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Shandian Zhe
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2020 – today
- 2025
- [c60]Yingjing Wu, Ahmed Elmokashfi, Foivos Michelinakis, Jacobus E. van der Merwe, Shandian Zhe:
ADDER: Service-Specific Adaptive Data-Driven Radio Resource Control for Cellular-IoT. WoWMoM 2025: 157-166 - 2024
- [j15]Hongsup Oh, Roman Amici, Geoffrey F. Bomarito, Shandian Zhe, Robert M. Kirby, Jacob D. Hochhalter
:
Inherently interpretable machine learning solutions to differential equations. Eng. Comput. 40(4): 2349-2361 (2024) - [c59]Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney:
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels. AISTATS 2024: 2413-2421 - [c58]Shibo Li, Xin Yu, Wei W. Xing, Robert M. Kirby, Akil Narayan, Shandian Zhe:
Multi-Resolution Active Learning of Fourier Neural Operators. AISTATS 2024: 2440-2448 - [c57]Shikai Fang, Xin Yu, Zheng Wang, Shibo Li, Mike Kirby, Shandian Zhe:
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data. ICLR 2024 - [c56]Shikai Fang, Madison Cooley, Da Long, Shibo Li, Mike Kirby, Shandian Zhe:
Solving High Frequency and Multi-Scale PDEs with Gaussian Processes. ICLR 2024 - [c55]Shikai Fang, Qingsong Wen, Yingtao Luo, Shandian Zhe, Liang Sun:
BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition. ICML 2024 - [i51]Zhitong Xu, Shandian Zhe:
Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization. CoRR abs/2402.02746 (2024) - [i50]Da Long, Shandian Zhe:
Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems. CoRR abs/2402.11722 (2024) - [i49]Yutao Feng, Yintong Shang, Xiang Feng, Lei Lan, Shandian Zhe, Tianjia Shao, Hongzhi Wu, Kun Zhou, Hao Su, Chenfanfu Jiang, Yin Yang:
ElastoGen: 4D Generative Elastodynamics. CoRR abs/2405.15056 (2024) - [i48]Madison Cooley, Shandian Zhe, Robert M. Kirby, Varun Shankar:
Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation. CoRR abs/2406.02336 (2024) - [i47]Zachary Bastiani, Robert M. Kirby, Jacob D. Hochhalter, Shandian Zhe:
Complexity-Aware Deep Symbolic Regression with Robust Risk-Seeking Policy Gradients. CoRR abs/2406.06751 (2024) - [i46]Matthew Lowery, John Turnage, Zachary Morrow, John D. Jakeman, Akil Narayan, Shandian Zhe, Varun Shankar:
Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning. CoRR abs/2407.00809 (2024) - [i45]Madison Cooley, Varun Shankar, Robert M. Kirby, Shandian Zhe:
Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases. CoRR abs/2410.03496 (2024) - [i44]Madison Cooley, Robert M. Kirby, Shandian Zhe, Varun Shankar:
HyResPINNs: Adaptive Hybrid Residual Networks for Learning Optimal Combinations of Neural and RBF Components for Physics-Informed Modeling. CoRR abs/2410.03573 (2024) - 2023
- [j14]Michael Penwarden, Shandian Zhe, Akil Narayan, Robert M. Kirby:
A metalearning approach for Physics-Informed Neural Networks (PINNs): Application to parameterized PDEs. J. Comput. Phys. 477: 111912 (2023) - [j13]Michael Penwarden
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions. J. Comput. Phys. 493: 112464 (2023) - [c54]Shibo Li, Zheng Wang, Akil Narayan, Robert M. Kirby, Shandian Zhe:
Meta-Learning with Adjoint Methods. AISTATS 2023: 7239-7251 - [c53]Junyang Cai, Khai-Nguyen Nguyen, Nishant Shrestha, Aidan Good, Ruisen Tu, Xin Yu, Shandian Zhe, Thiago Serra:
Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions. CPAIOR 2023: 200-218 - [c52]Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, Yuriy Nevmyvaka:
Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation. ICML 2023: 4485-4513 - [c51]Shibo Li, Michael Penwarden, Yiming Xu, Conor Tillinghast, Akil Narayan, Mike Kirby, Shandian Zhe:
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks. ICML 2023: 19855-19881 - [c50]Shikai Fang, Xin Yu, Shibo Li, Zheng Wang, Mike Kirby, Shandian Zhe:
Streaming Factor Trajectory Learning for Temporal Tensor Decomposition. NeurIPS 2023 - [c49]Zheng Wang, Shikai Fang, Shibo Li, Shandian Zhe:
Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes. NeurIPS 2023 - [i43]Junyang Cai, Khai-Nguyen Nguyen, Nishant Shrestha, Aidan Good, Ruisen Tu, Xin Yu, Shandian Zhe, Thiago Serra:
Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions. CoRR abs/2301.07966 (2023) - [i42]Hongsup Oh, Roman Amici, Geoffrey F. Bomarito, Shandian Zhe, Robert M. Kirby, Jacob D. Hochhalter:
Genetic Programming Based Symbolic Regression for Analytical Solutions to Differential Equations. CoRR abs/2302.03175 (2023) - [i41]Michael Penwarden, Ameya D. Jagtap, Shandian Zhe, George Em Karniadakis, Robert M. Kirby:
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions. CoRR abs/2302.14227 (2023) - [i40]Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, Yuriy Nevmyvaka:
Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation. CoRR abs/2305.07247 (2023) - [i39]Shikai Fang, Qingsong Wen
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition. CoRR abs/2308.14906 (2023) - [i38]Shibo Li, Xin Yu, Wei W. Xing, Mike Kirby, Akil Narayan, Shandian Zhe:
Multi-Resolution Active Learning of Fourier Neural Operators. CoRR abs/2309.16971 (2023) - [i37]Da Long, Wei W. Xing
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels. CoRR abs/2310.05387 (2023) - [i36]Shikai Fang, Xin Yu, Shibo Li, Zheng Wang, Robert M. Kirby, Shandian Zhe:
Streaming Factor Trajectory Learning for Temporal Tensor Decomposition. CoRR abs/2310.17021 (2023) - [i35]Zheng Wang, Shikai Fang, Shibo Li, Shandian Zhe:
Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes. CoRR abs/2310.19666 (2023) - [i34]Shikai Fang, Madison Cooley, Da Long, Shibo Li, Robert M. Kirby, Shandian Zhe:
Solving High Frequency and Multi-Scale PDEs with Gaussian Processes. CoRR abs/2311.04465 (2023) - [i33]Shikai Fang, Xin Yu, Zheng Wang, Shibo Li, Mike Kirby, Shandian Zhe:
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data. CoRR abs/2311.04829 (2023) - [i32]Zheng Wang, Shibo Li, Shikai Fang, Shandian Zhe:
Diffusion-Generative Multi-Fidelity Learning for Physical Simulation. CoRR abs/2311.05606 (2023) - 2022
- [j12]Michael Penwarden
Multifidelity modeling for Physics-Informed Neural Networks (PINNs). J. Comput. Phys. 451: 110844 (2022) - [c48]Zheng Wang, Wei W. Xing, Robert M. Kirby, Shandian Zhe:
Physics Informed Deep Kernel Learning. AISTATS 2022: 1206-1218 - [c47]Shibo Li, Zheng Wang, Robert M. Kirby, Shandian Zhe:
Deep Multi-Fidelity Active Learning of High-Dimensional Outputs. AISTATS 2022: 1694-1711 - [c46]Shikai Fang, Akil Narayan, Robert M. Kirby, Shandian Zhe:
Bayesian Continuous-Time Tucker Decomposition. ICML 2022: 6235-6245 - [c45]Shibo Li, Robert M. Kirby, Shandian Zhe:
Decomposing Temporal High-Order Interactions via Latent ODEs. ICML 2022: 12797-12812 - [c44]Da Long, Zheng Wang, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney:
