no code implementations • 12 Mar 2019 • Hugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon, Daniel L. Silver, Evelyne Viegas, Yuqiang Chen, Wenyuan Dai, Qiang Yang
We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018.
no code implementations • 29 Apr 2019 • Yuanfei Luo, Mengshuo Wang, Hao Zhou, Quanming Yao, Wei-Wei Tu, Yuqiang Chen, Qiang Yang, Wenyuan Dai
Feature crossing captures interactions among categorical features and is useful to enhance learning from tabular data in real-world businesses.
1 code implementation • ICLR 2020 • Guanghui Wang, Shiyin Lu, Wei-Wei Tu, Lijun Zhang
In this paper, we give an affirmative answer by developing a variant of Adam (referred to as SAdam) which achieves a data-dependant $O(\log T)$ regret bound for strongly convex functions.
2 code implementations • 30 May 2019 • Quanming Yao, Ju Xu, Wei-Wei Tu, Zhanxing Zhu
Recently, DARTS, which constructs a differentiable search space and then optimizes it by gradient descent, can obtain high-performance architecture and reduces the search time to several days.
no code implementations • NeurIPS 2021 • Lijun Zhang, Guanghui Wang, Wei-Wei Tu, Zhi-Hua Zhou
Along this line of research, this paper presents the first universal algorithm for minimizing the adaptive regret of convex functions.
no code implementations • 24 Jul 2019 • Jorge G. Madrid, Hugo Jair Escalante, Eduardo F. Morales, Wei-Wei Tu, Yang Yu, Lisheng Sun-Hosoya, Isabelle Guyon, Michele Sebag
We extendAuto-Sklearn with sound and intuitive mechanisms that allow it to cope with this sort ofproblems.
no code implementations • 20 May 2020 • Yuanfei Luo, Hao Zhou, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang
As a result, the intra-field information and the non-linear interactions between those operations (e. g. neural network and factorization machines) are ignored.
no code implementations • 25 Oct 2020 • Jingsong Wang, Tom Ko, Zhen Xu, Xiawei Guo, Souxiang Liu, Wei-Wei Tu, Lei Xie
The AutoSpeech challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to speech processing tasks.
no code implementations • 1 Jan 2021 • Tong Wei, Wei-Wei Tu, Yu-Feng Li
Extreme multi-label learning (XML) works to annotate objects with relevant labels from an extremely large label set.
no code implementations • 20 Mar 2021 • Yuanyu Wan, Guanghui Wang, Wei-Wei Tu, Lijun Zhang
In this paper, we propose an improved variant of D-OCG, namely D-BOCG, which can attain the same $O(T^{3/4})$ regret bound with only $O(\sqrt{T})$ communication rounds for convex losses, and a better regret bound of $O(T^{2/3}(\log T)^{1/3})$ with fewer $O(T^{1/3}(\log T)^{2/3})$ communication rounds for strongly convex losses.
no code implementations • 21 Mar 2021 • Yuanyu Wan, Wei-Wei Tu, Lijun Zhang
Specifically, we first extend the delayed variant of OGD for strongly convex functions, and establish a better regret bound of $O(d\log T)$, where $d$ is the maximum delay.
1 code implementation • 31 Mar 2021 • Jingsong Wang, Yuxuan He, Chunyu Zhao, Qijie Shao, Wei-Wei Tu, Tom Ko, Hung-Yi Lee, Lei Xie
Auto-KWS 2021 challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to a customized keyword spotting task.
1 code implementation • 28 Jul 2021 • Zhen Xu, Wei-Wei Tu, Isabelle Guyon
Driven by business scenarios, we organized the first Automated Time Series Regression challenge (AutoSeries) for the WSDM Cup 2020.
1 code implementation • 16 Aug 2021 • Mingcheng Chen, Zhenghui Wang, Zhiyun Zhao, Weinan Zhang, Xiawei Guo, Jian Shen, Yanru Qu, Jieli Lu, Min Xu, Yu Xu, Tiange Wang, Mian Li, Wei-Wei Tu, Yong Yu, Yufang Bi, Weiqing Wang, Guang Ning
To tackle the above challenges, we employ gradient boosting decision trees (GBDT) to handle data heterogeneity and introduce multi-task learning (MTL) to solve data insufficiency.
no code implementations • 26 Aug 2021 • Tong Wei, Jiang-Xin Shi, Wei-Wei Tu, Yu-Feng Li
To overcome this limitation, we establish a new prototypical noise detection method by designing a distance-based metric that is resistant to label noise.
Ranked #25 on Image Classification on mini WebVision 1.0
2 code implementations • 12 Oct 2021 • Zhen Xu, Sergio Escalera, Isabelle Guyon, Adrien Pavão, Magali Richard, Wei-Wei Tu, Quanming Yao, Huan Zhao
A public instance of Codabench (https://www. codabench. org/) is open to everyone, free of charge, and allows benchmark organizers to compare fairly submissions, under the same setting (software, hardware, data, algorithms), with custom protocols and data formats.
