Taking Human out of Learning Applications: A Survey on Automated Machine Learning. - Quanming Yao; Mengshuo Wang; Hugo Jair Escalante; Isabelle Guyon; Yi-Qi Hu; Yufeng Li 008; Wei-Wei Tu; Qiang Yang 001; Yang Yu 001 (CoRR, 2018)

Privacy-preserving Transfer Learning for Knowledge Sharing. - Xiawei Guo; Quanming Yao; Wei-Wei Tu; Yuqiang Chen; Wenyuan Dai; Qiang Yang 001 (CoRR, 2018)

Analysis of the AutoML Challenge Series 2015-2018. - Isabelle Guyon; Lisheng Sun-Hosoya; Marc Boullé; Hugo Jair Escalante; Sergio Escalera; Zhengying Liu; Damir Jajetic; Bisakha Ray; Mehreen Saeed; Michèle Sebag; Alexander R. Statnikov; Wei-Wei Tu; et al. (Automated Machine Learning, 2019)

Multi-Fidelity Automatic Hyper-Parameter Tuning via Transfer Series Expansion. - Yi-Qi Hu; Yang Yu 001; Wei-Wei Tu; Qiang Yang 001; Yuqiang Chen; Wenyuan Dai (AAAI, 2019)

Towards Automated Semi-Supervised Learning. - Yufeng Li 008; Hai Wang; Tong Wei 001; Wei-Wei Tu (AAAI, 2019)

Learning for Tail Label Data: A Label-Specific Feature Approach. - Tong Wei 001; Wei-Wei Tu; Yufeng Li 008 (IJCAI, 2019)

Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction. - Quanming Yao; Xiawei Guo; James T. Kwok; Wei-Wei Tu; Yuqiang Chen; Wenyuan Dai; Qiang Yang 001 (IJCAI, 2019)

AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications. - Yuanfei Luo; Mengshuo Wang; Hao Zhou; Quanming Yao; Wei-Wei Tu; Yuqiang Chen; Wenyuan Dai; Qiang Yang 001 (KDD, 2019)

Towards Automated Deep Learning: Analysis of the AutoDL challenge series 2019. - Zhengying Liu; Zhen Xu 007; Shangeth Rajaa; Meysam Madadi; Júlio C. S. Jacques Júnior; Sergio Escalera; Adrien Pavao; Sébastien Treguer; Wei-Wei Tu; Isabelle Guyon (NeurIPS (Competition and Demos), 2019)

AutoML @ NeurIPS 2018 challenge: Design and Results. - Hugo Jair Escalante; Wei-Wei Tu; Isabelle Guyon; Daniel L. Silver; Evelyne Viegas; Yuqiang Chen; Wenyuan Dai; Qiang Yang 001 (CoRR, 2019)

SAdam: A Variant of Adam for Strongly Convex Functions. - Guanghui Wang 006; Shiyin Lu; Weiwei Tu; Lijun Zhang 005 (CoRR, 2019)

Differentiable Neural Architecture Search via Proximal Iterations. - Quanming Yao; Ju Xu; Wei-Wei Tu; Zhanxing Zhu (CoRR, 2019)

Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions. - Lijun Zhang 005; Guanghui Wang 006; Weiwei Tu; Zhi-Hua Zhou (CoRR, 2019)

Towards AutoML in the presence of Drift: first results. - Jorge G. Madrid; Hugo Jair Escalante; Eduardo F. Morales 001; Wei-Wei Tu; Yang Yu 001; Lisheng Sun-Hosoya; Isabelle Guyon; Michèle Sebag (CoRR, 2019)

Efficient Neural Architecture Search via Proximal Iterations. - Quanming Yao; Ju Xu; Wei-Wei Tu; Zhanxing Zhu (AAAI, 2020)

SAdam: A Variant of Adam for Strongly Convex Functions. - Guanghui Wang 006; Shiyin Lu; Quan Cheng 001; Weiwei Tu; Lijun Zhang 005 (ICLR, 2020)

Projection-free Distributed Online Convex Optimization with $O( sqrt{T})$ Communication Complexity. - Yuanyu Wan; Wei-Wei Tu; Lijun Zhang 005 (ICML, 2020)

AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification. - Jingsong Wang; Tom Ko; Zhen Xu 007; Xiawei Guo; Souxiang Liu; Wei-Wei Tu; Lei Xie (INTERSPEECH, 2020)

Network On Network for Tabular Data Classification in Real-world Applications. - Yuanfei Luo; Hao Zhou; Wei-Wei Tu; Yuqiang Chen; Wenyuan Dai; Qiang Yang 001 (SIGIR, 2020)

MixPUL: Consistency-based Augmentation for Positive and Unlabeled Learning. - Tong Wei 001; Feng Shi; Hai Wang; Wei-Wei Tu; Yufeng Li 008 (CoRR, 2020)

Towards automated computer vision: analysis of the AutoCV challenges 2019. - Zhengying Liu; Zhen Xu 007; Sergio Escalera; Isabelle Guyon; Júlio C. S. Jacques Júnior; Meysam Madadi; Adrien Pavao; Sébastien Treguer; Wei-Wei Tu (Pattern Recognit. Lett., 2020)

Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction. - Xiawei Guo; Quanming Yao; James T. Kwok; Wei-Wei Tu; Yuqiang Chen; Wenyuan Dai; Qiang Yang 001 (Federated Learning, 2020)

Explanation Consistency Training: Facilitating Consistency-Based Semi-Supervised Learning with Interpretability. - Tao Han; Wei-Wei Tu; Yufeng Li 008 (AAAI, 2021)

Didn't see that coming: a survey on non-verbal social human behavior forecasting. - Germán Barquero; Johnny Núñez; Sergio Escalera; Zhen Xu 007; Wei-Wei Tu; Isabelle Guyon; Cristina Palmero (DYAD@ICCV, 2021)

Comparison of Spatio-Temporal Models for Human Motion and Pose Forecasting in Face-to-Face Interaction Scenarios. - Germán Barquero; Johnny Núñez; Zhen Xu 007; Sergio Escalera; Wei-Wei Tu; Isabelle Guyon; Cristina Palmero (DYAD@ICCV, 2021)

ChaLearn LAP Challenges on Self-Reported Personality Recognition and Non-Verbal Behavior Forecasting During Social Dyadic Interactions: Dataset, Design, and Results. - Cristina Palmero; Germán Barquero; Júlio C. S. Jacques Júnior; Albert Clapés; Johnny Núñez; David Curto; Sorina Smeureanu; Javier Selva; Zejian Zhang; David Saeteros; David Gallardo-Pujol; Georgina Guilera; et al. (DYAD@ICCV, 2021)

Understanding Social Behavior in Dyadic and Small Group Interactions: Preface. - Cristina Palmero; Júlio C. S. Jacques Júnior; Albert Clapés; Isabelle Guyon; Wei-Wei Tu; Thomas B. Moeslund; Sergio Escalera (DYAD@ICCV, 2021)

Search to aggregate neighborhood for graph neural network. - Huan Zhao 002; Quanming Yao; Weiwei Tu (ICDE, 2021)

Auto-KWS 2021 Challenge: Task, Datasets, and Baselines. - Jingsong Wang; Yuxuan He; Chunyu Zhao; Qijie Shao; Wei-Wei Tu; Tom Ko; Hung-yi Lee; Lei Xie (Interspeech, 2021)

Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction. - Mingcheng Chen; Zhenghui Wang; Zhiyun Zhao; Weinan Zhang 001; Xiawei Guo; Jian Shen 003; Yanru Qu; Jieli Lu; Min Xu; Yu Xu; Tiange Wang; Mian Li; et al. (KDD, 2021)

Towards Robust Prediction on Tail Labels. - Tong Wei 001; Wei-Wei Tu; Yufeng Li 008; Guo-Ping Yang (KDD, 2021)

OmniPrint: A Configurable Printed Character Synthesizer. - Haozhe Sun; Wei-Wei Tu; Isabelle Guyon (NeurIPS Datasets and Benchmarks, 2021)

Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions. - Lijun Zhang 005; Guanghui Wang 006; Wei-Wei Tu; Wei Jiang 029; Zhi-Hua Zhou (NeurIPS, 2021)

AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge. - Zhen Xu 007; Wei-Wei Tu; Isabelle Guyon (ECML/PKDD (5), 2021)

