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2020 – today
- 2024
[c132]Zi-Hao Qiu, Siqi Guo, Mao Xu, Tuo Zhao, Lijun Zhang, Tianbao Yang:
To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO. ICML 2024- [c131]Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying:
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms. ICML 2024 - [c130]Yongjian Zhong
, Hieu Vu, Tianbao Yang, Bijaya Adhikari:
Efficient and Effective Implicit Dynamic Graph Neural Network. KDD 2024: 4595-4606 - [c129]Haoran Liu, Bokun Wang, Jianling Wang, Xiangjue Dong, Tianbao Yang, James Caverlee:
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks. WWW (Companion Volume) 2024: 485-488 - [i120]Zi-Hao Qiu, Siqi Guo, Mao Xu, Tuo Zhao, Lijun Zhang, Tianbao Yang:
To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO. CoRR abs/2404.04575 (2024) - [i119]Quanqi Hu, Qi Qi, Zhaosong Lu, Tianbao Yang:
Single-loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions. CoRR abs/2405.18577 (2024) - [i118]Ilgee Hong, Zichong Li, Alexander Bukharin, Yixiao Li, Haoming Jiang, Tianbao Yang, Tuo Zhao:
Adaptive Preference Scaling for Reinforcement Learning with Human Feedback. CoRR abs/2406.02764 (2024) - [i117]Qi Qi, Quanqi Hu, Qihang Lin, Tianbao Yang:
Provable Optimization for Adversarial Fair Self-supervised Contrastive Learning. CoRR abs/2406.05686 (2024) - [i116]Yongjian Zhong, Hieu Vu, Tianbao Yang, Bijaya Adhikari:
Efficient and Effective Implicit Dynamic Graph Neural Network. CoRR abs/2406.17894 (2024) - [i115]Xiyuan Wei, Fanjiang Ye, Ori Yonay, Xingyu Chen, Baixi Sun, Dingwen Tao, Tianbao Yang:
FastCLIP: A Suite of Optimization Techniques to Accelerate CLIP Training with Limited Resources. CoRR abs/2407.01445 (2024) - [i114]Gang Li, Qihang Lin, Ayush Ghosh, Tianbao Yang:
Multi-Output Distributional Fairness via Post-Processing. CoRR abs/2409.00553 (2024) - 2023
- [j26]Tianbao Yang, Yiming Ying:
AUC Maximization in the Era of Big Data and AI: A Survey. ACM Comput. Surv. 55(8): 172:1-172:37 (2023) - [j25]Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang:
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning. J. Mach. Learn. Res. 24: 145:1-145:46 (2023) - [j24]Zhishuai Guo, Yan Yan, Zhuoning Yuan, Tianbao Yang:
Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition. J. Mach. Learn. Res. 24: 148:1-148:63 (2023) - [j23]Qi Qi, Jiameng Lyu, Kung-Sik Chan, Er-Wei Bai, Tianbao Yang:
Stochastic Constrained DRO with a Complexity Independent of Sample Size. Trans. Mach. Learn. Res. 2023 (2023) - [j22]Qi Qi, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang:
Attentional-Biased Stochastic Gradient Descent. Trans. Mach. Learn. Res. 2023 (2023) - [c128]Yao Yao, Qihang Lin, Tianbao Yang:
Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. AISTATS 2023: 10324-10342 - [c127]Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang:
FeDXL: Provable Federated Learning for Deep X-Risk Optimization. ICML 2023: 11934-11966 - [c126]Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo, Lijun Zhang, Tianbao Yang:
Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization. ICML 2023: 13550-13583 - [c125]Wei Jiang, Jiayu Qin, Lingyu Wu, Changyou Chen, Tianbao Yang, Lijun Zhang:
Learning Unnormalized Statistical Models via Compositional Optimization. ICML 2023: 15105-15124 - [c124]Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou:
Generalization Analysis for Contrastive Representation Learning. ICML 2023: 19200-19227 - [c123]Zi-Hao Qiu, Quanqi Hu, Zhuoning Yuan, Denny Zhou, Lijun Zhang, Tianbao Yang:
Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization. ICML 2023: 28389-28421 - [c122]Dixian Zhu, Bokun Wang, Zhi Chen, Yaxing Wang, Milan Sonka, Xiaodong Wu, Tianbao Yang:
