Search Results for author: Wei-Wei Tu

Found 23 papers, 10 papers with code

Projection-free Distributed Online Convex Optimization with $O(\sqrt{T})$ Communication Complexity

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.

online learning

Online Frank-Wolfe with Unknown Delays

no code implementations11 Apr 2022 Yuanyu Wan, Wei-Wei Tu, Lijun Zhang

The online Frank-Wolfe (OFW) method has gained much popularity for online convex optimization due to its projection-free property.

Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020

1 code implementation6 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.

Graph Learning Neural Architecture Search +1

LTU Attacker for Membership Inference

1 code implementation4 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.

Inference Attack Membership Inference Attack

OmniPrint: A Configurable Printed Character Synthesizer

2 code implementations17 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.

Meta-Learning

Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform

2 code implementations12 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.

Robust Long-Tailed Learning under Label Noise

no code implementations26 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.

Image Classification

Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction

no code implementations16 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.

Diabetes Prediction Multi-Task Learning

AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge

1 code implementation28 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.

AutoML Feature Engineering +2

Auto-KWS 2021 Challenge: Task, Datasets, and Baselines

1 code implementation31 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.

AutoML Keyword Spotting

Online Strongly Convex Optimization with Unknown Delays

no code implementations21 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.

Improving Tail Label Prediction for Extreme Multi-label Learning

no code implementations1 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.

Multi-Label Learning

AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification

no code implementations25 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.

AutoML General Classification

Network On Network for Tabular Data Classification in Real-world Applications

no code implementations20 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.

General Classification

Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions

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.

online learning

Efficient Neural Architecture Search via Proximal Iterations

2 code implementations30 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.

Neural Architecture Search

SAdam: A Variant of Adam for Strongly Convex Functions

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.

AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications

no code implementations29 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.

Distributed Computing

AutoML @ NeurIPS 2018 challenge: Design and Results

no code implementations12 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.

AutoML

Taking Human out of Learning Applications: A Survey on Automated Machine Learning

1 code implementation31 Oct 2018 Quanming Yao, Mengshuo Wang, Yuqiang Chen, Wenyuan Dai, Yu-Feng Li, Wei-Wei Tu, Qiang Yang, Yang Yu

We hope this survey can serve as not only an insightful guideline for AutoML beginners but also an inspiration for future research.

AutoML

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