8 citations · 19 across the 10 of their papers we have counts for
10 papers
When LLMs Imagine People: A Human-Centered Persona Brainstorm Audit for Bias and Fairness in Creative Applications
Hongliu Cao, Eoin Thomas, Rodrigo Acuna Agost
Large Language Models (LLMs) used in creative workflows can reinforce stereotypes and perpetuate inequities, making fairness auditing essential. Existing methods rely on constraine…
Diverse And Private Synthetic Datasets Generation for RAG evaluation: A multi-agent framework
Ilias Driouich, Hongliu Cao, Eoin Thomas
Retrieval-augmented generation (RAG) systems improve large language model outputs by incorporating external knowledge, enabling more informed and context-aware responses. However,…
Multi-Agent LLM Judge: automatic personalized LLM judge design for evaluating natural language generation applications
Hongliu Cao, Ilias Driouich, Robin Singh +1
Large Language Models (LLMs) have demonstrated impressive performance across diverse domains, yet they still encounter challenges such as insufficient domain-specific knowledge, bi…
Inclusive normalization of face images to passport format
Hongliu Cao, Minh Nhat Do, Alexis Ravanel +1
Face recognition has been used more and more in real world applications in recent years. However, when the skin color bias is coupled with intra-personal variations like harsh illu…
Multi-view user representation learning for user matching without personal information
Hongliu Cao, Ilias El Baamrani, Eoin Thomas
As the digitization of travel industry accelerates, analyzing and understanding travelers' behaviors becomes increasingly important. However, traveler data frequently exhibit high…
Destination similarity based on implicit user interest
Hongliu Cao, Eoin Thomas
With the digitization of travel industry, it is more and more important to understand users from their online behaviors. However, online travel industry data are more challenging t…
A Novel Random Forest Dissimilarity Measure for Multi-View Learning
Hongliu Cao, Simon Bernard, Robert Sabourin +1
Multi-view learning is a learning task in which data is described by several concurrent representations. Its main challenge is most often to exploit the complementarities between t…
Dynamic voting in multi-view learning for radiomics applications
Hongliu Cao, Simon Bernard, Laurent Heutte +1
Cancer diagnosis and treatment often require a personalized analysis for each patient nowadays, due to the heterogeneity among the different types of tumor and among patients. Radi…
Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images
Hongliu Cao, Simon Bernard, Laurent Heutte +1
Breast cancer is one of the most common types of cancer and leading cancer-related death causes for women. In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology I…
Dissimilarity-based representation for radiomics applications
Hongliu Cao, Simon Bernard, Laurent Heutte +1
Radiomics is a term which refers to the analysis of the large amount of quantitative tumor features extracted from medical images to find useful predictive, diagnostic or prognosti…