no code implementations • ECCV 2020 • Zhuoning Yuan, Zhishuai Guo, Xiaotian Yu, Xiaoyu Wang, Tianbao Yang
In our experiment, we demonstrate that the proposed frame-work is able to train deep learning models with millions of classes and achieve above 10×speedup compared to existing approaches.
1 code implementation • 24 Feb 2022 • Zhuoning Yuan, Yuexin Wu, Zi-Hao Qiu, Xianzhi Du, Lijun Zhang, Denny Zhou, Tianbao Yang
In this paper, we study contrastive learning from an optimization perspective, aiming to analyze and address a fundamental issue of existing contrastive learning methods that either rely on a large batch size or a large dictionary of feature vectors.
no code implementations • ICLR 2022 • Zhuoning Yuan, Zhishuai Guo, Nitesh Chawla, Tianbao Yang
The key idea of compositional training is to minimize a compositional objective function, where the outer function corresponds to an AUC loss and the inner function represents a gradient descent step for minimizing a traditional loss, e. g., the cross-entropy (CE) loss.
no code implementations • 9 Jun 2021 • Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang
This paper addresses these issues by (i) proposing efficient memory-based stochastic algorithms for MAML with the vanishing convergence error, which only requires sampling a constant number of tasks and a constant number of data samples per-iteration; (ii) proposing communication-efficient distributed memory-based MAML algorithms for personalized federated learning in both the cross-device (with client sampling) and the cross-silo (without client sampling) settings.
1 code implementation • 9 Feb 2021 • Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang
Deep AUC (area under the ROC curve) Maximization (DAM) has attracted much attention recently due to its great potential for imbalanced data classification.
3 code implementations • ICCV 2021 • Zhuoning Yuan, Yan Yan, Milan Sonka, Tianbao Yang
Our studies demonstrate that the proposed DAM method improves the performance of optimizing cross-entropy loss by a large margin, and also achieves better performance than optimizing the existing AUC square loss on these medical image classification tasks.
Ranked #1 on Multi-Label Classification on CheXpert
1 code implementation • 2 Dec 2020 • Zhengyang Wang, Meng Liu, Youzhi Luo, Zhao Xu, Yaochen Xie, Limei Wang, Lei Cai, Qi Qi, Zhuoning Yuan, Tianbao Yang, Shuiwang Ji
Here we develop a suite of comprehensive machine learning methods and tools spanning different computational models, molecular representations, and loss functions for molecular property prediction and drug discovery.
no code implementations • 12 Jun 2020 • Zhishuai Guo, Yan Yan, Zhuoning Yuan, Tianbao Yang
Compared with existing studies, (i) our analysis is based on a novel Lyapunov function consisting of the primal objective gap and the duality gap of a regularized function, and (ii) the results are more comprehensive with improved rates that have better dependence on the condition number under different assumptions.
1 code implementation • ICML 2020 • Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang
In this paper, we study distributed algorithms for large-scale AUC maximization with a deep neural network as a predictive model.
no code implementations • ICLR 2020 • Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang
In this paper, we consider stochastic AUC maximization problem with a deep neural network as the predictive model.
no code implementations • NeurIPS 2019 • Zhuoning Yuan, Yan Yan, Rong Jin, Tianbao Yang
For convex loss functions and two classes of "nice-behaviored" non-convex objectives that are close to a convex function, we establish faster convergence of stagewise training than the vanilla SGD under the PL condition on both training error and testing error.
no code implementations • ICLR 2019 • Zaiyi Chen, Zhuoning Yuan, Jin-Feng Yi, Bo-Wen Zhou, Enhong Chen, Tianbao Yang
For example, there is still a lack of theories of convergence for SGD and its variants that use stagewise step size and return an averaged solution in practice.