symbol Spotting Transformer for CAD Drawing”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. (Oral) [Paper] [Code]
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    “Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. [Paper] [Code]
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    “DiSparse: Disentangled Sparsification for Multitask Model Compression”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. [Paper] [Code]
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    “VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. [Paper] [Code]
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    “EI-CLIP: Entity-aware Interventional Contrastive Learning for E-commerce Cross-modal Retrieval”
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    “Symbolic Learning to Optimize: Towards Interpretability and Scalability”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
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    “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]
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    “Optimizer Amalgamation”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
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    “Sparsity Winning Twice: Better Robust Generalization from More Efficient Training”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
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    “Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
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    “Auto-Scaling Vision Transformers without Training”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
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    “Unified Visual Transformer Compression”
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    “Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, And No Retraining”
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    “Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods”
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    “Audio Lottery: Speech Recognition Made Ultra-Lightweight, Noise-Robust, and Transferable”
    International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]
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    “Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization”
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    “Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity”
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    “The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training”
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    “Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How”
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    “VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition”
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    “Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better”
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    “Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations”
    ACM International Conference on Web Search and Data Mining (WSDM), 2022. [Paper] [Code]
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    “TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up”
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    “AugMax: Adversarial Composition of Random Augmentations for Robust Training”
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    “Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective”
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    “The Elastic Lottery Ticket Hypothesis”
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    “Chasing Sparsity in Vision Transformers: An End-to-End Exploration”
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    “Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems”
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    “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]
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    “Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling”
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    “Hyperparameter Tuning is All You Need for LISTA”
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    “Stronger NAS with Weaker Predictors”
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