4 citations · 16 across the 19 of their papers we have counts for
10 papers · 1 filter
Towards Unveiling Vulnerabilities of Large Reasoning Models in Machine Unlearning
Aobo Chen, Chenxu Zhao, Chenglin Miao +1
Large language models (LLMs) possess strong semantic understanding, driving significant progress in data mining applications. This is further enhanced by large reasoning models (LR…
Uncertainty-aware Language Guidance for Concept Bottleneck Models
Yangyi Li, Mengdi Huai
Concept Bottleneck Models (CBMs) provide inherent interpretability by first mapping input samples to high-level semantic concepts, followed by a combination of these concepts for t…
Towards Benchmarking Privacy Vulnerabilities in Selective Forgetting with Large Language Models
Wei Qian, Chenxu Zhao, Yangyi Li +1
The rapid advancements in artificial intelligence (AI) have primarily focused on the process of learning from data to acquire knowledgeable learning systems. As these systems are i…
Towards Unveiling Predictive Uncertainty Vulnerabilities in the Context of the Right to Be Forgotten
Wei Qian, Chenxu Zhao, Yangyi Li +2
Currently, various uncertainty quantification methods have been proposed to provide certainty and probability estimates for deep learning models' label predictions. Meanwhile, with…
Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models
Yuan Zhong, Xiaochen Wang, Jiaqi Wang +5
Synthesizing electronic health records (EHR) data has become a preferred strategy to address data scarcity, improve data quality, and model fairness in healthcare. However, existin…
Exploring Fairness in Educational Data Mining in the Context of the Right to be Forgotten
Wei Qian, Aobo Chen, Chenxu Zhao +2
In education data mining (EDM) communities, machine learning has achieved remarkable success in discovering patterns and structures to tackle educational challenges. Notably, fairn…
Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction
Wei Qian, Chenxu Zhao, Yangyi Li +3
Despite the recent progress in deep neural networks (DNNs), it remains challenging to explain the predictions made by DNNs. Existing explanation methods for DNNs mainly focus on po…
MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation
Yuan Zhong, Suhan Cui, Jiaqi Wang +7
Health risk prediction is one of the fundamental tasks under predictive modeling in the medical domain, which aims to forecast the potential health risks that patients may face in…
Inductive Graph Unlearning
Cheng-Long Wang, Mengdi Huai, Di Wang
As a way to implement the "right to be forgotten" in machine learning, \textit{machine unlearning} aims to completely remove the contributions and information of the samples to be…
Understanding and Enhancing Robustness of Concept-based Models
Sanchit Sinha, Mengdi Huai, Jianhui Sun +1
Rising usage of deep neural networks to perform decision making in critical applications like medical diagnosis and financial analysis have raised concerns regarding their reliabil…