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2020 – today
- 2022
[c109]Guanghui Wang, Ming Yang, Lijun Zhang, Tianbao Yang:
Momentum Accelerates the Convergence of Stochastic AUPRC Maximization. AISTATS 2022: 3753-3771- [c108]Zhuoning Yuan, Zhishuai Guo, Nitesh V. Chawla, Tianbao Yang:
Compositional Training for End-to-End Deep AUC Maximization. ICLR 2022 - [c107]Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang:
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization. ICML 2022: 10195-10216 - [c106]Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong, Lijun Zhang, Tianbao Yang:
Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence. ICML 2022: 18122-18152 - [c105]Bokun Wang, Tianbao Yang:
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications. ICML 2022: 23292-23317 - [c104]Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji:
GraphFM: Improving Large-Scale GNN Training via Feature Momentum. ICML 2022: 25684-25701 - [c103]Zhuoning Yuan, Yuexin Wu, Zi-Hao Qiu, Xianzhi Du, Lijun Zhang, Denny Zhou, Tianbao Yang:
Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance. ICML 2022: 25760-25782 - [c102]Lijun Zhang, Guanghui Wang, Jinfeng Yi, Tianbao Yang:
A Simple yet Universal Strategy for Online Convex Optimization. ICML 2022: 26605-26623 - [c101]Dixian Zhu, Gang Li, Bokun Wang, Xiaodong Wu, Tianbao Yang:
When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee. ICML 2022: 27548-27573 - [i94]Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang:
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization. CoRR abs/2202.07530 (2022) - [i93]Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong, Lijun Zhang, Tianbao Yang:
Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence. CoRR abs/2202.12183 (2022) - [i92]Zhuoning Yuan, Yuexin Wu, Zihao Qiu, Xianzhi Du, Lijun Zhang, Denny Zhou, Tianbao Yang:
Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance. CoRR abs/2202.12387 (2022) - [i91]Bokun Wang, Tianbao Yang:
Finite-Sum Compositional Stochastic Optimization: Theory and Applications. CoRR abs/2202.12396 (2022) - [i90]Dixian Zhu, Gang Li, Bokun Wang, Xiaodong Wu, Tianbao Yang:
When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee. CoRR abs/2203.00176 (2022) - [i89]Yao Yao, Qihang Lin, Tianbao Yang:
Large-scale Optimization of Partial AUC in a Range of False Positive Rates. CoRR abs/2203.01505 (2022) - [i88]Dixian Zhu, Xiaodong Wu, Tianbao Yang:
Benchmarking Deep AUROC Optimization: Loss Functions and Algorithmic Choices. CoRR abs/2203.14177 (2022) - [i87]Tianbao Yang, Yiming Ying:
AUC Maximization in the Era of Big Data and AI: A Survey. CoRR abs/2203.15046 (2022) - [i86]Lijun Zhang, Wei Jiang, Jinfeng Yi, Tianbao Yang:
Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor. CoRR abs/2205.00741 (2022) - [i85]Quanqi Hu, Yongjian Zhong, Tianbao Yang:
Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization. CoRR abs/2206.00260 (2022) - [i84]Tianbao Yang:
Algorithmic Foundation of Deep X-Risk Optimization. CoRR abs/2206.00439 (2022) - [i83]Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji:
GraphFM: Improving Large-Scale GNN Training via Feature Momentum. CoRR abs/2206.07161 (2022) - [i82]Wei Jiang, Gang Li, Yibo Wang, Lijun Zhang, Tianbao Yang:
Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization. CoRR abs/2207.08540 (2022) - 2021
- [j19]Yaohui Zeng, Tianbao Yang, Patrick Breheny
:
Hybrid safe-strong rules for efficient optimization in lasso-type problems. Comput. Stat. Data Anal. 153: 107063 (2021) - [j18]Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang:
First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems. J. Mach. Learn. Res. 22: 169:1-169:34 (2021) - [c100]Zhuoning Yuan, Yan Yan, Milan Sonka, Tianbao Yang:
Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification. ICCV 2021: 3020-3029 - [c99]Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying:
Stability and Generalization of Stochastic Gradient Methods for Minimax Problems. ICML 2021: 6175-6186 - [c98]Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang:
Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity. ICML 2021: 12219-12229 - [c97]Qi Qi, Youzhi Luo, Zhao Xu, Shuiwang Ji, Tianbao Yang:
Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence. NeurIPS 2021: 1752-1765 - [c96]Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang:
An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives. NeurIPS 2021: 10067-10080 - [c95]Lijun Zhang, Wei Jiang, Shiyin Lu, Tianbao Yang:
Revisiting Smoothed Online Learning. NeurIPS 2021: 13599-13612 - [c94]Zhenhuan Yang, Yunwen Lei, Puyu Wang, Tianbao Yang, Yiming Ying:
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning. NeurIPS 2021: 20160-20171 - [c93]Guanghui Wang, Yuanyu Wan, Tianbao Yang, Lijun Zhang:
Online Convex Optimization with Continuous Switching Constraint. NeurIPS 2021: 28636-28647 - [i81]Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang:
Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity. CoRR abs/2102.04635 (2021) - [i80]Lijun Zhang, Wei Jiang, Shiyin Lu, Tianbao Yang:
Revisiting Smoothed Online Learning. CoRR abs/2102.06933 (2021) - [i79]Guanghui Wang, Yuanyu Wan, Tianbao Yang, Lijun Zhang:
Online Convex Optimization with Continuous Switching Constraint. CoRR abs/2103.11370 (2021) - [i78]Qi Qi, Youzhi Luo, Zhao Xu, Shuiwang Ji, Tianbao Yang:
Stochastic Optimization of Area Under Precision-Recall Curve for Deep Learning with Provable Convergence. CoRR abs/2104.08736 (2021) - [i77]Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang:
On Stochastic Moving-Average Estimators for Non-Convex Optimization. CoRR abs/2104.14840 (2021) - [i76]Zhishuai Guo, Tianbao Yang:
Randomized Stochastic Variance-Reduced Methods for Stochastic Bilevel Optimization. CoRR abs/2105.02266 (2021) - [i75]Lijun Zhang, Guanghui Wang, Jinfeng Yi, Tianbao Yang:
A Simple yet Universal Strategy for Online Convex Optimization. CoRR abs/2105.03681 (2021) - [i74]Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying:
Stability and Generalization of Stochastic Gradient Methods for Minimax Problems. CoRR abs/2105.03793 (2021) - [i73]Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang:
Memory-based Optimization Methods for Model-Agnostic Meta-Learning. CoRR abs/2106.04911 (2021) - [i72]Guanghui Wang, Ming Yang, Lijun Zhang, Tianbao Yang:
Momentum Accelerates the Convergence of Stochastic AUPRC Maximization. CoRR abs/2107.01173 (2021) - [i71]Tianbao Yang:
Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities. CoRR abs/2111.02400 (2021) - [i70]Zhenhuan Yang, Yunwen Lei, Puyu Wang, Tianbao Yang, Yiming Ying:
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning. CoRR abs/2111.12050 (2021) - [i69]Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang:
A Novel Convergence Analysis for Algorithms of the Adam Family. CoRR abs/2112.03459 (2021) - [i68]Dixian Zhu, Tianbao Yang:
A Unified DRO View of Multi-class Loss Functions with top-N Consistency. CoRR abs/2112.14869 (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) - [c92]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 - [c91]Pingbo Pan, Ping Liu, Yan Yan, Tianbao Yang, Yi Yang:
Adversarial Localized Energy Network for Structured Prediction. AAAI 2020: 5347-5354 - [c90]Lijun Zhang, Shiyin Lu, Tianbao Yang:
Minimizing Dynamic Regret and Adaptive Regret Simultaneously. AISTATS 2020: 309-319 - [c89]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 - [c88]Zhuoning Yuan, Zhishuai Guo, Xiaotian Yu, Xiaoyu Wang, Tianbao Yang:
Accelerating Deep Learning with Millions of Classes. ECCV (23) 2020: 711-726 - [c87]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 - [c86]Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang:
Stochastic AUC Maximization with Deep Neural Networks. ICLR 2020 - [c85]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 - [c84]Runchao Ma, Qihang Lin, Tianbao Yang:
Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints. ICML 2020: 6554-6564 - [c83]Yan Yan, Yi Xu, Lijun Zhang, Xiaoyu Wang, Tianbao Yang:
Stochastic Optimization for Non-convex Inf-Projection Problems. ICML 2020: 10660-10669 - [c82]Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang:
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. NeurIPS 2020 - [c81]Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing:
Improved Schemes for Episodic Memory-based Lifelong Learning. NeurIPS 2020 - [c80]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) - [c79]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 - [c78]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 - [c77]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 - [c76]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 - [c75]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 - [c74]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 - [c73]Zhuoning Yuan, Yan Yan, Rong Jin, Tianbao Yang:
Stagewise Training Accelerates Convergence of Testing Error Over SGD. NeurIPS 2019: 2604-2614 - [c72]Yi Xu, Rong Jin, Tianbao Yang:
Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems. NeurIPS 2019: 2626-2636 - [c71]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) - [c70]Tianbao Yang, Zhe Li, Lijun Zhang:
A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer. AISTATS 2018: 445-453 - [c69]Zihao Cheng, Dong Yue, Songlin Hu, Tianbao Yang, Lei Chen:
An Indirect-Direct event-triggered mechanism for networked control system against DoS attacks. ANZCC 2018: 27-32 - [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, Rong Jin, 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, Rong Jin, 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.