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2020 – today
- 2023
[i98]Hongru Yang, Ziyu Jiang, Ruizhe Zhang, Zhangyang Wang, Yingbin Liang:
Convergence and Generalization of Wide Neural Networks with Large Bias. CoRR abs/2301.00327 (2023)- [i97]Hongru Yang, Yingbin Liang, Xiaojie Guo, Lingfei Wu, Zhangyang Wang:
Pruning Before Training May Improve Generalization, Provably. CoRR abs/2301.00335 (2023) - [i96]Daouda Sow, Sen Lin, Yingbin Liang, Junshan Zhang:
Algorithm Design for Online Meta-Learning with Task Boundary Detection. CoRR abs/2302.00857 (2023) - [i95]Ming Shi, Yingbin Liang, Ness B. Shroff:
Near-Optimal Adversarial Reinforcement Learning with Switching Costs. CoRR abs/2302.04374 (2023) - [i94]Ming Shi, Yingbin Liang, Ness B. Shroff:
A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints. CoRR abs/2302.04375 (2023) - [i93]Sen Lin, Peizhong Ju, Yingbin Liang, Ness B. Shroff:
Theory on Forgetting and Generalization of Continual Learning. CoRR abs/2302.05836 (2023) - [i92]Junjie Yang, Tianlong Chen, Mingkang Zhu, Fengxiang He, Dacheng Tao, Yingbin Liang, Zhangyang Wang:
Learning to Generalize Provably in Learning to Optimize. CoRR abs/2302.11085 (2023) - [i91]Junjie Yang, Xuxi Chen, Tianlong Chen, Zhangyang Wang, Yingbin Liang:
M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation. CoRR abs/2303.00039 (2023) - [i90]Yuan Cheng, Ruiquan Huang, Jing Yang, Yingbin Liang:
Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs. CoRR abs/2303.10859 (2023) - 2022
- [j64]Kaiyi Ji, Junjie Yang, Yingbin Liang:
Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning. J. Mach. Learn. Res. 23: 29:1-29:41 (2022) - [j63]Yi Zhou
, Yingbin Liang, Huishuai Zhang:
Understanding generalization error of SGD in nonconvex optimization. Mach. Learn. 111(1): 345-375 (2022) - [j62]Bin Dai, Chong Li, Yingbin Liang, Zheng Ma, Shlomo Shamai:
Self-Secure Capacity-Achieving Feedback Schemes of Gaussian Multiple-Access Wiretap Channels With Degraded Message Sets. IEEE Trans. Inf. Forensics Secur. 17: 1583-1596 (2022) - [c106]Sen Lin, Ming Shi, Anish Arora, Raef Bassily, Elisa Bertino, Constantine Caramanis, Kaushik R. Chowdhury, Eylem Ekici, Atilla Eryilmaz, Stratis Ioannidis, Nan Jiang, Gauri Joshi, Jim Kurose, Yingbin Liang, Zhiqiang Lin, Jia Liu, Mingyan Liu, Tommaso Melodia, Aryan Mokhtari, Rob Nowak, Sewoong Oh, Srini Parthasarathy, Chunyi Peng, Hulya Seferoglu, Ness Shroff, Sanjay Shakkottai, Kannan Srinivasan, Ameet Talwalkar, Aylin Yener, Lei Ying:
Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks. CIC 2022: 16-25 - [c105]Ziwei Guan, Tengyu Xu, Yingbin Liang:
PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method. ICLR 2022 - [c104]Sen Lin, Jialin Wan, Tengyu Xu, Yingbin Liang, Junshan Zhang:
Model-Based Offline Meta-Reinforcement Learning with Regularization. ICLR 2022 - [c103]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 - [c102]Huaqing Xiong, Tengyu Xu, Lin Zhao, Yingbin Liang, Wei Zhang:
Deterministic policy gradient: Convergence analysis. UAI 2022: 2159-2169 - [i89]Sen Lin, Jialin Wan, Tengyu Xu, Yingbin Liang, Junshan Zhang:
Model-Based Offline Meta-Reinforcement Learning with Regularization. CoRR abs/2202.02929 (2022) - [i88]Daouda Sow, Kaiyi Ji, Ziwei Guan, Yingbin Liang:
A Constrained Optimization Approach to Bilevel Optimization with Multiple Inner Minima. CoRR abs/2203.01123 (2022) - [i87]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) - [i86]Kaiyi Ji, Mingrui Liu, Yingbin Liang, Lei Ying:
Will Bilevel Optimizers Benefit from Loops. CoRR abs/2205.14224 (2022) - [i85]Yuan Cheng, Songtao Feng, Jing Yang, Hong Zhang, Yingbin Liang:
Provable Benefit of Multitask Representation Learning in Reinforcement Learning. CoRR abs/2206.05900 (2022) - [i84]Tengyu Xu, Yingbin Liang:
Provably Efficient Offline Reinforcement Learning with Trajectory-Wise Reward. CoRR abs/2206.06426 (2022) - [i83]Yu Huang, Yingbin Liang, Longbo Huang:
Provable Generalization of Overparameterized Meta-learning Trained with SGD. CoRR abs/2206.09136 (2022) - [i82]Ruiquan Huang, Jing Yang, Yingbin Liang:
Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-free RL. CoRR abs/2206.14057 (2022) - [i81]Xuyang Chen, Jingliang Duan, Yingbin Liang, Lin Zhao:
Global Convergence of Two-timescale Actor-Critic for Solving Linear Quadratic Regulator. CoRR abs/2208.08744 (2022) - 2021
- [j61]Qunwei Li, Bhavya Kailkhura, Rushil Anirudh, Jize Zhang, Yi Zhou, Yingbin Liang, Thomas Yong-Jin Han, Pramod K. Varshney:
MR-GAN: Manifold Regularized Generative Adversarial Networks for Scientific Data. SIAM J. Math. Data Sci. 3(4): 1197-1222 (2021) - [j60]Kaiyi Ji, Yi Zhou, Yingbin Liang:
Understanding Estimation and Generalization Error of Generative Adversarial Networks. IEEE Trans. Inf. Theory 67(5): 3114-3129 (2021) - [c101]Huaqing Xiong, Tengyu Xu, Yingbin Liang, Wei Zhang:
Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling. AAAI 2021: 10460-10468 - [c100]Tengyu Xu, Yingbin Liang:
Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms. AISTATS 2021: 811-819 - [c99]Ziwei Guan, Tengyu Xu, Yingbin Liang:
When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence. AISTATS 2021: 1117-1125 - [c98]Ziyi Chen, Yi Zhou, Tengyu Xu, Yingbin Liang:
Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry. ICLR 2021 - [c97]Kaiyi Ji, Junjie Yang, Yingbin Liang:
Bilevel Optimization: Convergence Analysis and Enhanced Design. ICML 2021: 4882-4892 - [c96]Tengyu Xu, Yingbin Liang, Guanghui Lan:
CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee. ICML 2021: 11480-11491 - [c95]Tengyu Xu, Zhuoran Yang, Zhaoran Wang, Yingbin Liang:
Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality. ICML 2021: 11581-11591 - [c94]Lin Zhao, Huaqing Xiong, Yingbin Liang:
Faster Non-asymptotic Convergence for Double Q-learning. NeurIPS 2021: 7242-7253 - [c93]Junjie Yang, Kaiyi Ji, Yingbin Liang:
Provably Faster Algorithms for Bilevel Optimization. NeurIPS 2021: 13670-13682 - [c92]Bowen Weng, Huaqing Xiong, Lin Zhao, Yingbin Liang, Wei Zhang:
Finite-time theory for momentum Q-learning. UAI 2021: 665-674 - [i80]Kaiyi Ji, Yingbin Liang:
Lower Bounds and Accelerated Algorithms for Bilevel Optimization. CoRR abs/2102.03926 (2021) - [i79]Ziyi Chen, Yi Zhou, Tengyu Xu, Yingbin Liang:
Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry. CoRR abs/2102.04653 (2021) - [i78]Tengyu Xu, Zhuoran Yang, Zhaoran Wang, Yingbin Liang:
Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality. CoRR abs/2102.11866 (2021) - [i77]Junjie Yang, Kaiyi Ji, Yingbin Liang:
Provably Faster Algorithms for Bilevel Optimization. CoRR abs/2106.04692 (2021) - [i76]Tengyu Xu, Zhuoran Yang, Zhaoran Wang, Yingbin Liang:
A Unified Off-Policy Evaluation Approach for General Value Function. CoRR abs/2107.02711 (2021) - [i75]Ziwei Guan, Tengyu Xu, Yingbin Liang:
PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method. CoRR abs/2110.06906 (2021) - [i74]Daouda Sow, Kaiyi Ji, Yingbin Liang:
ES-Based Jacobian Enables Faster Bilevel Optimization. CoRR abs/2110.07004 (2021) - [i73]Tianjiao Li, Ziwei Guan, Shaofeng Zou, Tengyu Xu, Yingbin Liang, Guanghui Lan:
Faster Algorithm and Sharper Analysis for Constrained Markov Decision Process. CoRR abs/2110.10351 (2021) - 2020
- [j59]Bin Dai, Chong Li, Yingbin Liang, H. Vincent Poor, Shlomo Shamai (Shitz):
Enhancing physical layer security via channel feedback: a survey. EURASIP J. Wirel. Commun. Netw. 2020(1): 58 (2020) - [j58]Zhe Wang, Yingbin Liang, Pengsheng Ji:
Spectral Algorithms for Community Detection in Directed Networks. J. Mach. Learn. Res. 21: 153:1-153:45 (2020) - [j57]Bin Dai, Chong Li, Yingbin Liang, Zheng Ma, Shlomo Shamai Shitz:
Impact of Action-Dependent State and Channel Feedback on Gaussian Wiretap Channels. IEEE Trans. Inf. Theory 66(6): 3435-3455 (2020) - [j56]Haoyu Fu, Yuejie Chi, Yingbin Liang:
Guaranteed Recovery of One-Hidden-Layer Neural Networks via Cross Entropy. IEEE Trans. Signal Process. 68: 3225-3235 (2020) - [c91]Ziwei Guan, Kaiyi Ji, Donald J. Bucci Jr., Timothy Y. Hu, Joseph Palombo, Michael Liston, Yingbin Liang:
Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack. AAAI 2020: 4036-4043 - [c90]Ziwei Guan, Timothy Y. Hu, Joseph Palombo, Michael J. Liston, Donald J. Bucci, Yingbin Liang:
Robust Dynamic Spectrum Access in Adversarial Environments. ICC 2020: 1-7 - [c89]Tengyu Xu, Zhe Wang, Yi Zhou, Yingbin Liang:
Reanalysis of Variance Reduced Temporal Difference Learning. ICLR 2020 - [c88]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 - [c87]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 - [c86]Bowen Weng, Huaqing Xiong, Yingbin Liang, Wei Zhang:
Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descent. IJCAI 2020: 3051-3057 - [c85]Bin Dai, Chong Li, Yingbin Liang, Zheng Ma, Shlomo Shamai Shitz:
On the Capacity of Gaussian Multiple-Access Wiretap Channels with Feedback. ISITA 2020: 397-401 - [c84]Bin Dai, Chong Li, Yingbin Liang, Zheng Ma, Shlomo Shamai Shitz:
Feedback Capacity of Gaussian Multiple-Access Wiretap Channel with Degraded Message Sets. ITW 2020: 1-5 - [c83]Kaiyi Ji, Jason D. Lee, Yingbin Liang, H. Vincent Poor:
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters. NeurIPS 2020 - [c82]Huaqing Xiong, Lin Zhao, Yingbin Liang, Wei Zhang:
Finite-Time Analysis for Double Q-learning. NeurIPS 2020 - [c81]Tengyu Xu, Zhe Wang, Yingbin Liang:
Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms. NeurIPS 2020 - [i72]Tengyu Xu, Zhe Wang, Yi Zhou, Yingbin Liang:
