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Yi Zhou 0017
Person information
- affiliation: Texas A&M University, College Station, TX, USA
- affiliation (until 2024): University of Utah, Department of Electrical and Computer Engineering, Salt Lake City, UT, USA
Other persons with the same name
- Yi Zhou — disambiguation page
- Yi Zhou 0001
— Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Beijing, China
- Yi Zhou 0002 — Singapore Polytechnic,School of Electrical and Electronic Engineering, Singapore (and 1 more)
- Yi Zhou 0003 — Shanghai Jiao Tong University, Computer Science Department, China
- Yi Zhou 0004
- Yi Zhou 0005
- Yi Zhou 0006
- Yi Zhou 0007
- Yi Zhou 0008
- Yi Zhou 0009
- Yi Zhou 0010
- Yi Zhou 0011
- Yi Zhou 0012
- Yi Zhou 0013 — University of Western Sydney, School of Computing, Engineering and Mathematics, Australia
- Yi Zhou 0014
- Yi Zhou 0015
- Yi Zhou 0016
- Yi Zhou 0018 — Bytedance AI Lab, China (and 1 more)
- Yi Zhou 0019
- Yi Zhou 0020
- Yi Zhou 0022
- Yi Zhou 0023 — Adobe, USA (and 2 more)
- Yi Zhou 0024
- Yi Zhou 0025
- Yi Zhou 0026
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2020 – today
- 2025
- [j14]Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, Yi Zhou:
Deep learning of PDE correction and mesh adaption without automatic differentiation. Mach. Learn. 114(3): 61 (2025) - [j13]Qi Zhang, Yi Zhou, Shaofeng Zou:
Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance. Trans. Mach. Learn. Res. 2025 (2025) - [c53]Qi Zhang, Yi Zhou, Simon Khan, Ashley Prater-Bennette, Lixin Shen, Shaofeng Zou:
Revisiting Large-Scale Non-convex Distributionally Robust Optimization. ICLR 2025 - 2024
- [j12]Yue Wang, Yi Zhou, Shaofeng Zou
Finite-time error bounds for Greedy-GQ. Mach. Learn. 113(9): 5981-6018 (2024) - [j11]Yan Zhang
Boosting One-Point Derivative-Free Online Optimization via Residual Feedback. IEEE Trans. Autom. Control. 69(9): 6309-6316 (2024) - [c52]Qi Zhang, Yi Zhou, Ashley Prater-Bennette, Lixin Shen, Shaofeng Zou:
Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization. AAAI 2024: 8217-8225 - [c51]Chedi Morchdi, Cheng-Hsiang Chiu, Yi Zhou, Tsung-Wei Huang:
A Resource-efficient Task Scheduling System using Reinforcement Learning : Invited Paper. ASPDAC 2024: 89-95 - [c50]Cheng-Hsiang Chiu, Chedi Morchdi, Yi Zhou, Boyang Zhang, Che Chang, Tsung-Wei Huang:
Reinforcement Learning-Generated Topological Order for Dynamic Task Graph Scheduling. HPEC 2024: 1-7 - [c49]Ziyi Chen, Yi Zhou, Heng Huang:
On the Hardness of Constrained Cooperative Multi-Agent Reinforcement Learning. ICLR 2024 - [c48]Ziwei Guan, Yi Zhou, Yingbin Liang:
On the Hardness of Online Nonconvex Optimization with Single Oracle Feedback. ICLR 2024 - [c47]Yudan Wang, Yue Wang, Yi Zhou, Shaofeng Zou:
Non-Asymptotic Analysis for Single-Loop (Natural) Actor-Critic with Compatible Function Approximation. ICML 2024 - [i43]Qi Zhang, Yi Zhou, Ashley Prater-Bennette, Lixin Shen, Shaofeng Zou:
Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization. CoRR abs/2404.01200 (2024) - [i42]Qi Zhang, Yi Zhou, Shaofeng Zou:
Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance. CoRR abs/2404.01436 (2024) - [i41]Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, Yi Zhou:
End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver. CoRR abs/2404.11766 (2024) - [i40]Yudan Wang, Yue Wang, Yi Zhou, Shaofeng Zou:
Non-Asymptotic Analysis for Single-Loop (Natural) Actor-Critic with Compatible Function Approximation. CoRR abs/2406.01762 (2024) - 2023
