23 citations · 156 across the 51 of their papers we have counts for
32 papers · 1 filter
Your LLM, Your Style: Behavioral Mode Axes for LLM Behavioral Control
Haoze Liu, Run Liu, Haiying Xu +6
Large language models (LLMs) increasingly act in interactive settings where their behavioral styles affect user experience, safety, and downstream decision making. Existing LLM per…
RASFT: Rollout-Adaptive Supervised Fine-Tuning for Reasoning
Yongliang Miao, Fengyuan Liu, Wei Shi +4
Supervised fine-tuning (SFT) is a prevailing method for adapting large language models to reasoning tasks by imitating offline expert demonstrations, often treating a single expert…
To Call or Not to Call: Diagnosing Intrinsic Over-Calling Bias in LLM Agents
Wei Shi, Ziheng Peng, Sihang Li +4
LLM agents exhibit a consistent tendency to over-call, invoking tools even in situations where none is needed. On the When2Call benchmark, six models from three families show high…
Gradient Rewiring for Editable Graph Neural Network Training
Zhimeng Jiang, Zirui Liu, Xiaotian Han +6
Deep neural networks are ubiquitously adopted in many applications, such as computer vision, natural language processing, and graph analytics. However, well-trained neural networks…
IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networks
Yushun Dong, Binchi Zhang, Zhenyu Lei +2
Graph Neural Networks (GNNs) have been increasingly deployed in a plethora of applications. However, the graph data used for training may contain sensitive personal information of…
Chasing Fairness in Graphs: A GNN Architecture Perspective
Zhimeng Jiang, Xiaotian Han, Chao Fan +4
There has been significant progress in improving the performance of graph neural networks (GNNs) through enhancements in graph data, model architecture design, and training strateg…
Marginal Nodes Matter: Towards Structure Fairness in Graphs
Xiaotian Han, Kaixiong Zhou, Ting-Hsiang Wang +3
In social network, a person located at the periphery region (marginal node) is likely to be treated unfairly when compared with the persons at the center. While existing fairness w…
CODA: Temporal Domain Generalization via Concept Drift Simulator
Chia-Yuan Chang, Yu-Neng Chuang, Zhimeng Jiang +3
In real-world applications, machine learning models often become obsolete due to shifts in the joint distribution arising from underlying temporal trends, a phenomenon known as the…
Beyond Fairness: Age-Harmless Parkinson's Detection via Voice
Yicheng Wang, Xiaotian Han, Leisheng Yu +1
Parkinson's disease (PD), a neurodegenerative disorder, often manifests as speech and voice dysfunction. While utilizing voice data for PD detection has great potential in clinical…
Towards Assumption-free Bias Mitigation
Chia-Yuan Chang, Yu-Neng Chuang, Kwei-Herng Lai +3
Despite the impressive prediction ability, machine learning models show discrimination towards certain demographics and suffer from unfair prediction behaviors. To alleviate the di…
Graph Mixup with Soft Alignments
Hongyi Ling, Zhimeng Jiang, Meng Liu +2
We study graph data augmentation by mixup, which has been used successfully on images. A key operation of mixup is to compute a convex combination of a pair of inputs. This operati…
FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods
Xiaotian Han, Jianfeng Chi, Yu Chen +4
This paper introduces the Fair Fairness Benchmark (\textsf{FFB}), a benchmarking framework for in-processing group fairness methods. Ensuring fairness in machine learning is import…
PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data
Shenghan Zhang, Haoxuan Li, Ruixiang Tang +5
Detailed phenotype information is fundamental to accurate diagnosis and risk estimation of diseases. As a rich source of phenotype information, electronic health records (EHRs) pro…
Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant
Sirui Ding, Qiaoyu Tan, Chia-yuan Chang +5
Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure. Analyzing the post-transplant cause of death (CoD) after organ transplant pro…
Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint
Chia-Yuan Chang, Jiayi Yuan, Sirui Ding +5
Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To…
Understanding and Unifying Fourteen Attribution Methods with Taylor Interactions
Huiqi Deng, Na Zou, Mengnan Du +5
Various attribution methods have been developed to explain deep neural networks (DNNs) by inferring the attribution/importance/contribution score of each input variable to the fina…
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…
RELIANT: Fair Knowledge Distillation for Graph Neural Networks
Yushun Dong, Binchi Zhang, Yiling Yuan +3
Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameter…
Retiring DP: New Distribution-Level Metrics for Demographic Parity
Xiaotian Han, Zhimeng Jiang, Hongye Jin +4
Demographic parity is the most widely recognized measure of group fairness in machine learning, which ensures equal treatment of different demographic groups. Numerous works aim to…
Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning
Daochen Zha, Kwei-Herng Lai, Qiaoyu Tan +3
Imbalanced learning is a fundamental challenge in data mining, where there is a disproportionate ratio of training samples in each class. Over-sampling is an effective technique to…
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…
Fair Machine Learning in Healthcare: A Review
Qizhang Feng, Mengnan Du, Na Zou +1
The digitization of healthcare data coupled with advances in computational capabilities has propelled the adoption of machine learning (ML) in healthcare. However, these methods ca…
FMP: Toward Fair Graph Message Passing against Topology Bias
Zhimeng Jiang, Xiaotian Han, Chao Fan +4
Despite recent advances in achieving fair representations and predictions through regularization, adversarial debiasing, and contrastive learning in graph neural networks (GNNs), t…