TabGNN: Multiplex Graph Neural Network for Tabular Data Prediction. - Xiawei Guo; Yuhan Quan; Huan Zhao 002; Quanming Yao; Yong Li 008; Weiwei Tu (CoRR, 2021)

Robust Long-Tailed Learning under Label Noise. - Tong Wei 001; Jiang-Xin Shi; Wei-Wei Tu; Yufeng Li 008 (CoRR, 2021)

Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking for Everyone. - Zhen Xu 007; Huan Zhao 002; Wei-Wei Tu; Magali Richard; Sergio Escalera; Isabelle Guyon (CoRR, 2021)

Optimal ϵ -stealthy attack in cyber-physical systems. - Weiwei Tu; Jiuxiang Dong; Ding Zhai (J. Frankl. Inst., 2021)

Guest Editorial: Automated Machine Learning. - Hugo Jair Escalante; Quanming Yao; Wei-Wei Tu; Nelishia Pillay; Rong Qu; Yang Yu 001; Neil Houlsby (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Multiple Temporal Fusion based Weakly-supervised Pre-training Techniques for Video Categorization. - Xiaochen Cai; Hengxing Cai; Boqing Zhu; Kele Xu; Weiwei Tu; Dawei Feng (ACM Multimedia, 2022)

VSM: A Versatile Semi-supervised Model for Multi-modal Cell Instance Segmentation. - Xiaochen Cai; Hengxing Cai; Kele Xu; Wei-Wei Tu; Wu-Jun Li (Cell Segmentation Challenge @ NeurIPS, 2022)

Online Frank-Wolfe with Arbitrary Delays. - Yuanyu Wan; Wei-Wei Tu; Lijun Zhang 005 (NeurIPS, 2022)

Diverse Policies Converge in Reward-Free Markov Decision Processes. - Fanqi Lin; Shiyu Huang 001; Wei-Wei Tu (PRICAI (1), 2022)

Translation-Based Implicit Annotation Projection for Zero-Shot Cross-Lingual Event Argument Extraction. - Chenwei Lou; Jun Gao; Changlong Yu; Wei Wang 138; Huan Zhao 002; Weiwei Tu; Ruifeng Xu 001 (SIGIR, 2022)

LTU Attacker for Membership Inference. - Joseph Pedersen; Rafael Muñoz-Gómez; Jiangnan Huang 001; Haozhe Sun; Wei-Wei Tu; Isabelle Guyon (Algorithms, 2022)

Strongly adaptive online learning over partial intervals. - Yuanyu Wan; Wei-Wei Tu; Lijun Zhang 005 (Sci. China Inf. Sci., 2022)

Robust model selection for positive and unlabeled learning with constraints. - Tong Wei 001; Hai Wang; Weiwei Tu; Yufeng Li 008 (Sci. China Inf. Sci., 2022)

Graph Neural Networks for Double-Strand DNA Breaks Prediction. - Xu Wang; Huan Zhao 002; Weiwei Tu; Hao Li 035; Yu Sun; Xiaochen Bo (CoRR, 2022)

Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020. - Zhen Xu 007; Lanning Wei; Huan Zhao 002; Rex Ying; Quanming Yao; Wei-Wei Tu; Isabelle Guyon (CoRR, 2022)

Online Frank-Wolfe with Unknown Delays. - Yuanyu Wan; Wei-Wei Tu; Lijun Zhang 005 (CoRR, 2022)

Bridging the Gap of AutoGraph Between Academia and Industry: Analyzing AutoGraph Challenge at KDD Cup 2020. - Zhen Xu 007; Lanning Wei; Huan Zhao 002; Rex Ying; Quanming Yao; Wei-Wei Tu; Isabelle Guyon (Frontiers Artif. Intell., 2022)

Projection-free Distributed Online Learning with Sublinear Communication Complexity. - Yuanyu Wan; Guanghui Wang 006; Wei-Wei Tu; Lijun Zhang 005 (J. Mach. Learn. Res., 2022)

Online strongly convex optimization with unknown delays. - Yuanyu Wan; Wei-Wei Tu; Lijun Zhang 005 (Mach. Learn., 2022)

Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform. - Zhen Xu 007; Sergio Escalera; Adrien Pavão; Magali Richard; Wei-Wei Tu; Quanming Yao; Huan Zhao 002; Isabelle Guyon (Patterns, 2022)

TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play. - Fanqi Lin; Shiyu Huang 001; Tim Pearce; Wenze Chen; Wei-Wei Tu (AAMAS, 2023)

Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation. - Xinyi Yang 001; Shiyu Huang 001; Yiwen Sun; Yuxiang Yang; Chao Yu 005; Wei-Wei Tu; Huazhong Yang; Yu Wang 002 (AAMAS, 2023)

Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization. - Sijia Chen; Wei-Wei Tu; Peng Zhao 006; Lijun Zhang 005 (ICML, 2023)

Automated 3D Pre-Training for Molecular Property Prediction. - Xu Wang; Huan Zhao 002; Wei-Wei Tu; Quanming Yao (KDD, 2023)

Efficient Stochastic Approximation of Minimax Excess Risk Optimization. - Lijun Zhang 005; Wei-Wei Tu (CoRR, 2023)

Diverse Policies Converge in Reward-free Markov Decision Processe. - Fanqi Lin; Shiyu Huang 001; Weiwei Tu (CoRR, 2023)

Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models. - Haixu Song; Shiyu Huang 001; Yinpeng Dong; Wei-Wei Tu (CoRR, 2023)

OpenRL: A Unified Reinforcement Learning Framework. - Shiyu Huang 001; Wentse Chen; Yiwen Sun; Fuqing Bie; Wei-Wei Tu (CoRR, 2023)

Improved robust reduced-order sliding mode fault-tolerant control for T-S fuzzy systems with disturbances. - Weiwei Tu; Jiuxiang Dong (Fuzzy Sets Syst., 2023)

Robust sliding mode control for a class of nonlinear systems through dual-layer sliding mode scheme. - Weiwei Tu; Jiuxiang Dong (J. Frankl. Inst., 2023)

DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization. - Wentse Chen; Shiyu Huang 001; Yuan Chiang; Tim Pearce; Wei-Wei Tu; Ting Chen 006; Jun Zhu 001 (AAAI, 2024)

Safe Abductive Learning in the Presence of Inaccurate Rules. - Xiaowen Yang; Jie-Jing Shao; Wei-Wei Tu; Yufeng Li 008; Wang-Zhou Dai; Zhi-Hua Zhou (AAAI, 2024)

LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environments. - Junzhe Chen 001; Xuming Hu; Shuodi Liu; Shiyu Huang 001; Wei-Wei Tu; Zhaofeng He; Lijie Wen 001 (ACL (1), 2024)

Efficient Stochastic Approximation of Minimax Excess Risk Optimization. - Lijun Zhang 005; Haomin Bai; Wei-Wei Tu; Ping Yang; Yao Hu (ICML, 2024)

MQE: Unleashing the Power of Interaction with Multi-agent Quadruped Environment. - Ziyan Xiong; Bo Chen; Shiyu Huang 001; Wei-Wei Tu; Zhaofeng He; Yang Gao (IROS, 2024)

A Survey on Self-play Methods in Reinforcement Learning. - Ruize Zhang 001; Zelai Xu; Chengdong Ma; Chao Yu 005; Wei-Wei Tu; Shiyu Huang 001; Deheng Ye; Wenbo Ding 001; Yaodong Yang 001; Yu Wang 002 (CoRR, 2024)

Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization. - Sijia Chen; Yu-Jie Zhang; Wei-Wei Tu; Peng Zhao 006; Lijun Zhang 005 (J. Mach. Learn. Res., 2024)

Transfer and share: semi-supervised learning from long-tailed data. - Tong Wei 001; Qian-Yu Liu; Jiang-Xin Shi; Wei-Wei Tu; Lan-Zhe Guo (Mach. Learn., 2024)

Guest Editorial: AutoML for Nonstationary Data. - Ran Cheng; Hugo Jair Escalante; Wei-Wei Tu; Jan N. van Rijn; Shuo Wang 005; Yun Yang 003 (IEEE Trans. Artif. Intell., 2024)

Optimal large-scale stochastic optimization of NDCG surrogates for deep learning. - Zi-Hao Qiu; Quanqi Hu; Yongjian Zhong; Wei-Wei Tu; Lijun Zhang 005; Tianbao Yang (Mach. Learn., 2025)