activity
20172024
most citedLarge Language Models Meet User Interfaces: The Case of Provisioning Feedback

3 citations · 4 across the 4 of their papers we have counts for

collaborators

7 papers

cs.LG2024

Fairness without Sensitive Attributes via Knowledge Sharing

Hongliang Ni, Lei Han, Tong Chen +2

While model fairness improvement has been explored previously, existing methods invariably rely on adjusting explicit sensitive attribute values in order to improve model fairness…

cs.HC20243 cited

Large Language Models Meet User Interfaces: The Case of Provisioning Feedback

Stanislav Pozdniakov, Jonathan Brazil, Solmaz Abdi +5

Incorporating Generative AI (GenAI) and Large Language Models (LLMs) in education can enhance teaching efficiency and enrich student learning. Current LLM usage involves conversati…

cs.IR20241 cited

Poisoning Attacks against Recommender Systems: A Survey

Zongwei Wang, Min Gao, Junliang Yu +3

Modern recommender systems (RS) have seen substantial success, yet they remain vulnerable to malicious activities, notably poisoning attacks. These attacks involve injecting malici…

cs.CL20241 cited

Identification of Regulatory Requirements Relevant to Business Processes: A Comparative Study on Generative AI, Embedding-based Ranking, Crowd and Expert-driven Methods

Catherine Sai, Shazia Sadiq, Lei Han +2

Organizations face the challenge of ensuring compliance with an increasing amount of requirements from various regulatory documents. Which requirements are relevant depends on aspe…

cs.DL20231 cited

Leveraging Artificial Intelligence Technology for Mapping Research to Sustainable Development Goals: A Case Study

Hui Yin, Amir Aryani, Gavin Lambert +7

The number of publications related to the Sustainable Development Goals (SDGs) continues to grow. These publications cover a diverse spectrum of research, from humanities and socia…

cs.LG2023

To Predict or to Reject: Causal Effect Estimation with Uncertainty on Networked Data

Hechuan Wen, Tong Chen, Li Kheng Chai +3

Due to the imbalanced nature of networked observational data, the causal effect predictions for some individuals can severely violate the positivity/overlap assumption, rendering u…