Search Results for author: Guanchu Wang

Found 13 papers, 8 papers with code

DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research

1 code implementation4 Sep 2023 Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Kwei-Herng Lai, Daochen Zha, Ruixiang Tang, Fan Yang, Alfredo Costilla Reyes, Kaixiong Zhou, Xiaoqian Jiang, Xia Hu

The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research.

named-entity-recognition Named Entity Recognition +4

DISPEL: Domain Generalization via Domain-Specific Liberating

no code implementations14 Jul 2023 Chia-Yuan Chang, Yu-Neng Chuang, Guanchu Wang, Mengnan Du, Na Zou

Domain generalization aims to learn a generalization model that can perform well on unseen test domains by only training on limited source domains.

Domain Generalization

Efficient GNN Explanation via Learning Removal-based Attribution

no code implementations9 Jun 2023 Yao Rong, Guanchu Wang, Qizhang Feng, Ninghao Liu, Zirui Liu, Enkelejda Kasneci, Xia Hu

A strategy of subgraph sampling is designed in LARA to improve the scalability of the training process.

Interactive System-wise Anomaly Detection

no code implementations21 Apr 2023 Guanchu Wang, Ninghao Liu, Daochen Zha, Xia Hu

Anomaly detection, where data instances are discovered containing feature patterns different from the majority, plays a fundamental role in various applications.

Anomaly Detection Data Poisoning

Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach

1 code implementation NeurIPS 2023 Zhimeng Jiang, Xiaotian Han, Hongye Jin, Guanchu Wang, Rui Chen, Na Zou, Xia Hu

Motivated by these sufficient conditions, we propose robust fairness regularization (RFR) by considering the worst case within the model weight perturbation ball for each sensitive attribute group.


CoRTX: Contrastive Framework for Real-time Explanation

1 code implementation5 Mar 2023 Yu-Neng Chuang, Guanchu Wang, Fan Yang, Quan Zhou, Pushkar Tripathi, Xuanting Cai, Xia Hu

In this work, we propose a COntrastive Real-Time eXplanation (CoRTX) framework to learn the explanation-oriented representation and relieve the intensive dependence of explainer training on explanation labels.

Contrastive Learning

Efficient XAI Techniques: A Taxonomic Survey

no code implementations7 Feb 2023 Yu-Neng Chuang, Guanchu Wang, Fan Yang, Zirui Liu, Xuanting Cai, Mengnan Du, Xia Hu

Finally, we summarize the challenges of deploying XAI acceleration methods to real-world scenarios, overcoming the trade-off between faithfulness and efficiency, and the selection of different acceleration methods.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

DIVISION: Memory Efficient Training via Dual Activation Precision

1 code implementation5 Aug 2022 Guanchu Wang, Zirui Liu, Zhimeng Jiang, Ninghao Liu, Na Zou, Xia Hu

Activation compressed training provides a solution towards reducing the memory cost of training deep neural networks~(DNNs).


Mitigating Algorithmic Bias with Limited Annotations

1 code implementation20 Jul 2022 Guanchu Wang, Mengnan Du, Ninghao Liu, Na Zou, Xia Hu

Existing work on fairness modeling commonly assumes that sensitive attributes for all instances are fully available, which may not be true in many real-world applications due to the high cost of acquiring sensitive information.


Accelerating Shapley Explanation via Contributive Cooperator Selection

1 code implementation17 Jun 2022 Guanchu Wang, Yu-Neng Chuang, Mengnan Du, Fan Yang, Quan Zhou, Pushkar Tripathi, Xuanting Cai, Xia Hu

Even though Shapley value provides an effective explanation for a DNN model prediction, the computation relies on the enumeration of all possible input feature coalitions, which leads to the exponentially growing complexity.

Fairness via Representation Neutralization

no code implementations NeurIPS 2021 Mengnan Du, Subhabrata Mukherjee, Guanchu Wang, Ruixiang Tang, Ahmed Hassan Awadallah, Xia Hu

This process not only requires a lot of instance-level annotations for sensitive attributes, it also does not guarantee that all fairness sensitive information has been removed from the encoder.

Classification Fairness

Cannot find the paper you are looking for? You can Submit a new open access paper.