← Authors

Shandian Zhe

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

Achievements

🌍 Conference Polyglot (8) 🌈 Renaissance Researcher (5) 🐝 Cross-Pollinator (15) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (20) 🧭 Keyword Pioneer πŸƒ Academic Marathon (10) 🐣 Hot Topic Early Bird πŸ† Keyword Champion (3) πŸ† Grand Slam πŸ”¬ Deep Specialist (15) 🀝 Dynamic Duo (18) πŸ”₯ Unstoppable (11) πŸš€ Conference Pioneer ⚑ Prolific Year (12) πŸ“ˆ Trend Setter πŸ’Ž Century Club (47) πŸ—ƒοΈ 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) tensor factorization (6) bayesian inference (6) nonparametric bayesian (4) dirichlet process (4) bayesian nonparametric (4) differential equation (3) model compression (3) physics-informed learning (3) mutual information (3) acquisition function (3) spike-and-slab prior (3) streaming datum (3) embedding learning (3) uncertainty quantification (3) partial differential equation (3) neural network pruning (2) approximate inference (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 SchrΓΆdinger 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