2 code implementations • 17 Jan 2022 • Haozhe Sun, Wei-Wei Tu, Isabelle Guyon
We introduce OmniPrint, a synthetic data generator of isolated printed characters, geared toward machine learning research.
1 code implementation • 4 Feb 2022 • Joseph Pedersen, Rafael Muñoz-Gómez, Jiangnan Huang, Haozhe Sun, Wei-Wei Tu, Isabelle Guyon
In both cases classification accuracy or error rate are used as the metric: Utility is evaluated with the classification accuracy of the Defender model; Privacy is evaluated with the membership prediction error of a so-called "Leave-Two-Unlabeled" LTU Attacker, having access to all of the Defender and Reserved data, except for the membership label of one sample from each.
no code implementations • 4 Mar 2022 • German Barquero, Johnny Núñez, Sergio Escalera, Zhen Xu, Wei-Wei Tu, Isabelle Guyon, Cristina Palmero
Non-verbal social human behavior forecasting has increasingly attracted the interest of the research community in recent years.
1 code implementation • 7 Mar 2022 • German Barquero, Johnny Núñez, Zhen Xu, Sergio Escalera, Wei-Wei Tu, Isabelle Guyon, Cristina Palmero
In this work, we present the first systematic comparison of state-of-the-art approaches for behavior forecasting.
1 code implementation • 6 Apr 2022 • Zhen Xu, Lanning Wei, Huan Zhao, Rex Ying, Quanming Yao, Wei-Wei Tu, Isabelle Guyon
Researchers naturally adopt Automated Machine Learning on Graph Learning, aiming to reduce the human effort and achieve generally top-performing GNNs, but their methods focus more on the architecture search.
no code implementations • 11 Apr 2022 • Yuanyu Wan, Yibo Wang, Chang Yao, Wei-Wei Tu, Lijun Zhang
Projection-free online learning, which eschews the projection operation via less expensive computations such as linear optimization (LO), has received much interest recently due to its efficiency in handling high-dimensional problems with complex constraints.
no code implementations • 26 May 2022 • Tong Wei, Qian-Yu Liu, Jiang-Xin Shi, Wei-Wei Tu, Lan-Zhe Guo
TRAS transforms the imbalanced pseudo-label distribution of a traditional SSL model via a delicate function to enhance the supervisory signals for minority classes.
1 code implementation • 12 Jul 2022 • Wentse Chen, Shiyu Huang, Yuan Chiang, Tim Pearce, Wei-Wei Tu, Ting Chen, Jun Zhu
We propose Diversity-Guided Policy Optimization (DGPO), an on-policy algorithm that discovers multiple strategies for solving a given task.
1 code implementation • 8 Feb 2023 • Xinyi Yang, Shiyu Huang, Yiwen Sun, Yuxiang Yang, Chao Yu, Wei-Wei Tu, Huazhong Yang, Yu Wang
Goal-conditioned hierarchical reinforcement learning (HRL) provides a promising direction to tackle this challenge by introducing a hierarchical structure to decompose the search space, where the low-level policy predicts primitive actions in the guidance of the goals derived from the high-level policy.
Hierarchical Reinforcement Learning Multi-agent Reinforcement Learning +2
no code implementations • 9 Feb 2023 • Sijia Chen, Yu-Jie Zhang, Wei-Wei Tu, Peng Zhao, Lijun Zhang
Inspired by their work, we investigate the theoretical guarantees of optimistic online mirror descent (OMD) for the SEA model.
1 code implementation • 15 Feb 2023 • Fanqi Lin, Shiyu Huang, Tim Pearce, Wenze Chen, Wei-Wei Tu
Multi-agent football poses an unsolved challenge in AI research.
no code implementations • 31 May 2023 • Lijun Zhang, Wei-Wei Tu
While traditional distributionally robust optimization (DRO) aims to minimize the maximal risk over a set of distributions, Agarwal and Zhang (2022) recently proposed a variant that replaces risk with excess risk.
1 code implementation • 5 Sep 2023 • Haixu Song, Shiyu Huang, Yinpeng Dong, Wei-Wei Tu
The rise of deepfake images, especially of well-known personalities, poses a serious threat to the dissemination of authentic information.
1 code implementation • 20 Dec 2023 • Shiyu Huang, Wentse Chen, Yiwen Sun, Fuqing Bie, Wei-Wei Tu
We present OpenRL, an advanced reinforcement learning (RL) framework designed to accommodate a diverse array of tasks, from single-agent challenges to complex multi-agent systems.
no code implementations • 26 Feb 2024 • Junzhe Chen, Xuming Hu, Shuodi Liu, Shiyu Huang, Wei-Wei Tu, Zhaofeng He, Lijie Wen
Recent advancements in large language models (LLMs) have revealed their potential for achieving autonomous agents possessing human-level intelligence.
no code implementations • ICML 2020 • Yuanyu Wan, Wei-Wei Tu, Lijun Zhang
To deal with complicated constraints via locally light computation in distributed online learning, recent study has presented a projection-free algorithm called distributed online conditional gradient (D-OCG), and achieved an $O(T^{3/4})$ regret bound, where $T$ is the number of prediction rounds.