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

  • A. Velasquez, N. Bhatt*, U. Topcu, Z. Wang, K. Sycara, S. Stepputtis, S. Neemad, and G. Vallabhae
    “Neuro-symbolic AI as an Antithesis to Scaling Laws”
    Proceedings of the National Academy of Sciences Nexus (PNAS Nexus), 2025. [Paper] [Code]
  • Z. Yang*, X. Chen*, B. Zhu, T. Chen*, and Z. Wang
    “Deep Learning for Accurate Diagnosis of Viral Infections through scRNA-seq Analysis: A Comprehensive Benchmark Study”
    Journal of Data-centric Machine Learning Research (DMLR), 2025. [Paper] [Code]
  • E. Oikonomou, A. Vaid, G. Holste*, A. Coppi, R. McNamara, C. Baloescu, H. Krumholz, Z. Wang, D. Apakama, G. Nadkarni, and 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
    “Random Pruning Over-parameterized Neural Networks Can Improve Generalization: A Training Dynamics Analysis”
    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

  • A. Jaiswal*, Y. Wang, L. Yin*, S. Liu*, R. Chen*, J. Zhao, A. Grama, Y. Tian, and Z. Wang
    “From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications”
    International Conference on Machine Learning (ICML), 2025. [Paper] [Code]
  • G. Kim, J. Li*, S. Gandham, O. Baldonado, A. Gangidi, P. Balaji, Z. Wang, and A. Akella
    “HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training”
    International Conference on Machine Learning (ICML), 2025. [Paper] [Code]
  • J. Zhu*, P. Wang*, R. Cai*, J. Lee, P. Li, and Z. Wang
    “Rethinking Addressing in Language Models via Contextualized Equivariant Positional Encoding”
    International Conference on Machine Learning (ICML), 2025. [Paper] [Code]
  • (α-β) L. Hong* and Z. Wang
    “On the Provable Separation of Scales in Maximal Update Parameterization”
    International Conference on Machine Learning (ICML), 2025. [Paper] [Code]
  • J. Li*, Z. Wang, Q. Liu
    “PIPA: Prior-Informed Preference Alignment: as Plain Statistical Estimation”
    International Conference on Machine Learning (ICML), 2025. [Paper] [Code]
  • Y. Ro*, Z. Zhang*, S. Kundu, Z. Wang, and A. Akella
    “On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear Attention for Long-Context LLM Serving"
    International Conference on Machine Learning (ICML), 2025. [Paper] [Code]
  • D. Xu*, Y. Jiang*, C. Huang, L. Song, T. Gernoth, L. Cao, Z. Wang, H. Tang
    “Cavia: Camera-controllable Multi-view Video Diffusion with View-Integrated Attention”
    International Conference on Machine Learning (ICML), 2025. [Paper] [Code]
  • Z. Wang and P. Wang*
    "Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: Theoretic Foundation for Neurosymbolic AI”
    International Conference on Neuro-symbolic Systems (NeuS), 2025. (DAPRA Disruptive Idea Paper Award) [Paper]
  • Y. Kong, Y. Yang, Y. Hwang, W. Du, S. Zohren, Z. Wang, M. Jin, and Q. Wen
    “Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement”
    Annual Meeting of the Association for Computational Linguistics (ACL), 2025. (Main) [Paper][Code]
  • H. Liu, Y. Wang*, C. Li, R. Cai*, K. Wang*, W. Li, P. Molchanov, P. Wang*, and Z Wang
    "FlexGS: Train Once, Deploy Everywhere with Many-in-One Flexible 3D Gaussian Splatting”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025. [Paper] [Code]
  • P. Wang*, Y. Wang*, D. Wang, S. Mohan, Z. Fan*, L. Wu, R. Cai*, Y. Yeh, Z. Wang, Q. Liu, R. Ranjan
    "Steepest Descent Density Control for Compact 3D Gaussian Splatting”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025. [Paper] [Code]
  • S. Zhou, H. Ren, Y. Weng, S. Zhang, Z. Wang, D. Xu*, Z. Fan*, S. You, Z. Wang, L Guibas, and A. Kadambi
    "Feature4X: Bridging Any Monocular Video to 4D Agentic AI with Versatile Gaussian Feature Fields”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025. [Paper] [Code]
  • R. Henschel, L. Khachatryan, D. Hayrapetyan, H. Poghosyan, V. Tadevosyan, Z. Wang, S. Navasardyan, and H. Shi
    "StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025. [Paper] [Code]
