Author name cluster

Yukuo Cen

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4 papers

2 author rows

AAAI Conference 2023 Conference Paper

MTDiag: An Effective Multi-Task Framework for Automatic Diagnosis

Zhenyu Hou
Yukuo Cen
Ziding Liu
Dongxue Wu
Baoyan Wang
Xuanhe Li
Lei Hong
Jie Tang

Automatic diagnosis systems aim to probe for symptoms (i.e., symptom checking) and diagnose disease through multi-turn conversations with patients. Most previous works formulate it as a sequential decision process and use reinforcement learning (RL) to decide whether to inquire about symptoms or make a diagnosis. However, these RL-based methods heavily rely on the elaborate reward function and usually suffer from an unstable training process and low data efficiency. In this work, we propose an effective multi-task framework for automatic diagnosis called MTDiag. We first reformulate symptom checking as a multi-label classification task by direct supervision. Each medical dialogue is equivalently converted into multiple samples for classification, which can also help alleviate the data scarcity problem. Furthermore, we design a multi-task learning strategy to guide the symptom checking procedure with disease information and further utilize contrastive learning to better distinguish symptoms between diseases. Extensive experimental results show that our method achieves state-of-the-art performance on four public datasets with 1.7%~3.1% improvement in disease diagnosis, demonstrating the superiority of the proposed method. Additionally, our model is now deployed in an online medical consultant system as an assistant tool for real-life doctors.

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ICML Conference 2022 Conference Paper

GACT: Activation Compressed Training for Generic Network Architectures

Xiaoxuan Liu
Lianmin Zheng
Dequan Wang
Yukuo Cen
Weize Chen
Xu Han 0007
Jianfei Chen 0001
Zhiyuan Liu 0001

Training large neural network (NN) models requires extensive memory resources, and Activation Compression Training (ACT) is a promising approach to reduce training memory footprint. This paper presents GACT, an ACT framework to support a broad range of machine learning tasks for generic NN architectures with limited domain knowledge. By analyzing a linearized version of ACT’s approximate gradient, we prove the convergence of GACT without prior knowledge on operator type or model architecture. To make training stable, we propose an algorithm that decides the compression ratio for each tensor by estimating its impact on the gradient at run time. We implement GACT as a PyTorch library that readily applies to any NN architecture. GACT reduces the activation memory for convolutional NNs, transformers, and graph NNs by up to 8. 1x, enabling training with a 4. 2x to 24. 7x larger batch size, with negligible accuracy loss.

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IJCAI Conference 2022 Conference Paper

Rethinking the Setting of Semi-supervised Learning on Graphs

Ziang Li
Ming Ding
Weikai Li
Zihan Wang
Ziyu Zeng
Yukuo Cen
Jie Tang

We argue that the present setting of semisupervised learning on graphs may result in unfair comparisons, due to its potential risk of over-tuning hyper-parameters for models. In this paper, we highlight the significant influence of tuning hyper-parameters, which leverages the label information in the validation set to improve the performance. To explore the limit of over-tuning hyperparameters, we propose ValidUtil, an approach to fully utilize the label information in the validation set through an extra group of hyper-parameters. With ValidUtil, even GCN can easily get high accuracy of 85. 8% on Cora. To avoid over-tuning, we merge the training set and the validation set and construct an i. i. d. graph benchmark (IGB) consisting of 4 datasets. Each dataset contains 100 i. i. d. graphs sampled from a large graph to reduce the evaluation variance. Our experiments suggest that IGB is a more stable benchmark than previous datasets for semisupervised learning on graphs. Our code and data are released at https: //github. com/THUDM/IGB/.

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NeurIPS Conference 2021 Conference Paper

Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning

Qinkai Zheng
Xu Zou
Yuxiao Dong
Yukuo Cen
Da Yin
Jiarong Xu
Yang Yang
Jie Tang

Adversarial attacks on graphs have posed a major threat to the robustness of graph machine learning (GML) models. Naturally, there is an ever-escalating arms race between attackers and defenders. However, the strategies behind both sides are often not fairly compared under the same and realistic conditions. To bridge this gap, we present the Graph Robustness Benchmark (GRB) with the goal of providing a scalable, unified, modular, and reproducible evaluation for the adversarial robustness of GML models. GRB standardizes the process of attacks and defenses by 1) developing scalable and diverse datasets, 2) modularizing the attack and defense implementations, and 3) unifying the evaluation protocol in refined scenarios. By leveraging the modular GRB pipeline, the end-users can focus on the development of robust GML models with automated data processing and experimental evaluations. To support open and reproducible research on graph adversarial learning, GRB also hosts public leaderboards for different scenarios. As a starting point, we provide various baseline experiments to benchmark the state-of-the-art techniques. GRB is an open-source benchmark and all datasets, code, and leaderboards are available at https: //cogdl. ai/grb/home.

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