AutoIP: A United Framework to Integrate Physics into Gaussian Processes. ICML 2022: 14210-14222 - [c43]Conor Tillinghast, Zheng Wang, Shandian Zhe:
Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processes. ICML 2022: 21432-21448 - [c42]Zheng Wang, Yiming Xu, Conor Tillinghast, Shibo Li, Akil Narayan, Shandian Zhe:
Nonparametric Embeddings of Sparse High-Order Interaction Events. ICML 2022: 23237-23253 - [c41]Zheng Wang, Shandian Zhe:
Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition. ICML 2022: 23459-23469 - [c40]Xin Yu, Thiago Serra, Srikumar Ramalingam, Shandian Zhe:
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks. ICML 2022: 25668-25683 - [c39]Aidan Good, Jiaqi Lin, Xin Yu, Hannah Sieg, Mikey Ferguson, Shandian Zhe, Jerzy Wieczorek, Thiago Serra:
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm. NeurIPS 2022 - [c38]Shibo Li, Zheng Wang, Robert M. Kirby, Shandian Zhe:
Infinite-Fidelity Coregionalization for Physical Simulation. NeurIPS 2022 - [c37]Shibo Li, Jeff M. Phillips, Xin Yu, Robert M. Kirby, Shandian Zhe:
Batch Multi-Fidelity Active Learning with Budget Constraints. NeurIPS 2022 - [i31]Vahid Keshavarzzadeh, Shandian Zhe, Robert M. Kirby, Akil Narayan:
GP-HMAT: Scalable, O(n log(n)) Gaussian Process Regression with Hierarchical Low-Rank Matrices. CoRR abs/2201.00888 (2022) - [i30]Da Long, Zheng Wang, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney:
AutoIP: A United Framework to Integrate Physics into Gaussian Processes. CoRR abs/2202.12316 (2022) - [i29]Xin Yu, Thiago Serra, Srikumar Ramalingam, Shandian Zhe:
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks. CoRR abs/2203.04466 (2022) - [i28]Aidan Good, Jiaqi Lin
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm. CoRR abs/2206.02976 (2022) - [i27]Shibo Li, Zheng Wang, Robert M. Kirby, Shandian Zhe:
Infinite-Fidelity Coregionalization for Physical Simulation. CoRR abs/2207.00678 (2022) - [i26]Zheng Wang, Shandian Zhe:
Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition. CoRR abs/2207.02446 (2022) - [i25]Zheng Wang, Yiming Xu, Conor Tillinghast, Shibo Li, Akil Narayan, Shandian Zhe:
Nonparametric Embeddings of Sparse High-Order Interaction Events. CoRR abs/2207.03639 (2022) - [i24]Da Long, Nicole Mrvaljevic, Shandian Zhe, Bamdad Hosseini:
A Kernel Approach for PDE Discovery and Operator Learning. CoRR abs/2210.08140 (2022) - [i23]Shibo Li, Michael Penwarden, Robert M. Kirby, Shandian Zhe:
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks. CoRR abs/2210.12669 (2022) - [i22]Shibo Li, Jeff M. Phillips, Xin Yu, Robert M. Kirby, Shandian Zhe:
Batch Multi-Fidelity Active Learning with Budget Constraints. CoRR abs/2210.12704 (2022) - 2021
- [j11]Wei W. Xing
Deep coregionalization for the emulation of simulation-based spatial-temporal fields. J. Comput. Phys. 428: 109984 (2021) - [c36]Zheng Wang, Wei W. Xing, Robert Michael Kirby, Shandian Zhe:
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation. AISTATS 2021: 847-855 - [c35]Shikai Fang, Zheng Wang, Zhimeng Pan, Ji Liu, Shandian Zhe:
Streaming Bayesian Deep Tensor Factorization. ICML 2021: 3133-3142 - [c34]Conor Tillinghast, Shandian Zhe:
Nonparametric Decomposition of Sparse Tensors. ICML 2021: 10301-10311 - [c33]Zhimeng Pan, Zheng Wang, Jeff M. Phillips, Shandian Zhe:
Self-Adaptable Point Processes with Nonparametric Time Decays. NeurIPS 2021: 4594-4606 - [c32]Shibo Li, Robert M. Kirby, Shandian Zhe:
Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks. NeurIPS 2021: 25463-25475 - [c31]Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe, Robert M. Kirby, Michael W. Mahoney:
Characterizing possible failure modes in physics-informed neural networks. NeurIPS 2021: 26548-26560 - [c30]Shikai Fang, Robert M. Kirby, Shandian Zhe:
Bayesian streaming sparse Tucker decomposition. UAI 2021: 558-567 - [i21]Wei W. Xing, Akeel A. Shah, Peng Wang, Shandian Zhe, Qian Fu, Robert M. Kirby:
Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator for Multi-fidelity Simulations. CoRR abs/2104.03743 (2021) - [i20]Shibo Li, Robert M. Kirby, Shandian Zhe:
Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks. CoRR abs/2106.09884 (2021) - [i19]Michael Penwarden, Shandian Zhe, Akil Narayan, Robert M. Kirby:
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs). CoRR abs/2106.13361 (2021) - [i18]Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe, Robert M. Kirby, Michael W. Mahoney:
Characterizing possible failure modes in physics-informed neural networks. CoRR abs/2109.01050 (2021) - [i17]Shibo Li, Zheng Wang, Akil Narayan, Robert Michael Kirby, Shandian Zhe:
Meta-Learning with Adjoint Methods. CoRR abs/2110.08432 (2021) - [i16]Conor Tillinghast, Zheng Wang, Shandian Zhe:
Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processes. CoRR abs/2110.10082 (2021) - [i15]Michael Penwarden, Shandian Zhe, Akil Narayan, Robert M. Kirby:
Physics-Informed Neural Networks (PINNs) for Parameterized PDEs: A Metalearning Approach. CoRR abs/2110.13361 (2021) - 2020
- [j10]Yunjun Xiao, Jia Wei, Jiabing Wang, Qianli Ma, Shandian Zhe, Tolga Tasdizen:
Graph constraint-based robust latent space low-rank and sparse subspace clustering. Neural Comput. Appl. 32(12): 8187-8204 (2020) - [j9]Jinmian Ye, Guangxi Li, Di Chen, Haiqin Yang
Block-term tensor neural networks. Neural Networks 130: 11-21 (2020) - [c29]Wei W. Xing
Infinite ShapeOdds: Nonparametric Bayesian Models for Shape Representations. AAAI 2020: 6462-6469 - [c28]Zhimeng Pan, Zheng Wang, Shandian Zhe:
Scalable Nonparametric Factorization for High-Order Interaction Events. AISTATS 2020: 4325-4335 - [c27]Shikai Fang, Shandian Zhe, Kuang-chih Lee, Kai Zhang, Jennifer Neville:
Online Bayesian Sparse Learning with Spike and Slab Priors. ICDM 2020: 142-151 - [c26]Conor Tillinghast, Shikai Fang, Kai Zhang, Shandian Zhe:
Probabilistic Neural-Kernel Tensor Decomposition. ICDM 2020: 531-540 - [c25]Zheng Wang, Xinqi Chu, Shandian Zhe:
Self-Modulating Nonparametric Event-Tensor Factorization. ICML 2020: 9857-9867 - [c24]Shibo Li, Wei W. Xing
Scalable Gaussian Process Regression Networks. IJCAI 2020: 2456-2462 - [c23]Juefei Yuan, Tianyang Wang, Shandian Zhe, Yijuan Lu, Bo Li:
Semantic Tree-Based 3D Scene Model Recognition. MIPR 2020: 85-90 - [c22]Shibo Li, Wei W. Xing, Robert M. Kirby, Shandian Zhe:
Multi-Fidelity Bayesian Optimization via Deep Neural Networks. NeurIPS 2020 - [c21]Zhimeng Pan, Zheng Wang, Shandian Zhe:
Streaming Nonlinear Bayesian Tensor Decomposition. UAI 2020: 490-499 - [i14]Yun Yuan, Xianfeng Terry Yang, Zhao Zhang, Shandian Zhe:
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications. CoRR abs/2002.02374 (2020) - [i13]Shibo Li, Wei W. Xing, Mike Kirby, Shandian Zhe:
Scalable Variational Gaussian Process Regression Networks. CoRR abs/2003.11489 (2020) - [i12]Zheng Wang, Wei W. Xing
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation. CoRR abs/2006.04972 (2020) - [i11]Zheng Wang, Wei W. Xing, Robert Michael Kirby, Shandian Zhe:
Physics Regularized Gaussian Processes. CoRR abs/2006.04976 (2020) - [i10]Shibo Li, Wei W. Xing
Multi-Fidelity Bayesian Optimization via Deep Neural Networks. CoRR abs/2007.03117 (2020) - [i9]Shikai Fang, Zheng Wang, Zhimeng Pan, Ji Liu, Shandian Zhe:
Streaming Probabilistic Deep Tensor Factorization. CoRR abs/2007.07367 (2020) - [i8]Jinmian Ye, Guangxi Li, Di Chen, Haiqin Yang, Shandian Zhe, Zenglin Xu:
Block-term Tensor Neural Networks. CoRR abs/2010.04963 (2020) - [i7]Shibo Li, Robert M. Kirby, Shandian Zhe:
Deep Multi-Fidelity Active Learning of High-dimensional Outputs. CoRR abs/2012.00901 (2020)
2010 – 2019
- 2019
- [j8]Zenglin Xu
Variational Random Function Model for Network Modeling. IEEE Trans. Neural Networks Learn. Syst. 30(1): 318-324 (2019) - [j7]Liang Lan
Scaling Up Kernel SVM on Limited Resources: A Low-Rank Linearization Approach. IEEE Trans. Neural Networks Learn. Syst. 30(2): 369-378 (2019) - [c20]Shandian Zhe, Wei W. Xing, Robert M. Kirby:
Scalable High-Order Gaussian Process Regression. AISTATS 2019: 2611-2620 - [c19]Zheng Wang, Shandian Zhe:
Conditional Expectation Propagation. UAI 2019: 28-37 - [i6]Wei W. Xing
Deep Coregionalization for the Emulation of Spatial-Temporal Fields. CoRR abs/1910.07577 (2019) - [i5]Zheng Wang, Shandian Zhe:
Conditional Expectation Propagation. CoRR abs/1910.12360 (2019) - 2018
- [j6]Bin Liu, Lirong He, Yingming Li, Shandian Zhe, Zenglin Xu:
NeuralCP: Bayesian Multiway Data Analysis with Neural Tensor Decomposition. Cogn. Comput. 10(6): 1051-1061 (2018) - [c18]Lianjie Cao
Data-driven resource flexing for network functions visualization. ANCS 2018: 111-124 - [c17]Jinmian Ye, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, Zenglin Xu:
Learning Compact Recurrent Neural Networks With Block-Term Tensor Decomposition. CVPR 2018: 9378-9387 - [c16]Yishuai Du, Yimin Zheng
Probabilistic Streaming Tensor Decomposition. ICDM 2018: 99-108 - [c15]Shandian Zhe, Yishuai Du:
Stochastic Nonparametric Event-Tensor Decomposition. NeurIPS 2018: 6857-6867 - 2017
- [b1]Shandian Zhe:
Scalable Bayesian Nonparametrics and Sparse Learning for Hidden Relationship Discovery. Purdue University, USA, 2017 - [j5]Hao Peng, Yifan Yang, Shandian Zhe, Jian Wang, Michael Gribskov
DEIsoM: a hierarchical Bayesian model for identifying differentially expressed isoforms using biological replicates. Bioinform. 33(19): 3018-3027 (2017) - [c14]Shandian Zhe:
Scalable Nonparametric Tensor Analysis. AAAI 2017: 5058-5060 - [c13]Hao Peng, Shandian Zhe, Xiao Zhang, Yuan Qi:
Asynchronous Distributed Variational Gaussian Process for Regression. ICML 2017: 2788-2797 - [c12]Bin Liu, Zenglin Xu, Bo Dai, Haoli Bai, Xianghong Fang, Yazhou Ren
Learning from semantically dependent multi-tasks. IJCNN 2017: 3498-3505 - [i4]Jinmian Ye, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, Zenglin Xu:
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition. CoRR abs/1712.05134 (2017) - 2016
- [j4]Mohammad Adnan Rajib
SWATShare - A web platform for collaborative research and education through online sharing, simulation and visualization of SWAT models. Environ. Model. Softw. 75: 498-512 (2016) - [j3]Zenglin Xu, Shandian Zhe, Yuan Qi, Peng Yu:
Association Discovery and Diagnosis of Alzheimer's Disease with Bayesian Multiview Learning. J. Artif. Intell. Res. 56: 247-268 (2016) - [c11]Shandian Zhe, Yuan Qi, Youngja Park, Zenglin Xu, Ian M. Molloy, Suresh Chari:
DinTucker: Scaling Up Gaussian Process Models on Large Multidimensional Arrays. AAAI 2016: 2386-2392 - [c10]Syed Abbas Zilqurnain Naqvi, Shandian Zhe, Yuan Qi, Yifan Yang, Jieping Ye:
Fast Laplace Approximation for Sparse Bayesian Spike and Slab Models. IJCAI 2016: 1867-1973 - [c9]Kai Zhang, Shandian Zhe, Chaoran Cheng, Zhi Wei
Annealed Sparsity via Adaptive and Dynamic Shrinking. KDD 2016: 1325-1334 - [c8]Shandian Zhe, Kai Zhang, Pengyuan Wang, Kuang-chih Lee, Zenglin Xu, Yuan Qi, Zoubin Ghahramani:
Distributed Flexible Nonlinear Tensor Factorization. NIPS 2016: 920-928 - [c7]Changde Du, Changying Du, Shandian Zhe, Ali Luo
Bayesian Group Feature Selection for Support Vector Learning Machines. PAKDD (1) 2016: 239-252 - [i3]Shandian Zhe, Pengyuan Wang, Kuang-chih Lee, Zenglin Xu, Jian Yang, Youngja Park, Yuan Qi:
Distributed Flexible Nonlinear Tensor Factorization. CoRR abs/1604.07928 (2016) - 2015
- [c6]Shandian Zhe, Zenglin Xu, Yuan Qi, Peng Yu:
Sparse Bayesian Multiview Learning for Simultaneous Association Discovery and Diagnosis of Alzheimer's Disease. AAAI 2015: 1966-1972 - [c5]Changying Du, Shandian Zhe, Fuzhen Zhuang, Yuan Qi, Qing He, Zhongzhi Shi:
Bayesian Maximum Margin Principal Component Analysis. AAAI 2015: 2582-2588 - [c4]Shandian Zhe, Zenglin Xu, Xinqi Chu, Yuan (Alan) Qi, Youngja Park:
Scalable Nonparametric Multiway Data Analysis. AISTATS 2015 - 2014
- [c3]Shandian Zhe, Zenglin Xu, Yuan Qi, Peng Yu:
Joint Association Discovery and Diagnosis of Alzheimer's Disease by Supervised Heterogeneous Multiview Learning. Pacific Symposium on Biocomputing 2014: 300-311 - 2013
- [j2]Shandian Zhe, Syed A. Z. Naqvi, Yifan Yang, Yuan Qi:
Joint network and node selection for pathway-based genomic data analysis. Bioinform. 29(16): 1987-1996 (2013) - [i2]Shandian Zhe, Zenglin Xu, Yuan Qi, Peng Yu:
Supervised Heterogeneous Multiview Learning for Joint Association Study and Disease Diagnosis. CoRR abs/1304.7284 (2013) - [i1]Shandian Zhe, Yuan Qi, Youngja Park, Ian M. Molloy, Suresh Chari:
DinTucker: Scaling up Gaussian process models on multidimensional arrays with billions of elements. CoRR abs/1311.2663 (2013) - 2011
- [j1]Tian Xia, Shandian Zhe, Jinsong Su, Qun Liu:
Conditional Random Fields for Machine Translation System Combination. Int. J. Asian Lang. Process. 21(3): 83-94 (2011) - 2010
- [c2]Tian Xia, Shandian Zhe, Qun Liu:
Conditional Random Fields for Machine Translation System Combination. IALP 2010: 237-240 - [c1]Shandian Zhe, Tian Xia, Xueqi Cheng:
Modeling Users' Information Goal Transitions and Satisfaction Judgment: Understanding the Full Search Process. Web Intelligence 2010: 431-434
Coauthor Index
[j15] [c59] [c58] [c57] [c56] [i48] [i47] [i45] [i44] [j14] [j13] [c54] [c51] [c50] [i42] [i41] [i38] [i37] [i36] [i34] [i33] [j12] [c48] [c47] [c46] [c45] [c44] [c38] [c37] [i31] [i30] [i27] [i23] [i22] [j11] [c36] [c32] [c31] [c30] [i21] [i20] [i19] [i18] [i17] [i15] [c29] [c24] [c22] [i13] [i12] [i11] [i10] [i7] [c20] [i6]
Yuan Qi 0001
aka: Yuan (Alan) Qi
aka: Yuan (Alan) Qi
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