Search Results for author: Wei-Wei Tu

Found 32 papers, 16 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.

LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environments

1 code implementation26 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.

OpenRL: A Unified Reinforcement Learning Framework

1 code implementation20 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.

reinforcement-learning Reinforcement Learning (RL)

Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models

1 code implementation5 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.

DeepFake Detection Face Swapping

Efficient Stochastic Approximation of Minimax Excess Risk Optimization

no code implementations31 May 2023 Lijun Zhang, Haomin Bai, Wei-Wei Tu, Ping Yang, Yao Hu

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.

Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization

no code implementations9 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.

Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation

1 code implementation8 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

DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization

1 code implementation12 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.

reinforcement-learning Reinforcement Learning (RL)

Transfer and Share: Semi-Supervised Learning from Long-Tailed Data

no code implementations26 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.

Pseudo Label Representation Learning

Projection-free Online Learning with Arbitrary Delays

no code implementations11 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.

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.


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

1 code implementation16 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 +3

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 BIG-bench Machine Learning +1

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.

Projection-free Distributed Online Learning with Sublinear Communication Complexity

no code implementations20 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.

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 BIG-bench Machine Learning +1

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.

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 BIG-bench Machine Learning

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