SelectedWorks - Bin Gu

Selected Works of Bin Gu

Assistant Professor, Department of Machine Learning, MBZUAI

Dr. Bin Gu is an Assistant Professor in the Machine Learning Department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).

Biography

Dr. Bin Gu was a full professor of Nanjing University of Information Science and Technology before joining MBZUAI. He received the B.S. and Ph.D. degrees in Computer Science from the Nanjing University of Aeronautics and Astronautics, in 2005 and 2011, respectively. He was a Post-Doctoral Fellow with the University of Western Ontario from 2013 to 2015, with the University of Texas at Arlington from 2016 to 2017, and the University of Pittsburgh from 2017 to 2018. His research interests focus on large scaling optimization in machine learning and data mining. He has published 40 more papers, with over 2,000 citations according to Google Scholar.  He served as a program committee member or reviewer for several leading machine learning and data mining conferences and journals such as NIPS, ICML, KDD, AAAI, TPAMI, JMLR, and a senior program committee member of IJCAI 2019 and 2020.

Works
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About Bin Gu

Dr. Bin Gu is an Assistant Professor in the Machine Learning Department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).

Biography

Dr. Bin Gu was a full professor of Nanjing University of Information Science and Technology before joining MBZUAI. He received the B.S. and Ph.D. degrees in Computer Science from the Nanjing University of Aeronautics and Astronautics, in 2005 and 2011, respectively. He was a Post-Doctoral Fellow with the University of Western Ontario from 2013 to 2015, with the University of Texas at Arlington from 2016 to 2017, and the University of Pittsburgh from 2017 to 2018. His research interests focus on large scaling optimization in machine learning and data mining. He has published 40 more papers, with over 2,000 citations according to Google Scholar.  He served as a program committee member or reviewer for several leading machine learning and data mining conferences and journals such as NIPS, ICML, KDD, AAAI, TPAMI, JMLR, and a senior program committee member of IJCAI 2019 and 2020.

Present	Assistant Professor, Mohamed bin Zayed University of Artificial Intelligence ‐ Department of Machine Learning
Institution Organization Position Type Position Title Time Period to Options Current Position Affiliate my profile with this Institution

Computer Sciences, Artificial Intelligence and Robotics, Statistics and Probability, and Data Science

optimization, data mining, and medical image analysis

http://orcid.org/0000-0001-8653-1117

Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm (Qingsong Zhang, Bin Gu, Zhiyuan Dang, et al.) International Conference on Information and Knowledge Management, Proceedings (2021)

Vertical federated learning (VFL) attracts increasing attention due to the emerging demands of multi-party collaborative modeling and concerns of privacy leakage. A complete list of metrics to evaluate VFL algorithms should include model applicability, privacy ...

AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization (Qingsong Zhang, Bin Gu, Cheng Deng, et al.) Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2021)

Vertical federated learning (VFL) is an effective paradigm of training the emerging cross-organizational (e.g., different corporations, companies and organizations) collaborative learning with privacy preserving. Stochastic gradient descent (SGD) methods are the popular choices for training ...

Kernel Path for nu-Support Vector Classification (Bin Gu, Ziran Xiong, Zhai Zhou, et al.) IEEE Transactions on Neural Networks and Learning Systems (2021)

It is well known that the performance of a kernel method is highly dependent on the choice of kernel parameter. However, existing kernel path algorithms are limited to plain support vector machines (SVMs), which has ...

Finding Age Path of Self-Paced Learning (Bin Gu, Zhou Zhai, Xiang Li, et al.) Proceedings - IEEE International Conference on Data Mining, ICDM (2021)

Self-paced learning (SPL) is an emerging research topic in recent machine learning research which is often formulated as a bi-convex problem. The choice of the age parameter in SPL can control the learning pace and ...

Improved Penalty Method via Doubly Stochastic Gradients for Bilevel Hyperparameter Optimization (Wanli Shi and Bin Gu) Thirty-Fifth AAAI Conference on Artificial Intelligence, Thirty-third Conference on Innovative Applications of Artificial Intelligence and the Eleventh Symposium on Educational Advances in Artificial Intelligence (2021)

Hyperparameter optimization (HO) is an important problem in machine learning which is normally formulated as a bilevel optimization problem. Gradient-based methods are dominant in bilevel optimization due to their high scalability to the number of ...

