Search Results for author: Zhuoning Yuan

Found 12 papers, 5 papers with code

Accelerating Deep Learning with Millions of Classes

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.

Classification General Classification +1

Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance

1 code implementation24 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.

Contrastive Learning Self-Supervised Learning +1

Compositional Training for End-to-End Deep AUC Maximization

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.

Image Classification Medical Image Classification +1

Memory-based Optimization Methods for Model-Agnostic Meta-Learning

no code implementations9 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.

Continual Learning Meta-Learning +2

Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity

1 code implementation9 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.

Federated Learning

Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image 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.

General Classification Graph Property Prediction +3

Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery

1 code implementation2 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.

BIG-bench Machine Learning Drug Discovery +1

Fast Objective & Duality Gap Convergence for Nonconvex-Strongly-Concave Min-Max Problems

no code implementations12 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.

Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks

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.

Distributed Optimization

Stochastic AUC Maximization with Deep Neural Networks

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.

Stagewise Training Accelerates Convergence of Testing Error Over SGD

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.

Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions

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.

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