Provable Multi-instance Deep AUC Maximization with Stochastic Pooling. ICML 2023: 43205-43227 - [c121]Dixian Zhu, Yiming Ying, Tianbao Yang:
Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity. ICML 2023: 43289-43325 - [c120]Zhuoning Yuan, Dixian Zhu, Zi-Hao Qiu, Gang Li, Xuanhui Wang, Tianbao Yang:
LibAUC: A Deep Learning Library for X-Risk Optimization. KDD 2023: 5487-5499 - [c119]Ryan King, Tianbao Yang, Bobak J. Mortazavi:
Multimodal Pretraining of Medical Time Series and Notes. ML4H@NeurIPS 2023: 244-255 - [c118]Lijun Zhang, Peng Zhao, Zhen-Hua Zhuang, Tianbao Yang, Zhi-Hua Zhou:
Stochastic Approximation Approaches to Group Distributionally Robust Optimization. NeurIPS 2023 - [c117]Bang An, Xun Zhou, Yongjian Zhong, Tianbao Yang:
SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data. NeurIPS 2023 - [c116]Quanqi Hu, Dixian Zhu, Tianbao Yang:
Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization. NeurIPS 2023 - [c115]Gang Li, Wei Tong, Tianbao Yang:
Maximization of Average Precision for Deep Learning with Adversarial Ranking Robustness. NeurIPS 2023 - [c114]Xinwen Zhang, Yihan Zhang, Tianbao Yang, Richard Souvenir, Hongchang Gao:
Federated Compositional Deep AUC Maximization. NeurIPS 2023 - [i113]Lijun Zhang, Peng Zhao, Tianbao Yang, Zhi-Hua Zhou:
Stochastic Approximation Approaches to Group Distributionally Robust Optimization. CoRR abs/2302.09267 (2023) - [i112]Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou:
Generalization Analysis for Contrastive Representation Learning. CoRR abs/2302.12383 (2023) - [i111]Xinwen Zhang, Yihan Zhang, Tianbao Yang, Richard Souvenir, Hongchang Gao:
Federated Compositional Deep AUC Maximization. CoRR abs/2304.10101 (2023) - [i110]Dixian Zhu, Bokun Wang, Zhi Chen, Yaxing Wang, Milan Sonka, Xiaodong Wu, Tianbao Yang:
Provable Multi-instance Deep AUC Maximization with Stochastic Pooling. CoRR abs/2305.08040 (2023) - [i109]Zi-Hao Qiu, Quanqi Hu, Zhuoning Yuan, Denny Zhou, Lijun Zhang, Tianbao Yang:
Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization. CoRR abs/2305.11965 (2023) - [i108]Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo, Lijun Zhang, Tianbao Yang:
Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization. CoRR abs/2305.18730 (2023) - [i107]Zhuoning Yuan, Dixian Zhu, Zi-Hao Qiu, Gang Li, Xuanhui Wang, Tianbao Yang:
LibAUC: A Deep Learning Library for X-Risk Optimization. CoRR abs/2306.03065 (2023) - [i106]Wei Jiang, Jiayu Qin, Lingyu Wu, Changyou Chen, Tianbao Yang, Lijun Zhang:
Learning Unnormalized Statistical Models via Compositional Optimization. CoRR abs/2306.07485 (2023) - [i105]Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying:
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms. CoRR abs/2307.03357 (2023) - [i104]Haoran Liu, Bokun Wang, Jianling Wang, Xiangjue Dong, Tianbao Yang, James Caverlee:
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks. CoRR abs/2308.15614 (2023) - [i103]Bang An, Xun Zhou, Yongjian Zhong, Tianbao Yang:
SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data. CoRR abs/2310.00270 (2023) - [i102]Quanqi Hu, Dixian Zhu, Tianbao Yang:
Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization. CoRR abs/2310.03234 (2023) - [i101]Jianzhi Xv, Gang Li, Tianbao Yang:
AUC-mixup: Deep AUC Maximization with Mixup. CoRR abs/2310.11693 (2023) - [i100]Bokun Wang, Tianbao Yang:
ALEXR: Optimal Single-Loop Algorithms for Convex Finite-Sum Coupled Compositional Stochastic Optimization. CoRR abs/2312.02277 (2023) - [i99]Ryan King, Tianbao Yang, Bobak Mortazavi:
Multimodal Pretraining of Medical Time Series and Notes. CoRR abs/2312.06855 (2023) - 2022
- [j21]Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang:
