| 2026 | AAAI | ElastoGen: 4D Generative Elastodynamics. | Yutao Feng, Yintong Shang, Xiang Feng, Lei Lan, Shandian Zhe, Tianjia Shao, Hongzhi Wu, Kun Zhou, Chenfanfu Jiang, Yin Yang |
| 2025 | AISTATS | Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems. | Da Long, Zhitong Xu, Qiwei Yuan, Yin Yang, Shandian Zhe |
| 2025 | ICLR | Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization. | Zhitong Xu, Haitao Wang, Jeff M. Phillips, Shandian Zhe |
| 2025 | ICML | Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation. | Da Long, Zhitong Xu, Guang Yang, Akil Narayan, Shandian Zhe |
| 2025 | ICML | Toward Efficient Kernel-Based Solvers for Nonlinear PDEs. | Zhitong Xu, Da Long, Yiming Xu, Guang Yang, Shandian Zhe, Houman Owhadi |
| 2025 | WoWMoM | ADDER: Service-Specific Adaptive Data-Driven Radio Resource Control for Cellular-IoT. | Yingjing Wu, Ahmed Elmokashfi, Foivos Michelinakis, Jacobus E. van der Merwe, Shandian Zhe |
| 2024 | AISTATS | Multi-Resolution Active Learning of Fourier Neural Operators. | Shibo Li, Xin Yu, Wei W. Xing, Robert M. Kirby, Akil Narayan, Shandian Zhe |
| 2024 | AISTATS | Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels. | Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney |
| 2024 | ICLR | Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data. | Shikai Fang, Xin Yu, Zheng Wang, Shibo Li, Mike Kirby, Shandian Zhe |
| 2024 | ICLR | Solving High Frequency and Multi-Scale PDEs with Gaussian Processes. | Shikai Fang, Madison Cooley, Da Long, Shibo Li, Mike Kirby, Shandian Zhe |
| 2024 | ICML | BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition. | Shikai Fang, Qingsong Wen, Yingtao Luo, Shandian Zhe, Liang Sun |
| 2023 | AISTATS | Meta-Learning with Adjoint Methods. | Shibo Li, Zheng Wang, Akil Narayan, Robert M. Kirby, Shandian Zhe |
| 2023 | CPAIOR | Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions. | Junyang Cai, Khai-Nguyen Nguyen, Nishant Shrestha, Aidan Good, Ruisen Tu, Xin Yu, Shandian Zhe, Thiago Serra |
| 2023 | ICML | Provably Convergent Schrdinger Bridge with Applications to Probabilistic Time Series Imputation. | Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, Yuriy Nevmyvaka |
| 2023 | ICML | Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks. | Shibo Li, Michael Penwarden, Yiming Xu, Conor Tillinghast, Akil Narayan, Mike Kirby, Shandian Zhe |
| 2022 | AISTATS | Deep Multi-Fidelity Active Learning of High-Dimensional Outputs. | Shibo Li, Zheng Wang, Robert M. Kirby, Shandian Zhe |
| 2022 | AISTATS | Physics Informed Deep Kernel Learning. | Zheng Wang, Wei W. Xing, Robert M. Kirby, Shandian Zhe |
| 2022 | ICML | The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks. | Xin Yu, Thiago Serra, Srikumar Ramalingam, Shandian Zhe |
| 2022 | ICML | Bayesian Continuous-Time Tucker Decomposition. | Shikai Fang, Akil Narayan, Robert M. Kirby, Shandian Zhe |
| 2022 | ICML | Decomposing Temporal High-Order Interactions via Latent ODEs. | Shibo Li, Robert M. Kirby, Shandian Zhe |
| 2022 | ICML | AutoIP: A United Framework to Integrate Physics into Gaussian Processes. | Da Long, Zheng Wang, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney |
| 2022 | ICML | Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processes. | Conor Tillinghast, Zheng Wang, Shandian Zhe |
| 2022 | ICML | Nonparametric Embeddings of Sparse High-Order Interaction Events. | Zheng Wang, Yiming Xu, Conor Tillinghast, Shibo Li, Akil Narayan, Shandian Zhe |
| 2022 | ICML | Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition. | Zheng Wang, Shandian Zhe |