Reanalysis of Variance Reduced Temporal Difference Learning. CoRR abs/2001.01898 (2020) - [i71]Huaqing Xiong, Tengyu Xu, Yingbin Liang, Wei Zhang:
Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling. CoRR abs/2002.06286 (2020) - [i70]Ziwei Guan, Kaiyi Ji, Donald J. Bucci Jr., Timothy Y. Hu, Joseph Palombo, Michael Liston, Yingbin Liang:
Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack. CoRR abs/2002.07214 (2020) - [i69]Kaiyi Ji, Junjie Yang, Yingbin Liang:
Multi-Step Model-Agnostic Meta-Learning: Convergence and Improved Algorithms. CoRR abs/2002.07836 (2020) - [i68]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) - [i67]Tengyu Xu, Zhe Wang, Yingbin Liang:
Improving Sample Complexity Bounds for Actor-Critic Algorithms. CoRR abs/2004.12956 (2020) - [i66]Tengyu Xu, Zhe Wang, Yingbin Liang:
Non-asymptotic Convergence Analysis of Two Time-scale (Natural) Actor-Critic Algorithms. CoRR abs/2005.03557 (2020) - [i65]Tengyu Xu, Zhe Wang, Yingbin Liang, H. Vincent Poor:
Enhanced First and Zeroth Order Variance Reduced Algorithms for Min-Max Optimization. CoRR abs/2006.09361 (2020) - [i64]Kaiyi Ji, Jason D. Lee, Yingbin Liang, H. Vincent Poor:
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters. CoRR abs/2006.09486 (2020) - [i63]Ziwei Guan, Tengyu Xu, Yingbin Liang:
When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence. CoRR abs/2006.13506 (2020) - [i62]Bowen Weng, Huaqing Xiong, Yingbin Liang, Wei Zhang:
Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descent. CoRR abs/2007.07422 (2020) - [i61]Bin Dai, Chong Li, Yingbin Liang, Zheng Ma, Shlomo Shamai:
Feedback Capacities of Gaussian Multiple-Access Wiretap Channels. CoRR abs/2007.14555 (2020) - [i60]Bowen Weng, Huaqing Xiong, Lin Zhao, Yingbin Liang, Wei Zhang:
Momentum Q-learning with Finite-Sample Convergence Guarantee. CoRR abs/2007.15418 (2020) - [i59]Zhe Wang, Yingbin Liang, Pengsheng Ji:
Spectral Algorithms for Community Detection in Directed Networks. CoRR abs/2008.03820 (2020) - [i58]Huaqing Xiong, Lin Zhao, Yingbin Liang, Wei Zhang:
Finite-Time Analysis for Double Q-learning. CoRR abs/2009.14257 (2020) - [i57]Kaiyi Ji, Junjie Yang, Yingbin Liang:
Provably Faster Algorithms for Bilevel Optimization and Applications to Meta-Learning. CoRR abs/2010.07962 (2020) - [i56]Tengyu Xu, Yingbin Liang:
Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms. CoRR abs/2011.05053 (2020) - [i55]Tengyu Xu, Yingbin Liang, Guanghui Lan:
A Primal Approach to Constrained Policy Optimization: Global Optimality and Finite-Time Analysis. CoRR abs/2011.05869 (2020)
2010 – 2019
- 2019
- [j55]Yi Zhou, Yingbin Liang, Lixin Shen:
A simple convergence analysis of Bregman proximal gradient algorithm. Comput. Optim. Appl. 73(3): 903-912 (2019) - [j54]Michael Dikshtein, Ruchen Duan, Yingbin Liang, Shlomo Shamai (Shitz):
MIMO Gaussian State-Dependent Channels with a State-Cognitive Helper. Entropy 21(3): 273 (2019) - [j53]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) - [j52]Yunhao Sun, Ruchen Duan, Yingbin Liang, Shlomo Shamai Shitz:
State-Dependent Interference Channel With Correlated States. IEEE Trans. Inf. Theory 65(7): 4518-4531 (2019) - [j51]Chong Li, Yingbin Liang, H. Vincent Poor, Shlomo Shamai Shitz:
Secrecy Capacity of Colored Gaussian Noise Channels With Feedback. IEEE Trans. Inf. Theory 65(9): 5771-5782 (2019) - [j50]Tiexing Wang, Qunwei Li, Donald J. Bucci, Yingbin Liang, Biao Chen, Pramod K. Varshney:
K-Medoids Clustering of Data Sequences With Composite Distributions. IEEE Trans. Signal Process. 67(8): 2093-2106 (2019) - [c80]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization. AISTATS 2019: 2731-2740 - [c79]Jayanth Regatti, Gaurav Tendolkar, Yi Zhou, Abhishek Gupta, Yingbin Liang:
Distributed SGD Generalizes Well Under Asynchrony. Allerton 2019: 863-870 - [c78]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 - [c77]Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang:
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization. ICML 2019: 3100-3109 - [c76]Bin Dai, Chong Li, Yingbin Liang, Zheng Ma, Shlomo Shamai Shitz:
The Dirty Paper Wiretap Feedback Channel with or without Action on the State. ISIT 2019: 657-661 - [c75]Haoyu Fu, Yuejie Chi, Yingbin Liang:
Local Geometry of Cross Entropy Loss in Learning One-Hidden-Layer Neural Networks. ISIT 2019: 1972-1976 - [c74]Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh:
SpiderBoost and Momentum: Faster Variance Reduction Algorithms. NeurIPS 2019: 2403-2413 - [c73]Shaofeng Zou, Tengyu Xu, Yingbin Liang:
Finite-Sample Analysis for SARSA with Linear Function Approximation. NeurIPS 2019: 8665-8675 - [c72]Tengyu Xu, Shaofeng Zou, Yingbin Liang:
Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples. NeurIPS 2019: 10633-10643 - [c71]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
Cubic Regularization with Momentum for Nonconvex Optimization. UAI 2019: 313-322 - [i54]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) - [i53]Shaofeng Zou, Tengyu Xu, Yingbin Liang:
Finite-Sample Analysis for SARSA and Q-Learning with Linear Function Approximation. CoRR abs/1902.02234 (2019) - [i52]Tengyu Xu, Shaofeng Zou, Yingbin Liang:
Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples. CoRR abs/1909.11907 (2019) - [i51]Jayanth Regatti, Gaurav Tendolkar, Yi Zhou, Abhishek Gupta, Yingbin Liang:
Distributed SGD Generalizes Well Under Asynchrony. CoRR abs/1909.13391 (2019) - [i50]Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang:
Faster Stochastic Algorithms via History-Gradient Aided Batch Size Adaptation. CoRR abs/1910.09670 (2019) - [i49]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
- [j49]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) - [j48]Qunwei Li, Tiexing Wang, Donald J. Bucci, Yingbin Liang, Biao Chen, Pramod K. Varshney:
Nonparametric Composite Hypothesis Testing in an Asymptotic Regime. IEEE J. Sel. Top. Signal Process. 12(5): 1005-1014 (2018) - [j47]Shaofeng Zou, Yingbin Liang, Lifeng Lai, H. Vincent Poor, Shlomo Shamai Shitz:
Degraded Broadcast Channel With Secrecy Outside a Bounded Range. IEEE Trans. Inf. Theory 64(3): 2104-2120 (2018) - [j46]Yuheng Bu, Shaofeng Zou, Yingbin Liang, Venugopal V. Veeravalli:
Estimation of KL Divergence: Optimal Minimax Rate. IEEE Trans. Inf. Theory 64(4): 2648-2674 (2018) - [j45]Huishuai Zhang, Yuejie Chi, Yingbin Liang:
Median-Truncated Nonconvex Approach for Phase Retrieval With Outliers. IEEE Trans. Inf. Theory 64(11): 7287-7310 (2018) - [j44]Wei Yang, Yingbin Liang, Shlomo Shamai Shitz, H. Vincent Poor:
State-Dependent Gaussian Multiple Access Channels: New Outer Bounds and Capacity Results. IEEE Trans. Inf. Theory 64(12): 7866-7882 (2018) - [j43]Yixian Liu, Yingbin Liang, Shuguang Cui:
Data-Driven Nonparametric Existence and Association Problems. IEEE Trans. Signal Process. 66(24): 6377-6389 (2018) - [c70]Tiexing Wang, Donald J. Bucci, Yingbin Liang, Biao Chen, Pramod K. Varshney:
Clustering under composite generative models. CISS 2018: 1-6 - [c69]Tiexing Wang, Donald J. Bucci, Yingbin Liang, Biao Chen, Pramod K. Varshney:
Exponentially Consistent K-Means Clustering Algorithm Based on Kolmogrov-Smirnov Test. ICASSP 2018: 2296-2300 - [c68]Yixian Liu, Yingbin Liang, Shuguang Cui:
Data-Driven Nonparametric Hypothesis Testing. ICASSP 2018: 4234-4238 - [c67]Yi Zhou, Yingbin Liang:
Critical Points of Linear Neural Networks: Analytical Forms and Landscape Properties. ICLR (Poster) 2018 - [c66]Chong Li, Yingbin Liang, H. Vincent Poor, Shlomo Shamai:
A Coding Scheme for Colored Gaussian Wiretap Channels with Feedback. ISIT 2018: 131-135 - [c65]Yi Zhou, Zhe Wang, Yingbin Liang:
Convergence of Cubic Regularization for Nonconvex Optimization under KL Property. NeurIPS 2018: 3764-3773 - [c64]Kaiyi Ji, Yingbin Liang:
Minimax Estimation of Neural Net Distance. NeurIPS 2018: 3849-3858 - [i48]Chong Li, Yingbin Liang, H. Vincent Poor, Shlomo Shamai:
Secrecy Capacity of Colored Gaussian Noise Channels with Feedback. CoRR abs/1801.06702 (2018) - [i47]Haoyu Fu, Yuejie Chi, Yingbin Liang:
Local Geometry of One-Hidden-Layer Neural Networks for Logistic Regression. CoRR abs/1802.06463 (2018) - [i46]Yi Zhou, Yingbin Liang, Huishuai Zhang:
Generalization Error Bounds with Probabilistic Guarantee for SGD in Nonconvex Optimization. CoRR abs/1802.06903 (2018) - [i45]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
Sample Complexity of Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization. CoRR abs/1802.07372 (2018) - [i44]Yunhao Sun, Ruchen Duan, Yingbin Liang, Shlomo Shamai:
State-Dependent Interference Channel with Correlated States. CoRR abs/1806.00937 (2018) - [i43]Tengyu Xu, Yi Zhou, Kaiyi Ji, Yingbin Liang:
Convergence of SGD in Learning ReLU Models with Separable Data. CoRR abs/1806.04339 (2018) - [i42]Michael Dikshtein, Ruchen Duan, Yingbin Liang, Shlomo Shamai:
Parallel Gaussian Channels Corrupted by Independent States With a State-Cognitive Helper. CoRR abs/1807.03518 (2018) - [i41]Tiexing Wang, Qunwei Li, Donald J. Bucci, Yingbin Liang, Biao Chen, Pramod K. Varshney:
K-medoids Clustering of Data Sequences with Composite Distributions. CoRR abs/1807.11620 (2018) - [i40]Yi Zhou, Zhe Wang, Yingbin Liang:
Convergence of Cubic Regularization for Nonconvex Optimization under KL Property. CoRR abs/1808.07382 (2018) - [i39]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
A Note on Inexact Condition for Cubic Regularized Newton's Method. CoRR abs/1808.07384 (2018) - [i38]