Shandian Zhe

Proceedings of the 25th IEEE International Symposium on a World of Wireless, 2025

2024

Inherently interpretable machine learning solutions to differential equations.

[BibT_eX]

[DOI]

Eng. Comput., August, 2024

HyResPINNs: Adaptive Hybrid Residual Networks for Learning Optimal Combinations of Neural and RBF Components for Physics-Informed Modeling.

[BibT_eX]

[DOI]

CoRR, 2024

Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases.

[BibT_eX]

[DOI]

CoRR, 2024

Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning.

[BibT_eX]

[DOI]

CoRR, 2024

Complexity-Aware Deep Symbolic Regression with Robust Risk-Seeking Policy Gradients.

[BibT_eX]

[DOI]

CoRR, 2024

Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation.

[BibT_eX]

[DOI]

CoRR, 2024

ElastoGen: 4D Generative Elastodynamics.

[BibT_eX]

[DOI]

CoRR, 2024

Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems.

[BibT_eX]

[DOI]

Da Long

CoRR, 2024

Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization.

[BibT_eX]

[DOI]

Zhitong Xu

CoRR, 2024

BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Solving High Frequency and Multi-Scale PDEs with Gaussian Processes.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels.

[BibT_eX]

[DOI]

Da Long

Aditi S. Krishnapriyan

Michael W. Mahoney

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Multi-Resolution Active Learning of Fourier Neural Operators.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions.

[BibT_eX]

[DOI]

Michael Penwarden

Ameya D. Jagtap

George Em Karniadakis

J. Comput. Phys., November, 2023

A metalearning approach for Physics-Informed Neural Networks (PINNs): Application to parameterized PDEs.

[BibT_eX]

[DOI]

J. Comput. Phys., March, 2023

Diffusion-Generative Multi-Fidelity Learning for Physical Simulation.

[BibT_eX]

[DOI]

CoRR, 2023

BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition.

[BibT_eX]

[DOI]

CoRR, 2023

Genetic Programming Based Symbolic Regression for Analytical Solutions to Differential Equations.

[BibT_eX]

[DOI]

CoRR, 2023

Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Streaming Factor Trajectory Learning for Temporal Tensor Decomposition.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions.

[BibT_eX]

[DOI]

Proceedings of the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 2023

Meta-Learning with Adjoint Methods.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Multifidelity modeling for Physics-Informed Neural Networks (PINNs).

[BibT_eX]

[DOI]

J. Comput. Phys., 2022

Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2022

A Kernel Approach for PDE Discovery and Operator Learning.

[BibT_eX]

[DOI]

CoRR, 2022

GP-HMAT: Scalable, O(n log(n)) Gaussian Process Regression with Hierarchical Low-Rank Matrices.

[BibT_eX]

[DOI]

CoRR, 2022

Batch Multi-Fidelity Active Learning with Budget Constraints.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Infinite-Fidelity Coregionalization for Physical Simulation.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Nonparametric Embeddings of Sparse High-Order Interaction Events.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processes.

[BibT_eX]

[DOI]

Conor Tillinghast

Proceedings of the International Conference on Machine Learning, 2022

AutoIP: A United Framework to Integrate Physics into Gaussian Processes.

[BibT_eX]

[DOI]

Da Long

Aditi S. Krishnapriyan

Michael W. Mahoney

Proceedings of the International Conference on Machine Learning, 2022

Decomposing Temporal High-Order Interactions via Latent ODEs.

[BibT_eX]

[DOI]

Shibo Li

Proceedings of the International Conference on Machine Learning, 2022

Bayesian Continuous-Time Tucker Decomposition.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Physics Informed Deep Kernel Learning.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Deep Multi-Fidelity Active Learning of High-Dimensional Outputs.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Deep coregionalization for the emulation of simulation-based spatial-temporal fields.

[BibT_eX]

[DOI]

J. Comput. Phys., 2021

Physics-Informed Neural Networks (PINNs) for Parameterized PDEs: A Metalearning Approach.

[BibT_eX]

[DOI]

CoRR, 2021

Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator for Multi-fidelity Simulations.

[BibT_eX]

[DOI]

CoRR, 2021

Bayesian streaming sparse Tucker decomposition.

[BibT_eX]

[DOI]

Shikai Fang

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Self-Adaptable Point Processes with Nonparametric Time Decays.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks.

[BibT_eX]

[DOI]

Shibo Li

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Characterizing possible failure modes in physics-informed neural networks.

[BibT_eX]

[DOI]

Aditi S. Krishnapriyan

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Nonparametric Decomposition of Sparse Tensors.

[BibT_eX]

[DOI]

Conor Tillinghast

Proceedings of the 38th International Conference on Machine Learning, 2021

Streaming Bayesian Deep Tensor Factorization.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Multi-Fidelity High-Order Gaussian Processes for Physical Simulation.

[BibT_eX]

[DOI]

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Block-term tensor neural networks.

[BibT_eX]

[DOI]

Neural Networks, 2020

Graph constraint-based robust latent space low-rank and sparse subspace clustering.

[BibT_eX]

[DOI]

Neural Comput. Appl., 2020

Deep Multi-Fidelity Active Learning of High-dimensional Outputs.

[BibT_eX]

[DOI]

Shibo Li

CoRR, 2020

Streaming Probabilistic Deep Tensor Factorization.

[BibT_eX]

[DOI]

CoRR, 2020

Physics Regularized Gaussian Processes.

[BibT_eX]

[DOI]

CoRR, 2020

Scalable Variational Gaussian Process Regression Networks.