| 2021 | AISTATS | Multi-Fidelity High-Order Gaussian Processes for Physical Simulation. | Zheng Wang, Wei W. Xing, Robert Michael Kirby, Shandian Zhe |
| 2021 | ICML | Streaming Bayesian Deep Tensor Factorization. | Shikai Fang, Zheng Wang, Zhimeng Pan, Ji Liu, Shandian Zhe |
| 2021 | ICML | Nonparametric Decomposition of Sparse Tensors. | Conor Tillinghast, Shandian Zhe |
| 2021 | UAI | Bayesian streaming sparse Tucker decomposition. | Shikai Fang, Robert M. Kirby, Shandian Zhe |
| 2020 | AAAI | Infinite ShapeOdds: Nonparametric Bayesian Models for Shape Representations. | Wei W. Xing, Shireen Y. Elhabian, Robert Michael Kirby, Ross T. Whitaker, Shandian Zhe |
| 2020 | AISTATS | Scalable Nonparametric Factorization for High-Order Interaction Events. | Zhimeng Pan, Zheng Wang, Shandian Zhe |
| 2020 | ICDM | Online Bayesian Sparse Learning with Spike and Slab Priors. | Shikai Fang, Shandian Zhe, Kuang-chih Lee, Kai Zhang, Jennifer Neville |
| 2020 | ICDM | Probabilistic Neural-Kernel Tensor Decomposition. | Conor Tillinghast, Shikai Fang, Kai Zhang, Shandian Zhe |
| 2020 | ICML | Self-Modulating Nonparametric Event-Tensor Factorization. | Zheng Wang, Xinqi Chu, Shandian Zhe |
| 2020 | IJCAI | Scalable Gaussian Process Regression Networks. | Shibo Li, Wei W. Xing, Robert M. Kirby, Shandian Zhe |
| 2020 | UAI | Streaming Nonlinear Bayesian Tensor Decomposition. | Zhimeng Pan, Zheng Wang, Shandian Zhe |
| 2019 | AISTATS | Scalable High-Order Gaussian Process Regression. | Shandian Zhe, Wei W. Xing, Robert M. Kirby |
| 2019 | UAI | Conditional Expectation Propagation. | Zheng Wang, Shandian Zhe |
| 2018 | CVPR | Learning Compact Recurrent Neural Networks With Block-Term Tensor Decomposition. | Jinmian Ye, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, Zenglin Xu |
| 2018 | ICDM | Probabilistic Streaming Tensor Decomposition. | Yishuai Du, Yimin Zheng, Kuang-chih Lee, Shandian Zhe |
| 2017 | AAAI | Scalable Nonparametric Tensor Analysis. | Shandian Zhe |
| 2017 | ICML | Asynchronous Distributed Variational Gaussian Process for Regression. | Hao Peng, Shandian Zhe, Xiao Zhang, Yuan Qi |
| 2017 | IJCNN | Learning from semantically dependent multi-tasks. | Bin Liu, Zenglin Xu, Bo Dai, Haoli Bai, Xianghong Fang, Yazhou Ren, Shandian Zhe |
| 2016 | AAAI | DinTucker: Scaling Up Gaussian Process Models on Large Multidimensional Arrays. | Shandian Zhe, Yuan Qi, Youngja Park, Zenglin Xu, Ian M. Molloy, Suresh Chari |
| 2016 | IJCAI | Fast Laplace Approximation for Sparse Bayesian Spike and Slab Models. | Syed Abbas Zilqurnain Naqvi, Shandian Zhe, Yuan Qi, Yifan Yang, Jieping Ye |
| 2016 | KDD | Annealed Sparsity via Adaptive and Dynamic Shrinking. | Kai Zhang, Shandian Zhe, Chaoran Cheng, Zhi Wei, Zhengzhang Chen, Haifeng Chen, Guofei Jiang, Yuan Qi, Jieping Ye |
| 2016 | PAKDD | Bayesian Group Feature Selection for Support Vector Learning Machines. | Changde Du, Changying Du, Shandian Zhe, Ali Luo, Qing He, Guoping Long |
| 2015 | AAAI | Bayesian Maximum Margin Principal Component Analysis. | Changying Du, Shandian Zhe, Fuzhen Zhuang, Yuan Qi, Qing He, Zhongzhi Shi |
| 2015 | AAAI | Sparse Bayesian Multiview Learning for Simultaneous Association Discovery and Diagnosis of Alzheimer's Disease. | Shandian Zhe, Zenglin Xu, Yuan Qi, Peng Yu |
| 2015 | AISTATS | Scalable Nonparametric Multiway Data Analysis. | Shandian Zhe, Zenglin Xu, Xinqi Chu, Yuan (Alan) Qi, Youngja Park |
| 2014 | PSB | Joint Association Discovery and Diagnosis of Alzheimer's Disease by Supervised Heterogeneous Multiview Learning. | Shandian Zhe, Zenglin Xu, Yuan Qi, Peng Yu |