Weakly-convex-concave min-max optimization: provable algorithms and applications in machine learning. Optim. Methods Softw. 37(3): 1087-1121 (2022) - [j20]Qingqing Hong, Xinyi Zhong, Weitong Chen, Zhenghua Zhang, Bin Li, Hao Sun, Tianbao Yang, Changwei Tan:
SATNet: A Spatial Attention Based Network for Hyperspectral Image Classification. Remote. Sens. 14(22): 5902 (2022) - [c113]Guanghui Wang, Ming Yang, Lijun Zhang, Tianbao Yang:
Momentum Accelerates the Convergence of Stochastic AUPRC Maximization. AISTATS 2022: 3753-3771 - [c112]Zhuoning Yuan, Zhishuai Guo, Nitesh V. Chawla, Tianbao Yang:
Compositional Training for End-to-End Deep AUC Maximization. ICLR 2022 - [c111]Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang:
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization. ICML 2022: 10195-10216 - [c110]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 - [c109]Bokun Wang, Tianbao Yang:
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications. ICML 2022: 23292-23317 - [c108]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 - [c107]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 - [c106]Lijun Zhang, Guanghui Wang, Jinfeng Yi, Tianbao Yang:
A Simple yet Universal Strategy for Online Convex Optimization. ICML 2022: 26605-26623 - [c105]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 - [c104]Quanqi Hu, Yongjian Zhong, Tianbao Yang:
Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization. NeurIPS 2022 - [c103]Wei Jiang, Gang Li, Yibo Wang, Lijun Zhang, Tianbao Yang:
Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization. NeurIPS 2022 - [c102]Yao Yao, Qihang Lin, Tianbao Yang:
Large-scale Optimization of Partial AUC in a Range of False Positive Rates. NeurIPS 2022 - [c101]Lijun Zhang, Wei Jiang, Jinfeng Yi, Tianbao Yang:
Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor. NeurIPS 2022 - [i98]Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang:
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization. CoRR abs/2202.07530 (2022) - [i97]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) - [i96]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. CoRR abs/2202.12387 (2022) - [i95]Bokun Wang, Tianbao Yang:
Finite-Sum Compositional Stochastic Optimization: Theory and Applications. CoRR abs/2202.12396 (2022) - [i94]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) - [i93]Yao Yao, Qihang Lin, Tianbao Yang:
Large-scale Optimization of Partial AUC in a Range of False Positive Rates. CoRR abs/2203.01505 (2022) - [i92]Dixian Zhu, Xiaodong Wu, Tianbao Yang:
Benchmarking Deep AUROC Optimization: Loss Functions and Algorithmic Choices. CoRR abs/2203.14177 (2022) - [i91]Tianbao Yang, Yiming Ying:
AUC Maximization in the Era of Big Data and AI: A Survey. CoRR abs/2203.15046 (2022) - [i90]Lijun Zhang, Wei Jiang, Jinfeng Yi, Tianbao Yang:
Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor. CoRR abs/2205.00741 (2022) - [i89]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) - [i88]Tianbao Yang:
Algorithmic Foundation of Deep X-Risk Optimization. CoRR abs/2206.00439 (2022) - [i87]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) - [i86]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) - [i85]Qi Qi, Jiameng Lyu, Kung-Sik Chan, Er-Wei Bai, Tianbao Yang:
Stochastic Constrained DRO with a Complexity Independent of Sample Size. CoRR abs/2210.05740 (2022) - [i84]Qi Qi, Shervin Ardeshir, Yi Xu, Tianbao Yang:
Fairness via Adversarial Attribute Neighbourhood Robust Learning. CoRR abs/2210.06630 (2022) - [i83]Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang:
FedX: Federated Learning for Compositional Pairwise Risk Optimization. CoRR abs/2210.14396 (2022) - [i82]Yao Yao, Qihang Lin, Tianbao Yang:
Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. CoRR abs/2212.12603 (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)