  • W. Zhao, Y. Han, J. Tang, Z. Li, Y. Song, K. Wang, Z. Wang, and Y. You
    "A Stitch in Time Saves Nine: Small VLM is a Precise Guidance for accelerating Large VLMs”
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025. [Paper] [Code]
  • H. Zhu, Z. Zhang*, W. Cong*, X. Liu, S. Park, V. Chandra, B. Long, D. Pan, Z. Wang, and J. Lee
    "APOLLO: SGD-like Memory, AdamW-level Performance"
    Conference on Machine Learning and Systems (MLSys), 2025. (Outstanding Paper Honorable Mention) [Paper] [Code]
  • N. Bhatt*, Y. Yang, R. Siva, D. Milan, U. Topcu, and Z. Wang
    "Know Where You’re Uncertain When Planning with Multimodal Foundation Models: A Formal Framework"
    Conference on Machine Learning and Systems (MLSys), 2025. [Paper] [Code]
  • W. Redman, Z. Wang, A. Ingrosso, and S. Goldt
    "On How Iterative Magnitude Pruning Discovers Local Receptive Fields in Fully Connected Neural Networks”
    Conference on Parsimony and Learning (CPAL), 2025. [Paper] [Code]
  • Z. Zhang*, A. Jaiswal*, L. Yin*, S. Liu*, J. Zhao, Y. Tian, and Z. Wang
    "Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients”
    Conference on Parsimony and Learning (CPAL), 2025. [Paper] [Code]
  • P. Wang*, R. Cai*, Y. Wang*, J. Zhu*, P. Srivastava, Z. Wang, and P. Li
    "Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing”
    International Conference on Learning Representations (ICLR), 2025. [Paper] [Code]
  • H. Yang*, Z. Wang, J. Lee, and Y. Liang
    "Transformers Provably Learn Two-Mixture of Linear Classification via Gradient Flow”
    International Conference on Learning Representations (ICLR), 2025. [Paper] [Code]
  • R. Cai*, S. Muralidharan, H. Yin, Z. Wang, J. Kautz, and P. Molchanov
    "LLaMaFlex: Many-in-one LLMs via Generalized Pruning and Weight Sharing”
    International Conference on Learning Representations (ICLR), 2025. [Paper] [Code]
  • Z. Zhang*, Z. Liu, Y. Tian, H. Khaitan, Z. Wang, and S. Li
    "R-Sparse: Rank-Aware Activation Sparsity for Efficient LLM Inference”
    International Conference on Learning Representations (ICLR), 2025. [Paper] [Code]
  • T. Huang, Z. Zhu, G. Jin, L. Liu, Z. Wang, and S. Liu*
    "SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training”
    International Conference on Learning Representations (ICLR), 2025. [Paper] [Code]
  • R. Li, P. Pan, B. Yang, D. Xu*, S. Zhou, X. Zhang, Z. Li, A. Kadambi, Z. Wang, Z. Tu, Z. Fan*
    "4K4DGen: Panoramic 4D Generation at 4K Resolution”
    International Conference on Learning Representations (ICLR), 2025. (Spotlight) [Paper] [Code]
  • H. Manukyan, A. Sargsyan, B. Atanyan, Z. Wang, S. Navasardyan, and H. Shi
    "HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion Models”
    International Conference on Learning Representations (ICLR), 2025. [Paper] [Code]
  • P. Wang*, Z. Fan*, D. Xu*, D. Wang, S. Mohan, F. Iandola, R. Ranjan, Y. Li, Q. liu, Z. Wang, and V. Chandra
    "SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity”
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2025. [Paper] [[Code]
  • 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. (Oral Pitch Finalist) [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”
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    “CADTransformer: Panoptic Symbol Spotting Transformer for CAD Drawing”
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    “Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free”
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    “You Are Caught Stealing My Winning Lottery Ticket! Making a Lottery Ticket Claim its Ownership”
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    “Graph Contrastive Learning Automated”
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    “A Unified Lottery Ticket Hypothesis for Graph Neural Networks”
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    “Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning”
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