Shandian Zhe

48 papers · 2015–2026 · 8 conferences · across top CS/AI conferences

Achievements

+13 more ↓

🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🗺️ Taxonomy Completionist (20) 🌍 Conference Polyglot (8)

🧭 Keyword Pioneer 🏃 Academic Marathon (10) 🐣 Hot Topic Early Bird 🏆 Keyword Champion (3) 🏆 Grand Slam 🔬 Deep Specialist (15) 🤝 Dynamic Duo (18) 🔥 Unstoppable (11) 🚀 Conference Pioneer ⚡ Prolific Year (12) 📈 Trend Setter 💎 Century Club (47) 🗃️ Keyword Collector (60)

Conferences

ICML (16) NIPS (11) AISTATS (10) ICLR (3) UAI (3) AAAI (2) IJCAI (2) CVPR (1)

Top co-authors

Zheng Wang (18) Robert Kirby (15) Shibo Li (15) Shikai Fang (9) Wei Xing (8) Da Long (6) Xin Yu (6) Akil Narayan (6) Yuan Qi (4) Zhimeng Pan (4)

Keywords

variational inference (19) gaussian process (18) tensor decomposition (13) bayesian inference (6) tensor factorization (6) bayesian nonparametrics (5) nonparametric bayesian (4) dirichlet process (4) differential equation (3) model compression (3) partial differential equation (3) mutual information (3) acquisition function (3) physics-informed learning (3) spike-and-slab prior (3) uncertainty quantification (3) streaming datum (3) embedding learning (3) temporal dynamics (2) neural network pruning (2)

Papers

ElastoGen: 4D Generative Elastodynamics AAAI 2026 Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization ICLR 2025 Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation ICML 2025 Toward Efficient Kernel-Based Solvers for Nonlinear PDEs ICML 2025 Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems AISTATS 2025 Multi-Resolution Active Learning of Fourier Neural Operators AISTATS 2024 Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data ICLR 2024 Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels AISTATS 2024 Solving High Frequency and Multi-Scale PDEs with Gaussian Processes ICLR 2024 BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition ICML 2024 Meta-Learning with Adjoint Methods AISTATS 2023 Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes NIPS 2023 Streaming Factor Trajectory Learning for Temporal Tensor Decomposition NIPS 2023 Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation ICML 2023 Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks ICML 2023 Decomposing Temporal High-Order Interactions via Latent ODEs ICML 2022 Deep Multi-Fidelity Active Learning of High-Dimensional Outputs AISTATS 2022 The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks ICML 2022 Bayesian Continuous-Time Tucker Decomposition ICML 2022 Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition ICML 2022 Nonparametric Embeddings of Sparse High-Order Interaction Events ICML 2022 Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm NIPS 2022 Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processes ICML 2022 Infinite-Fidelity Coregionalization for Physical Simulation NIPS 2022 Batch Multi-Fidelity Active Learning with Budget Constraints NIPS 2022 Physics Informed Deep Kernel Learning AISTATS 2022 AutoIP: A United Framework to Integrate Physics into Gaussian Processes ICML 2022 Multi-Fidelity High-Order Gaussian Processes for Physical Simulation AISTATS 2021 Characterizing possible failure modes in physics-informed neural networks NIPS 2021 Streaming Bayesian Deep Tensor Factorization ICML 2021 Nonparametric Decomposition of Sparse Tensors ICML 2021 Bayesian streaming sparse Tucker decomposition UAI 2021 Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks NIPS 2021 Self-Adaptable Point Processes with Nonparametric Time Decays NIPS 2021 Infinite ShapeOdds: Nonparametric Bayesian Models for Shape Representations AAAI 2020 Streaming Nonlinear Bayesian Tensor Decomposition UAI 2020 Scalable Nonparametric Factorization for High-Order Interaction Events AISTATS 2020 Multi-Fidelity Bayesian Optimization via Deep Neural Networks NIPS 2020 Scalable Gaussian Process Regression Networks IJCAI 2020 Self-Modulating Nonparametric Event-Tensor Factorization ICML 2020 Conditional Expectation Propagation UAI 2019 Scalable High-Order Gaussian Process Regression AISTATS 2019 Stochastic Nonparametric Event-Tensor Decomposition NIPS 2018 Learning Compact Recurrent Neural Networks With Block-Term Tensor Decomposition CVPR 2018 Asynchronous Distributed Variational Gaussian Process for Regression ICML 2017 Fast Laplace Approximation for Sparse Bayesian Spike and Slab Models IJCAI 2016 Distributed Flexible Nonlinear Tensor Factorization NIPS 2016 Scalable Nonparametric Multiway Data Analysis AISTATS 2015