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2020 – today
- 2021
[j18]Yaohui Zeng, Tianbao Yang, Patrick Breheny:
Hybrid safe-strong rules for efficient optimization in lasso-type problems. Comput. Stat. Data Anal. 153: 107063 (2021)- 2020
- [j17]Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang:
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints. J. Mach. Learn. Res. 21: 143:1-143:45 (2020) - [j16]Soumitra Pal
, Tingyang Xu, Tianbao Yang, Sanguthevar Rajasekaran, Jinbo Bi:
Hybrid-DCA: A double asynchronous approach for stochastic dual coordinate ascent. J. Parallel Distributed Comput. 143: 47-66 (2020) - [j15]Tianbao Yang, Lijun Zhang, Qihang Lin, Shenghuo Zhu, Rong Jin:
High-dimensional model recovery from random sketched data by exploring intrinsic sparsity. Mach. Learn. 109(5): 899-938 (2020) - [c91]Dixian Zhu, Dongjin Song, Yuncong Chen, Cristian Lumezanu, Wei Cheng, Bo Zong, Jingchao Ni, Takehiko Mizoguchi, Tianbao Yang, Haifeng Chen:
Deep Unsupervised Binary Coding Networks for Multivariate Time Series Retrieval. AAAI 2020: 1403-1411 - [c90]Pingbo Pan, Ping Liu, Yan Yan, Tianbao Yang, Yi Yang:
Adversarial Localized Energy Network for Structured Prediction. AAAI 2020: 5347-5354 - [c89]Lijun Zhang, Shiyin Lu, Tianbao Yang:
Minimizing Dynamic Regret and Adaptive Regret Simultaneously. AISTATS 2020: 309-319 - [c88]Qi Qi, Yan Yan, Zixuan Wu, Xiaoyu Wang, Tianbao Yang:
A Simple and Effective Framework for Pairwise Deep Metric Learning. ECCV (27) 2020: 375-391 - [c87]Zhuoning Yuan, Zhishuai Guo, Xiaotian Yu, Xiaoyu Wang, Tianbao Yang:
Accelerating Deep Learning with Millions of Classes. ECCV (23) 2020: 711-726 - [c86]Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, Tianbao Yang:
Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets. ICLR 2020 - [c85]Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang:
Stochastic AUC Maximization with Deep Neural Networks. ICLR 2020 - [c84]Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang:
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. ICML 2020: 3864-3874 - [c83]Runchao Ma, Qihang Lin, Tianbao Yang:
Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints. ICML 2020: 6554-6564 - [c82]Yan Yan, Yi Xu, Lijun Zhang, Xiaoyu Wang, Tianbao Yang:
Stochastic Optimization for Non-convex Inf-Projection Problems. ICML 2020: 10660-10669 - [c81]Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang:
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. NeurIPS 2020 - [c80]Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing:
Improved Schemes for Episodic Memory-based Lifelong Learning. NeurIPS 2020 - [c79]Mingrui Liu, Wei Zhang, Youssef Mroueh, Xiaodong Cui, Jarret Ross, Tianbao Yang, Payel Das:
A Decentralized Parallel Algorithm for Training Generative Adversarial Nets. NeurIPS 2020 - [i67]Lijun Zhang, Shiyin Lu, Tianbao Yang:
Minimizing Dynamic Regret and Adaptive Regret Simultaneously. CoRR abs/2002.02085 (2020) - [i66]Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang:
Sharp Analysis of Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. CoRR abs/2002.05309 (2020) - [i65]Zhishuai Guo, Zixuan Wu, Yan Yan, Xiaoyu Wang, Tianbao Yang:
Revisiting SGD with Increasingly Weighted Averaging: Optimization and Generalization Perspectives. CoRR abs/2003.04339 (2020) - [i64]Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang:
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. CoRR abs/2005.02426 (2020) - [i63]Zhishuai Guo, Zhuoning Yuan, Yan Yan, Tianbao Yang:
Fast Objective and Duality Gap Convergence for Non-convex Strongly-concave Min-max Problems. CoRR abs/2006.06889 (2020) - [i62]Yan Yan, Xin Man, Tianbao Yang:
Nearly Optimal Robust Method for Convex Compositional Problems with Heavy-Tailed Noise. CoRR abs/2006.10095 (2020) - [i61]Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang:
A Practical Online Method for Distributionally Deep Robust Optimization. CoRR abs/2006.10138 (2020) - [i60]Daoming Lyu, Qi Qi, Mohammad Ghavamzadeh, Hengshuai Yao, Tianbao Yang, Bo Liu:
Variance-Reduced Off-Policy Memory-Efficient Policy Search. CoRR abs/2009.06548 (2020) - [i59]Mingrui Liu, Wei Zhang, Francesco Orabona, Tianbao Yang:
Adam+: A Stochastic Method with Adaptive Variance Reduction. CoRR abs/2011.11985 (2020) - [i58]Zhuoning Yuan, Yan Yan, Milan Sonka, Tianbao Yang:
Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification. CoRR abs/2012.03173 (2020) - [i57]Qi Qi, Yi Xu, Rong Jin, Wotao Yin, Tianbao Yang:
Attentional Biased Stochastic Gradient for Imbalanced Classification. CoRR abs/2012.06951 (2020)
2010 – 2019
- 2019
- [j14]Tianbao Yang:
Advancing non-convex and constrained learning: challenges and opportunities. AI Matters 5(3): 29-39 (2019) - [j13]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion. J. Mach. Learn. Res. 20: 97:1-97:22 (2019) - [j12]Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu, Zhi-Hua Zhou:
A simple homotopy proximal mapping algorithm for compressive sensing. Mach. Learn. 108(6): 1019-1056 (2019) - [c78]Dixian Zhu, Zhe Li, Xiaoyu Wang, Boqing Gong, Tianbao Yang:
A Robust Zero-Sum Game Framework for Pool-based Active Learning. AISTATS 2019: 517-526 - [c77]Jian Ren, Zhe Li, Jianchao Yang, Ning Xu, Tianbao Yang, David J. Foran:
EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching From Scratch. CVPR 2019: 9059-9068 - [c76]Zaiyi Chen, Zhuoning Yuan, Jinfeng Yi, Bowen Zhou, Enhong Chen, Tianbao Yang:
Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions. ICLR (Poster) 2019 - [c75]Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang:
Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number. ICML 2019: 1102-1111 - [c74]Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang:
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence. ICML 2019: 6942-6951 - [c73]Yi Xu, Zhuoning Yuan, Sen Yang, Rong Jin, Tianbao Yang:
On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Minimization. IJCAI 2019: 4003-4009 - [c72]Zhuoning Yuan, Yan Yan, Rong Jin, Tianbao Yang:
Stagewise Training Accelerates Convergence of Testing Error Over SGD. NeurIPS 2019: 2604-2614 - [c71]Yi Xu, Rong Jin, Tianbao Yang:
Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems. NeurIPS 2019: 2626-2636 - [c70]Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang:
Learning with Non-Convex Truncated Losses by SGD. UAI 2019: 701-711 - [i56]Yan Yan, Yi Xu, Qihang Lin, Lijun Zhang, Tianbao Yang:
Stochastic Primal-Dual Algorithms with Faster Convergence than O(1/√T) for Problems without Bilinear Structure. CoRR abs/1904.10112 (2019) - [i55]Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang:
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints. CoRR abs/1908.03077 (2019) - [i54]Yan Yan, Yi Xu, Lijun Zhang, Xiaoyu Wang, Tianbao Yang:
Stochastic Optimization for Non-convex Inf-Projection Problems. CoRR abs/1908.09941 (2019) - [i53]Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang:
Stochastic AUC Maximization with Deep Neural Networks. CoRR abs/1908.10831 (2019) - [i52]Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing:
Learning with Long-term Remembering: Following the Lead of Mixed Stochastic Gradient. CoRR abs/1909.11763 (2019) - [i51]Mingrui Liu, Youssef Mroueh, Wei Zhang, Xiaodong Cui, Jerret Ross, Tianbao Yang, Payel Das:
Decentralized Parallel Algorithm for Training Generative Adversarial Nets. CoRR abs/1910.12999 (2019) - [i50]Qi Qi, Yan Yan, Zixuan Wu, Xiaoyu Wang, Tianbao Yang:
a simple and effective framework for pairwise deep metric learning. CoRR abs/1912.11194 (2019) - [i49]Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, Tianbao Yang:
Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets. CoRR abs/1912.11940 (2019) - 2018
- [j11]Dixian Zhu, Changjie Cai, Tianbao Yang, Xun Zhou:
A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization. Big Data Cogn. Comput. 2(1): 5 (2018) - [j10]Tianbao Yang, Qihang Lin:
RSG: Beating Subgradient Method without Smoothness and Strong Convexity. J. Mach. Learn. Res. 19: 6:1-6:33 (2018) - [c69]Tianbao Yang, Zhe Li, Lijun Zhang:
A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer. AISTATS 2018: 445-453 - [c68]Yandong Li, Liqiang Wang, Tianbao Yang, Boqing Gong:
How Local Is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization. ECCV (8) 2018: 156-174 - [c67]Aidean Sharghi, Ali Borji, Chengtao Li, Tianbao Yang, Boqing Gong:
Improving Sequential Determinantal Point Processes for Supervised Video Summarization. ECCV (3) 2018: 533-550 - [c66]Zaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang:
SADAGRAD: Strongly Adaptive Stochastic Gradient Methods. ICML 2018: 912-920 - [c65]Qihang Lin, Runchao Ma, Tianbao Yang:
Level-Set Methods for Finite-Sum Constrained Convex Optimization. ICML 2018: 3118-3127 - [c64]Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, Tianbao Yang:
Fast Stochastic AUC Maximization with O(1/n)-Convergence Rate. ICML 2018: 3195-3203 - [c63]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Dynamic Regret of Strongly Adaptive Methods. ICML 2018: 5877-5886 - [c62]Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang:
A Unified Analysis of Stochastic Momentum Methods for Deep Learning. IJCAI 2018: 2955-2961 - [c61]Xiaotian Yu, Irwin King, Michael R. Lyu, Tianbao Yang:
A Generic Approach for Accelerating Stochastic Zeroth-Order Convex Optimization. IJCAI 2018: 3040-3046 - [c60]Zhuoning Yuan, Xun Zhou, Tianbao Yang:
Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data. KDD 2018: 984-992 - [c59]Xiaoxuan Zhang, Mingrui Liu, Xun Zhou, Tianbao Yang:
Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization. NeurIPS 2018: 3893-3903 - [c58]Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Jing Rong, Tianbao Yang:
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions. NeurIPS 2018: 4683-4694 - [c57]Mingrui Liu, Zhe Li, Xiaoyu Wang, Jinfeng Yi, Tianbao Yang:
Adaptive Negative Curvature Descent with Applications in Non-convex Optimization. NeurIPS 2018: 4858-4867 - [c56]Yi Xu, Jing Rong, Tianbao Yang:
First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time. NeurIPS 2018: 5535-5545 - [r2]Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu:
Combining Link and Content for Community Detection. Encyclopedia of Social Network Analysis and Mining. 2nd Ed. 2018 - [i48]Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang:
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions. CoRR abs/1805.04577 (2018) - [i47]Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang:
Learning with Non-Convex Truncated Losses by SGD. CoRR abs/1805.07880 (2018) - [i46]Zhe Li, Xuehan Xiong, Zhou Ren, Ning Zhang, Xiaoyu Wang, Tianbao Yang:
An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints. CoRR abs/1806.00851 (2018) - [i45]Jian Ren, Zhe Li, Jianchao Yang, Ning Xu, Tianbao Yang, David J. Foran:
EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching. CoRR abs/1806.01940 (2018) - [i44]Yandong Li, Liqiang Wang, Tianbao Yang, Boqing Gong:
How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization. CoRR abs/1807.04219 (2018) - [i43]Aidean Sharghi, Ali Borji, Chengtao Li, Tianbao Yang, Boqing Gong:
Improving Sequential Determinantal Point Processes for Supervised Video Summarization. CoRR abs/1807.10957 (2018) - [i42]Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang:
A Unified Analysis of Stochastic Momentum Methods for Deep Learning. CoRR abs/1808.10396 (2018) - [i41]Pingbo Pan, Yan Yan, Tianbao Yang, Yi Yang:
Learning Discriminators as Energy Networks in Adversarial Learning. CoRR abs/1810.01152 (2018) - [i40]Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang:
Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning. CoRR abs/1810.02060 (2018) - [i39]Tianbao Yang, Yan Yan, Zhuoning Yuan, Rong Jin:
Why Does Stagewise Training Accelerate Convergence of Testing Error Over SGD? CoRR abs/1812.03934 (2018) - 2017
- [j9]Jason D. Lee, Qihang Lin, Tengyu Ma, Tianbao Yang:
Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement. J. Mach. Learn. Res. 18: 122:1-122:43 (2017) - [c55]Zhe Li, Tianbao Yang, Lijun Zhang, Rong Jin:
A Two-Stage Approach for Learning a Sparse Model with Sharp Excess Risk Analysis. AAAI 2017: 2224-2230 - [c54]Yi Xu, Haiqin Yang, Lijun Zhang, Tianbao Yang:
Efficient Non-Oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee. AAAI 2017: 2796-2802 - [c53]Yan Yan, Tianbao Yang, Yi Yang, Jianhui Chen:
A Framework of Online Learning with Imbalanced Streaming Data. AAAI 2017: 2817-2823 - [c52]Lijun Zhang, Tianbao Yang, Rong Jin:
Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$- and $O(1/n^2)$-type of Risk Bounds. COLT 2017: 1954-1979 - [c51]Yi Xu, Qihang Lin, Tianbao Yang:
Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence. ICML 2017: 3821-3830 - [c50]Tianbao Yang, Qihang Lin, Lijun Zhang:
A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates. ICML 2017: 3901-3910 - [c49]Yichi Xiao, Zhe Li, Tianbao Yang, Lijun Zhang:
SVD-free Convex-Concave Approaches for Nuclear Norm Regularization. IJCAI 2017: 3126-3132 - [c48]Lijun Zhang, Tianbao Yang, Jinfeng Yi, Jing Rong, Zhi-Hua Zhou:
Improved Dynamic Regret for Non-degenerate Functions. NIPS 2017: 732-741 - [c47]Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang:
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization. NIPS 2017: 1267-1277 - [c46]Mingrui Liu, Tianbao Yang:
Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition. NIPS 2017: 3104-3114 - [c45]Yi Xu, Qihang Lin, Tianbao Yang:
Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter. NIPS 2017: 3277-3287 - [p1]Chuang Gan, Tianbao Yang, Boqing Gong:
A Multisource Domain Generalization Approach to Visual Attribute Detection. Domain Adaptation in Computer Vision Applications 2017: 277-289 - [i38]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Strongly Adaptive Regret Implies Optimally Dynamic Regret. CoRR abs/1701.07570 (2017) - [i37]Lijun Zhang, Tianbao Yang, Rong Jin:
Empirical Risk Minimization for Stochastic Convex Optimization: O(1/n)- and O(1/n2)-type of Risk Bounds. CoRR abs/1702.02030 (2017) - [i36]Zhe Li, Xiaoyu Wang, Xutao Lv, Tianbao Yang:
SEP-Nets: Small and Effective Pattern Networks. CoRR abs/1706.03912 (2017) - [i35]Tianbao Yang, Zhe Li, Lijun Zhang:
A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer. CoRR abs/1709.02909 (2017) - [i34]Mingrui Liu, Tianbao Yang:
Stochastic Non-convex Optimization with Strong High Probability Second-order Convergence. CoRR abs/1710.09447 (2017) - 2016
- [j8]Tianbao Yang, Rong Jin, Shenghuo Zhu, Qihang Lin:
On Data Preconditioning for Regularized Loss Minimization. Mach. Learn. 103(1): 57-79 (2016) - [c44]Zhe Li, Tianbao Yang, Lijun Zhang, Rong Jin:
Fast and Accurate Refined Nyström-Based Kernel SVM. AAAI 2016: 1830-1836 - [c43]Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou:
Stochastic Optimization for Kernel PCA. AAAI 2016: 2315-2322 - [c42]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Sparse Learning for Large-Scale and High-Dimensional Data: A Randomized Convex-Concave Optimization Approach. ALT 2016: 83-97 - [c41]Chuang Gan, Tianbao Yang, Boqing Gong:
Learning Attributes Equals Multi-Source Domain Generalization. CVPR 2016: 87-97 - [c40]Lijun Zhang, Tianbao Yang, Rong Jin, Yichi Xiao, Zhi-Hua Zhou:
Online Stochastic Linear Optimization under One-bit Feedback. ICML 2016: 392-401 - [c39]Tianbao Yang, Lijun Zhang, Rong Jin, Jinfeng Yi:
Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient. ICML 2016: 449-457 - [c38]Xiaoxuan Zhang, Tianbao Yang, Padmini Srinivasan:
Online Asymmetric Active Learning with Imbalanced Data. KDD 2016: 2055-2064 - [c37]Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang:
Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/\epsilon). NIPS 2016: 1208-1216 - [c36]Zhe Li, Boqing Gong, Tianbao Yang:
Improved Dropout for Shallow and Deep Learning. NIPS 2016: 2523-2531 - [c35]Jianhui Chen, Tianbao Yang, Qihang Lin, Lijun Zhang, Yi Chang:
Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections. UAI 2016 - [i33]Zhe Li, Boqing Gong, Tianbao Yang:
Improved Dropout for Shallow and Deep Learning. CoRR abs/1602.02220 (2016) - [i32]Chuang Gan, Tianbao Yang, Boqing Gong:
Learning Attributes Equals Multi-Source Domain Generalization. CoRR abs/1605.00743 (2016) - [i31]Tianbao Yang, Lijun Zhang, Rong Jin, Jinfeng Yi:
Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient. CoRR abs/1605.04638 (2016) - [i30]Yi Xu, Qihang Lin, Tianbao Yang:
Accelerate Stochastic Subgradient Method by Leveraging Local Error Bound. CoRR abs/1607.01027 (2016) - [i29]Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou:
Improved dynamic regret for non-degeneracy functions. CoRR abs/1608.03933 (2016) - [i28]Soumitra Pal, Tingyang Xu, Tianbao Yang, Sanguthevar Rajasekaran, Jinbo Bi:
Hybrid-DCA: A Double Asynchronous Approach for Stochastic Dual Coordinate Ascent. CoRR abs/1610.07184 (2016) - [i27]Yi Xu, Haiqin Yang, Lijun Zhang, Tianbao Yang:
Efficient Non-oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee. CoRR abs/1612.01663 (2016) - 2015
- [j7]Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Shenghuo Zhu:
An efficient primal dual prox method for non-smooth optimization. Mach. Learn. 98(3): 369-406 (2015) - [c34]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Online Bandit Learning for a Special Class of Non-Convex Losses. AAAI 2015: 3158-3164 - [c33]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
A Simple Homotopy Algorithm for Compressive Sensing. AISTATS 2015 - [c32]Saining Xie, Tianbao Yang, Xiaoyu Wang, Yuanqing Lin:
Hyper-class augmented and regularized deep learning for fine-grained image classification. CVPR 2015: 2645-2654 - [c31]Syed Shabih Hasan, Ryan Brummet, Octav Chipara, Yu-Hsiang Wu, Tianbao Yang:
In-Situ Measurement and Prediction of Hearing Aid Outcomes Using Mobile Phones. ICHI 2015: 525-534 - [c30]Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu:
An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection. ICML 2015: 135-143 - [c29]Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu:
Theory of Dual-sparse Regularized Randomized Reduction. ICML 2015: 305-314 - [c28]Jinfeng Yi, Lijun Zhang, Tianbao Yang, Wei Liu, Jun Wang:
An Efficient Semi-Supervised Clustering Algorithm with Sequential Constraints. KDD 2015: 1405-1414 - [c27]Tianbao Yang, Qihang Lin, Rong Jin:
Big Data Analytics: Optimization and Randomization. KDD 2015: 2327 - [i26]Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu:
Theory of Dual-sparse Regularized Randomized Reduction. CoRR abs/1504.03991 (2015) - [i25]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Analysis of Nuclear Norm Regularization for Full-rank Matrix Completion. CoRR abs/1504.06817 (2015) - [i24]Tianbao Yang, Lijun Zhang, Qihang Lin, Rong Jin:
Fast Sparse Least-Squares Regression with Non-Asymptotic Guarantees. CoRR abs/1507.05185 (2015) - [i23]Adams Wei Yu, Qihang Lin, Tianbao Yang:
Doubly Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization with Factorized Data. CoRR abs/1508.03390 (2015) - [i22]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Online Stochastic Linear Optimization under One-bit Feedback. CoRR abs/1509.07728 (2015) - [i21]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Stochastic Proximal Gradient Descent for Nuclear Norm Regularization. CoRR abs/1511.01664 (2015) - [i20]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Sparse Learning for Large-scale and High-dimensional Data: A Randomized Convex-concave Optimization Approach. CoRR abs/1511.03766 (2015) - 2014
- [j6]Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Shenghuo Zhu:
Regret bounded by gradual variation for online convex optimization. Mach. Learn. 95(2): 183-223 (2014) - [j5]Lijun Zhang, Mehrdad Mahdavi, Rong Jin, Tianbao Yang, Shenghuo Zhu:
Random Projections for Classification: A Recovery Approach. IEEE Trans. Inf. Theory 60(11): 7300-7316 (2014) - [c26]Jianhui Chen, Tianbao Yang, Shenghuo Zhu:<