[BibT_eX]

[DOI]

CoRR, 2020

Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications.

[BibT_eX]

[DOI]

CoRR, 2020

Streaming Nonlinear Bayesian Tensor Decomposition.

[BibT_eX]

[DOI]

Zhimeng Pan

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Multi-Fidelity Bayesian Optimization via Deep Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Semantic Tree-Based 3D Scene Model Recognition.

[BibT_eX]

[DOI]

Proceedings of the 3rd IEEE Conference on Multimedia Information Processing and Retrieval, 2020

Scalable Gaussian Process Regression Networks.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Self-Modulating Nonparametric Event-Tensor Factorization.

[BibT_eX]

[DOI]

Xinqi Chu

Proceedings of the 37th International Conference on Machine Learning, 2020

Probabilistic Neural-Kernel Tensor Decomposition.

[BibT_eX]

[DOI]

Proceedings of the 20th IEEE International Conference on Data Mining, 2020

Online Bayesian Sparse Learning with Spike and Slab Priors.

[BibT_eX]

[DOI]

Proceedings of the 20th IEEE International Conference on Data Mining, 2020

Scalable Nonparametric Factorization for High-Order Interaction Events.

[BibT_eX]

[DOI]

Zhimeng Pan

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Infinite ShapeOdds: Nonparametric Bayesian Models for Shape Representations.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Variational Random Function Model for Network Modeling.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2019

Scaling Up Kernel SVM on Limited Resources: A Low-Rank Linearization Approach.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2019

Deep Coregionalization for the Emulation of Spatial-Temporal Fields.

[BibT_eX]

[DOI]

CoRR, 2019

Conditional Expectation Propagation.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Scalable High-Order Gaussian Process Regression.

[BibT_eX]

[DOI]

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

NeuralCP: Bayesian Multiway Data Analysis with Neural Tensor Decomposition.

[BibT_eX]

[DOI]

Cogn. Comput., 2018

Stochastic Nonparametric Event-Tensor Decomposition.

[BibT_eX]

[DOI]

Yishuai Du

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Probabilistic Streaming Tensor Decomposition.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Data Mining, 2018

Learning Compact Recurrent Neural Networks With Block-Term Tensor Decomposition.

[BibT_eX]

[DOI]

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Data-driven resource flexing for network functions visualization.

[BibT_eX]

[DOI]

Proceedings of the 2018 Symposium on Architectures for Networking and Communications Systems, 2018

2017

Scalable Bayesian Nonparametrics and Sparse Learning for Hidden Relationship Discovery

[BibT_eX]

[DOI]

PhD thesis, 2017

DEIsoM: a hierarchical Bayesian model for identifying differentially expressed isoforms using biological replicates.

[BibT_eX]

[DOI]

Bioinform., 2017

Learning from semantically dependent multi-tasks.

[BibT_eX]

[DOI]

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Asynchronous Distributed Variational Gaussian Process for Regression.

[BibT_eX]

[DOI]

Proceedings of the 34th International Conference on Machine Learning, 2017

Scalable Nonparametric Tensor Analysis.

[BibT_eX]

[DOI]

Syed Abbas Zilqurnain Naqvi

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Association Discovery and Diagnosis of Alzheimer's Disease with Bayesian Multiview Learning.

[BibT_eX]

[DOI]

J. Artif. Intell. Res., 2016

SWATShare - A web platform for collaborative research and education through online sharing, simulation and visualization of SWAT models.

[BibT_eX]

[DOI]

Environ. Model. Softw., 2016

Distributed Flexible Nonlinear Tensor Factorization.

[BibT_eX]

[DOI]

CoRR, 2016

Bayesian Group Feature Selection for Support Vector Learning Machines.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2016

Distributed Flexible Nonlinear Tensor Factorization.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Annealed Sparsity via Adaptive and Dynamic Shrinking.

[BibT_eX]

[DOI]

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

Fast Laplace Approximation for Sparse Bayesian Spike and Slab Models.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

DinTucker: Scaling Up Gaussian Process Models on Large Multidimensional Arrays.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Scalable Nonparametric Multiway Data Analysis.

[BibT_eX]

[DOI]

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

Sparse Bayesian Multiview Learning for Simultaneous Association Discovery and Diagnosis of Alzheimer's Disease.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

Bayesian Maximum Margin Principal Component Analysis.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Joint Association Discovery and Diagnosis of Alzheimer's Disease by Supervised Heterogeneous Multiview Learning.

[BibT_eX]

[DOI]

Proceedings of the Biocomputing 2014: Proceedings of the Pacific Symposium, 2014

2013

Supervised Heterogeneous Multiview Learning for Joint Association Study and Disease Diagnosis

[BibT_eX]

[DOI]

CoRR, 2013

DinTucker: Scaling up Gaussian process models on multidimensional arrays with billions of elements.

[BibT_eX]

[DOI]

CoRR, 2013

Joint network and node selection for pathway-based genomic data analysis.

[BibT_eX]

[DOI]

Bioinform., 2013

2011

Conditional Random Fields for Machine Translation System Combination.

[BibT_eX]

[DOI]

Int. J. Asian Lang. Process., 2011

2010

Modeling Users' Information Goal Transitions and Satisfaction Judgment: Understanding the Full Search Process.

[BibT_eX]

[DOI]

Tian Xia

Xueqi Cheng

Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence, 2010

Conditional Random Fields for Machine Translation System Combination.

[BibT_eX]

[DOI]

Tian Xia