28 citations · 87 across the 29 of their papers we have counts for
12 papers · 1 filter
LTSM-Bundle: A Toolbox and Benchmark on Large Language Models for Time Series Forecasting
Yu-Neng Chuang, Songchen Li, Jiayi Yuan +11
Time Series Forecasting (TSF) has long been a challenge in time series analysis. Inspired by the success of Large Language Models (LLMs), researchers are now developing Large Time…
TVE: Learning Meta-attribution for Transferable Vision Explainer
Guanchu Wang, Yu-Neng Chuang, Fan Yang +8
Explainable machine learning significantly improves the transparency of deep neural networks. However, existing work is constrained to explaining the behavior of individual model p…
Efficient GNN Explanation via Learning Removal-based Attribution
Yao Rong, Guanchu Wang, Qizhang Feng +4
As Graph Neural Networks (GNNs) have been widely used in real-world applications, model explanations are required not only by users but also by legal regulations. However, simultan…
Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model
Zirui Liu, Guanchu Wang, Shaochen Zhong +8
With the rapid growth in model size, fine-tuning the large pre-trained language model has become increasingly difficult due to its extensive memory usage. Previous works usually fo…
Interactive System-wise Anomaly Detection
Guanchu Wang, Ninghao Liu, Daochen Zha +1
Anomaly detection, where data instances are discovered containing feature patterns different from the majority, plays a fundamental role in various applications. However, it is cha…
CoRTX: Contrastive Framework for Real-time Explanation
Yu-Neng Chuang, Guanchu Wang, Fan Yang +4
Recent advancements in explainable machine learning provide effective and faithful solutions for interpreting model behaviors. However, many explanation methods encounter efficienc…
Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach
Zhimeng Jiang, Xiaotian Han, Hongye Jin +4
Fairness in machine learning has attracted increasing attention in recent years. The fairness methods improving algorithmic fairness for in-distribution data may not perform well u…
Efficient XAI Techniques: A Taxonomic Survey
Yu-Neng Chuang, Guanchu Wang, Fan Yang +4
Recently, there has been a growing demand for the deployment of Explainable Artificial Intelligence (XAI) algorithms in real-world applications. However, traditional XAI methods ty…
DIVISION: Memory Efficient Training via Dual Activation Precision
Guanchu Wang, Zirui Liu, Zhimeng Jiang +3
Activation compressed training provides a solution towards reducing the memory cost of training deep neural networks~(DNNs). However, state-of-the-art work combines a search of qua…
Mitigating Algorithmic Bias with Limited Annotations
Guanchu Wang, Mengnan Du, Ninghao Liu +2
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 th…
Accelerating Shapley Explanation via Contributive Cooperator Selection
Guanchu Wang, Yu-Neng Chuang, Mengnan Du +5
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 le…
Fairness via Representation Neutralization
Mengnan Du, Subhabrata Mukherjee, Guanchu Wang +3
Existing bias mitigation methods for DNN models primarily work on learning debiased encoders. This process not only requires a lot of instance-level annotations for sensitive attri…