- [j10]Shaocong Ma, Ziyi Chen, Shaofeng Zou, Yi Zhou:
Decentralized Robust V-learning for Solving Markov Games with Model Uncertainty. J. Mach. Learn. Res. 24: 371:1-371:40 (2023) - [j9]Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou:
Assisted Learning for Organizations with Limited Imbalanced Data. Trans. Mach. Learn. Res. 2023 (2023) - [c46]Joohyun Cho, Mingxi Liu, Yi Zhou, Rong-Rong Chen:
Multi-Agent Recurrent Deterministic Policy Gradient with Inter-Agent Communication. ACSSC 2023: 1394-1398 - [c45]Chedi Morchdi, Yi Zhou, Jie Ding, Bei Wang
Exploring Gradient Oscillation in Deep Neural Network Training. Allerton 2023: 1-7 - [c44]Ziwei Guan, Yi Zhou, Yingbin Liang:
Online Nonconvex Optimization with Limited Instantaneous Oracle Feedback. COLT 2023: 3328-3355 - [c43]Ziyi Chen, Yi Zhou, Yingbin Liang, Zhaosong Lu:
Generalized-Smooth Nonconvex Optimization is As Efficient As Smooth Nonconvex Optimization. ICML 2023: 5396-5427 - [c42]Cheng Chen, Jiawei Zhang, Jie Ding, Yi Zhou:
Assisted Unsupervised Domain Adaptation. ISIT 2023: 2482-2487 - [c41]Youjia Zhou, Yi Zhou, Jie Ding, Bei Wang:
Visualizing and Analyzing the Topology of Neuron Activations in Deep Adversarial Training. TAG-ML 2023: 134-145 - 2022
- [j8]Yan Zhang, Yi Zhou, Kaiyi Ji, Michael M. Zavlanos:
A new one-point residual-feedback oracle for black-box learning and control. Autom. 136: 110006 (2022) - [j7]Yi Zhou
Understanding generalization error of SGD in nonconvex optimization. Mach. Learn. 111(1): 345-375 (2022) - [j6]Ziyi Chen, Yi Zhou, Rong-Rong Chen:
Multi-Agent Off-Policy TDC with Near-Optimal Sample and Communication Complexities. Trans. Mach. Learn. Res. 2022 (2022) - [c40]Ziyi Chen, Shaocong Ma, Yi Zhou:
Sample Efficient Stochastic Policy Extragradient Algorithm for Zero-Sum Markov Game. ICLR 2022 - [c39]Ziyi Chen, Yi Zhou, Rong-Rong Chen, Shaofeng Zou:
Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis. ICML 2022: 3794-3834 - [c38]Ziyi Chen, Shaocong Ma, Yi Zhou:
Accelerated Proximal Alternating Gradient-Descent-Ascent for Nonconvex Minimax Machine Learning. ISIT 2022: 672-677 - [c37]Yudan Wang, Yue Wang, Yi Zhou, Alvaro Velasquez, Shaofeng Zou:
Data-Driven Robust Multi-Agent Reinforcement Learning. MLSP 2022: 1-6 - [c36]Ziyi Chen, Shaocong Ma, Yi Zhou:
Finding Correlated Equilibrium of Constrained Markov Game: A Primal-Dual Approach. NeurIPS 2022 - [c35]Shaocong Ma, Ziyi Chen, Yi Zhou, Kaiyi Ji, Yingbin Liang:
Data sampling affects the complexity of online SGD over dependent data. UAI 2022: 1296-1305 - [i39]Ziyi Chen, Bhavya Kailkhura, Yi Zhou:
A Fast and Convergent Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-level Optimization. CoRR abs/2203.16615 (2022) - [i38]Shaocong Ma, Ziyi Chen, Yi Zhou, Kaiyi Ji, Yingbin Liang:
Data Sampling Affects the Complexity of Online SGD over Dependent Data. CoRR abs/2204.00006 (2022) - [i37]Yue Wang, Yi Zhou, Shaofeng Zou:
Finite-Time Error Bounds for Greedy-GQ. CoRR abs/2209.02555 (2022) - 2021
- [j5]Qunwei Li, Bhavya Kailkhura, Rushil Anirudh, Jize Zhang
MR-GAN: Manifold Regularized Generative Adversarial Networks for Scientific Data. SIAM J. Math. Data Sci. 3(4): 1197-1222 (2021) - [j4]Kaiyi Ji
Understanding Estimation and Generalization Error of Generative Adversarial Networks. IEEE Trans. Inf. Theory 67(5): 3114-3129 (2021) - [c34]Joohyun Cho, Mingxi Liu
Communication-Free Two-Stage Multi-Agent DDPG under Partial States and Observations. ACSCC 2021: 459-463 - [c33]Ziyi Chen, Yi Zhou, Rongrong Chen:
Multi-Agent Off-Policy TDC with Near-Optimal Sample and Communication Complexity. ACSCC 2021: 504-508 - [c32]Ziyi Chen, Yi Zhou, Tengyu Xu, Yingbin Liang:
Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry. ICLR 2021 - [c31]Shaocong Ma, Ziyi Chen, Yi Zhou, Shaofeng Zou:
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity. ICLR 2021 - [c30]Cheng Chen, Bhavya Kailkhura, Ryan A. Goldhahn
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing. MASS 2021: 173-179 - [c29]Yue Wang, Shaofeng Zou, Yi Zhou:
Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation. NeurIPS 2021: 9747-9758 - [i36]Ziyi Chen, Yi Zhou, Tengyu Xu, Yingbin Liang:
Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry. CoRR abs/2102.04653 (2021) - [i35]Ziyi Chen, Yi Zhou, Rongrong Chen:
Multi-Agent Off-Policy TD Learning: Finite-Time Analysis with Near-Optimal Sample Complexity and Communication Complexity. CoRR abs/2103.13147 (2021) - [i34]Cheng Chen, Bhavya Kailkhura, Ryan A. Goldhahn, Yi Zhou:
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing. CoRR abs/2103.16031 (2021) - [i33]Shaocong Ma, Ziyi Chen, Yi Zhou, Shaofeng Zou:
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity. CoRR abs/2103.16377 (2021) - [i32]Yue Wang, Shaofeng Zou, Yi Zhou:
Finite-Sample Analysis for Two Time-scale Non-linear TDC with General Smooth Function Approximation. CoRR abs/2104.02836 (2021) - [i31]Ziyi Chen, Yi Zhou, Rongrong Chen, Shaofeng Zou:
Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis. CoRR abs/2109.03699 (2021) - [i30]Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou:
Assisted Learning for Organizations with Limited Data. CoRR abs/2109.09307 (2021) - [i29]Ziyi Chen, Qunwei Li, Yi Zhou:
Escaping Saddle Points in Nonconvex Minimax Optimization via Cubic-Regularized Gradient Descent-Ascent. CoRR abs/2110.07098 (2021) - [i28]Ziyi Chen, Shaocong Ma, Yi Zhou:
Accelerated Proximal Alternating Gradient-Descent-Ascent for Nonconvex Minimax Machine Learning. CoRR abs/2112.11663 (2021) - 2020
- [c28]Cheng Chen, Junjie Yang, Yi Zhou:
Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study. IEEE BigData 2020: 141-146 - [c27]Cheng Chen, Ziyi Chen, Yi Zhou, Bhavya Kailkhura:
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling. IEEE BigData 2020: 5017-5026 - [c26]Cat P. Le
Supervised Encoding for Discrete Representation Learning. ICASSP 2020: 3447-3451 - [c25]Chris Cannella, Jie Ding, Mohammadreza Soltani, Yi Zhou, Vahid Tarokh:
Perception-Distortion Trade-Off with Restricted Boltzmann Machines. ICASSP 2020: 4022-4026 - [c24]Tengyu Xu, Zhe Wang, Yi Zhou, Yingbin Liang:
Reanalysis of Variance Reduced Temporal Difference Learning. ICLR 2020 - [c23]Kaiyi Ji, Zhe Wang, Bowen Weng, Yi Zhou, Wei Zhang, Yingbin Liang:
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms. ICML 2020: 4762-4772 - [c22]Shaocong Ma, Yi Zhou:
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle. ICML 2020: 6565-6574 - [c21]Yi Zhou, Zhe Wang, Kaiyi Ji, Yingbin Liang, Vahid Tarokh:
Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization. IJCAI 2020: 1445-1451 - [c20]Shaocong Ma, Yi Zhou, Shaofeng Zou:
Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis. NeurIPS 2020 - [i27]Tengyu Xu, Zhe Wang, Yi Zhou, Yingbin Liang:
Reanalysis of Variance Reduced Temporal Difference Learning. CoRR abs/2001.01898 (2020) - [i26]Yi Zhou, Zhe Wang, Kaiyi Ji, Yingbin Liang, Vahid Tarokh:
Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization. CoRR abs/2002.11582 (2020) - [i25]Ziyi Chen, Yi Zhou:
Momentum with Variance Reduction for Nonconvex Composition Optimization. CoRR abs/2005.07755 (2020) - [i24]Yan Zhang, Yi Zhou, Kaiyi Ji, Michael M. Zavlanos:
Improving the Convergence Rate of One-Point Zeroth-Order Optimization using Residual Feedback. CoRR abs/2006.10820 (2020) - [i23]Shaocong Ma, Yi Zhou:
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle. CoRR abs/2007.03509 (2020) - [i22]Cheng Chen, Ziyi Chen, Yi Zhou, Bhavya Kailkhura:
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling. CoRR abs/2009.10748 (2020) - [i21]Yan Zhang, Yi Zhou, Kaiyi Ji, Michael M. Zavlanos:
Boosting One-Point Derivative-Free Online Optimization via Residual Feedback. CoRR abs/2010.07378 (2020) - [i20]Shaocong Ma, Yi Zhou, Shaofeng Zou:
Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis. CoRR abs/2010.13272 (2020) - [i19]Cheng Chen, Junjie Yang, Yi Zhou:
Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study. CoRR abs/2011.06702 (2020)
2010 – 2019
- 2019
- [j3]Yi Zhou
A simple convergence analysis of Bregman proximal gradient algorithm. Comput. Optim. Appl. 73(3): 903-912 (2019) - [j2]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
A note on inexact gradient and Hessian conditions for cubic regularized Newton's method. Oper. Res. Lett. 47(2): 146-149 (2019) - [c19]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization. AISTATS 2019: 2731-2740 - [c18]Jayanth Regatti, Gaurav Tendolkar, Yi Zhou, Abhishek Gupta, Yingbin Liang:
Distributed SGD Generalizes Well Under Asynchrony. Allerton 2019: 863-870 - [c17]Yi Feng, Yi Zhou, Vahid Tarokh:
Recurrent Neural Network-Assisted Adaptive Sampling for Approximate Computing. IEEE BigData 2019: 2240-2246 - [c16]Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang, Eric P. Xing:
Toward Understanding the Impact of Staleness in Distributed Machine Learning. ICLR (Poster) 2019 - [c15]Yi Zhou, Junjie Yang, Huishuai Zhang, Yingbin Liang, Vahid Tarokh:
SGD Converges to Global Minimum in Deep Learning via Star-convex Path. ICLR (Poster) 2019 - [c14]Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang:
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization. ICML 2019: 3100-3109 - [c13]Yi Zhou, Yi Feng, Vahid Tarokh, Vadas Gintautas, Jessee McClelland, Denis Garagic:
Multi-Level Mean-Shift Clustering for Single-Channel Radio Frequency Signal Separation. MLSP 2019: 1-6 - [c12]Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh:
SpiderBoost and Momentum: Faster Variance Reduction Algorithms. NeurIPS 2019: 2403-2413 - [c11]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
Cubic Regularization with Momentum for Nonconvex Optimization. UAI 2019: 313-322 - [i18]Yi Zhou, Junjie Yang, Huishuai Zhang, Yingbin Liang, Vahid Tarokh:
SGD Converges to Global Minimum in Deep Learning via Star-convex Path. CoRR abs/1901.00451 (2019) - [i17]Jayanth Regatti, Gaurav Tendolkar, Yi Zhou, Abhishek Gupta, Yingbin Liang:
Distributed SGD Generalizes Well Under Asynchrony. CoRR abs/1909.13391 (2019) - [i16]Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang:
Faster Stochastic Algorithms via History-Gradient Aided Batch Size Adaptation. CoRR abs/1910.09670 (2019) - [i15]Cat P. Le
Supervised Encoding for Discrete Representation Learning. CoRR abs/1910.11067 (2019) - [i14]Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang:
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization. CoRR abs/1910.12166 (2019) - 2018
- [j1]Yi Zhou, Yingbin Liang, Yaoliang Yu, Wei Dai, Eric P. Xing:
Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters. J. Mach. Learn. Res. 19: 19:1-19:32 (2018) - [c10]Yi Zhou, Yingbin Liang:
Critical Points of Linear Neural Networks: Analytical Forms and Landscape Properties. ICLR (Poster) 2018 - [c9]Yi Zhou, Zhe Wang, Yingbin Liang:
Convergence of Cubic Regularization for Nonconvex Optimization under KL Property. NeurIPS 2018: 3764-3773 - [i13]Yi Zhou, Yingbin Liang, Huishuai Zhang:
Generalization Error Bounds with Probabilistic Guarantee for SGD in Nonconvex Optimization. CoRR abs/1802.06903 (2018) - [i12]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
Sample Complexity of Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization. CoRR abs/1802.07372 (2018) - [i11]Tengyu Xu, Yi Zhou, Kaiyi Ji, Yingbin Liang:
Convergence of SGD in Learning ReLU Models with Separable Data. CoRR abs/1806.04339 (2018) - [i10]Yi Zhou, Zhe Wang, Yingbin Liang:
Convergence of Cubic Regularization for Nonconvex Optimization under KL Property. CoRR abs/1808.07382 (2018) - [i9]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
A Note on Inexact Condition for Cubic Regularized Newton's Method. CoRR abs/1808.07384 (2018) - [i8]Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang, Eric P. Xing:
Toward Understanding the Impact of Staleness in Distributed Machine Learning. CoRR abs/1810.03264 (2018) - [i7]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
Cubic Regularization with Momentum for Nonconvex Optimization. CoRR abs/1810.03763 (2018) - [i6]Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh:
SpiderBoost: A Class of Faster Variance-reduced Algorithms for Nonconvex Optimization. CoRR abs/1810.10690 (2018) - [i5]Qunwei Li, Bhavya Kailkhura, Rushil Anirudh, Yi Zhou, Yingbin Liang, Pramod K. Varshney:
MR-GAN: Manifold Regularized Generative Adversarial Networks. CoRR abs/1811.10427 (2018) - 2017
- [c8]Yi Zhou, Yingbin Liang:
Demixing sparse signals via convex optimization. ICASSP 2017: 4202-4206 - [c7]Qunwei Li, Yi Zhou, Yingbin Liang, Pramod K. Varshney:
Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization. ICML 2017: 2111-2119 - [c6]Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu, James Zou, Eric P. Xing:
Learning Latent Space Models with Angular Constraints. ICML 2017: 3799-3810 - [i4]Qunwei Li, Yi Zhou, Yingbin Liang, Pramod K. Varshney:
Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization. CoRR abs/1705.04925 (2017) - [i3]Yi Zhou, Yingbin Liang:
Characterization of Gradient Dominance and Regularity Conditions for Neural Networks. CoRR abs/1710.06910 (2017) - [i2]Yi Zhou, Yingbin Liang:
Critical Points of Neural Networks: Analytical Forms and Landscape Properties. CoRR abs/1710.11205 (2017) - 2016
- [c5]Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric P. Xing:
On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System. AISTATS 2016: 713-722 - [c4]Yi Zhou, Huishuai Zhang, Yingbin Liang:
On Compressive orthonormal Sensing. Allerton 2016: 299-305 - [c3]Yi Zhou, Huishuai Zhang, Yingbin Liang:
Geometrical properties and accelerated gradient solvers of non-convex phase retrieval. Allerton 2016: 331-335 - [c2]Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yaoliang Yu, Eric P. Xing:
Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting. UAI 2016 - 2015
- [c1]Huishuai Zhang, Yi Zhou, Yingbin Liang:
Analysis of Robust PCA via Local Incoherence. NIPS 2015: 1819-1827 - [i1]Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yaoliang Yu, Eric P. Xing:
Distributed Machine Learning via Sufficient Factor Broadcasting. CoRR abs/1511.08486 (2015)
Coauthor Index
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