GAGA: Deciphering Age-path of Generalized Self-paced Regularizer (Xingyu Qu, Diyang Li, Xiaohan Zhao, et al.) arXiv (2022)

Nowadays self-paced learning (SPL) is an important machine learning paradigm that mimics the cognitive process of humans and animals. The SPL regime involves a self-paced regularizer and a gradually increasing age parameter, which plays a ...

Balanced Self-Paced Learning for AUC Maximization (Bin Gu, Chenkang Zhang, Huan Xiong, et al.) arXiv (2022)

Learning to improve AUC performance is an important topic in machine learning. However, AUC maximization algorithms may decrease generalization performance due to the noisy data. Self-paced learning is an effective method for handling noisy data. ...

Stochastic Bilevel Distributed Optimization over a Network (Hongchang Gao, Bin Gu and My T. Thai) arXiv (2022)

Bilevel optimization has been applied to a wide variety of machine learning models. Numerous stochastic bilevel optimization algorithms have been developed in recent years. However, most of them restrict their focus on the single-machine setting ...

Learning to Control under Time-Varying Environment (Yuzhen Han, Ruben Solozabal, Jing Dong, et al.) arXiv (2022)

This paper investigates the problem of regret minimization in linear time-varying (LTV) dynamical systems. Due to the simultaneous presence of uncertainty and non-stationarity, designing online control algorithms for unknown LTV systems remains a challenging task. ...

The Power Of First-Order Smooth Optimization for Black-Box Non-Smooth Problems (Alexander V. Gasnikov., Anton Novitskii, Vasilii Novitskii, et al.) arXiv (2022)

Gradient-free/zeroth-order methods for black-box convex optimization have been extensively studied in the last decade with the main focus on oracle calls complexity. In this paper, besides the oracle complexity, we focus also on iteration complexity, ...

A Fully Single Loop Algorithm for Bilevel Optimization Without Hessian Inverse (Junyi Li, Bin Gu and Heng Huang) arXiv (2021)

In this paper, we propose a new Hessian inverse free Fully Single Loop Algorithm (FSLA) for bilevel optimization problems. Classic algorithms for bilevel optimization admit a double loop structure which is computationally expensive. Recently, several ...

An Accelerated Variance-Reduced Conditional Gradient Sliding Algorithm For First-Order And Zeroth-Order Optimization (Xiyuan Wei, Bin Gu and Heng Huang) arXiv (2021)

The conditional gradient algorithm (also known as the Frank-Wolfe algorithm) has recently regained popularity in the machine learning community due to its projection-free property to solve constrained problems. Although many variants of the conditional gradient ...

Fast And Scalable Adversarial Training Of Kernel SVM Via Doubly Stochastic Gradients (Huimin Wu, Zhengmian Hu and Bin Gu) arXiv (2021)

Adversarial attacks by generating examples which are almost indistinguishable from natural examples, pose a serious threat to learning models. Defending against adversarial attacks is a critical element for a reliable learning system. Support vector machine ...

Learning Sampling Policy for Faster Derivative Free Optimization (Zhou Zhai, Bin Gu and Heng Huang) arXiv (2021)

Zeroth-order (ZO, also known as derivative-free) methods, which estimate the gradient only by two function evaluations, have attracted much attention recently because of its broad applications in machine learning community. The two function evaluations are ...

Secure Bilevel Asynchronous Vertical Federated Learning With Backward Updating (Qingsong Zhang, Bin Gu, Cheng Deng, et al.) arXiv (2021)

Vertical federated learning (VFL) attracts increasing attention due to the emerging demands of multi-party collaborative modeling and concerns of privacy leakage. In the real VFL applications, usually only one or partial parties hold labels, which ...

Optimizing Large-Scale Hyperparameters Via Automated Learning Algorithm (Bin Gu, Guodong Liu, Yanfu Zhang, et al.) arXiv (2021)

Modern machine learning algorithms usually involve tuning multiple (from one to thousands) hyperparameters which play a pivotal role in terms of model generalizability. Black-box optimization and gradient-based algorithms are two dominant approaches to hyperparameter optimization ...