Xiangyang Li

Dataset Bias in Few-shot Image Recognition

2020 · arXiv (Cornell University)

The goal of few-shot image recognition (FSIR) is to identify novel categories with a small number of annotated samples by exploiting transferable knowledge from training data (base categories). Most current studies assume that the transferable …

Rethinking Dense Retrieval's Few-Shot Ability

2023 · arXiv (Cornell University)

Few-shot dense retrieval (DR) aims to effectively generalize to novel search scenarios by learning a few samples. Despite its importance, there is little study on specialized datasets and standardized evaluation protocols. As a result, current …

LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations

2024 · arXiv (Cornell University)

The lack of training data gives rise to the system cold-start problem in recommendation systems, making them struggle to provide effective recommendations. To address this problem, Large Language Models (LLMs) can model recommendation tasks as …

Bayesian networks based on differential privacy for financial data privacy

2025

As the demand for privacy protection continues to grow, ensuring effective financial data processing while preventing sensitive information leakage has become a pressing issue in the financial industry and related research fields. We propose a …

SampleLLM: Optimizing Tabular Data Synthesis in Recommendations

2025

Tabular data synthesis is crucial in machine learning, yet existing general methods-primarily based on statistical or deep learning models-are highly data-dependent and often fall short in recommender systems. This limitation arises from their difficulty in …