activity
20242026
most citedParamReL: Learning Parameter Space Representation via Progressively Encoding Bayesian Flow Networks

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

collaborators

5 papers

cs.CL2026

DKCD: Domain Knowledge-Enhanced Causal Discovery from Unstructured Data

Xin Li, Jin Li, Shoujin Wang +2

Causal discovery from unstructured data is a challenging yet underexplored task in high-expertise domains such as healthcare, finance, and education. Existing methods typically lev…

cs.LG2025

Revealing Multimodal Causality with Large Language Models

Jin Li, Shoujin Wang, Qi Zhang +5

Uncovering cause-and-effect mechanisms from data is fundamental to scientific progress. While large language models (LLMs) show promise for enhancing causal discovery (CD) from uns…

cs.IR2025

Generating with Fairness: A Modality-Diffused Counterfactual Framework for Incomplete Multimodal Recommendations

Jin Li, Shoujin Wang, Qi Zhang +2

Incomplete scenario is a prevalent, practical, yet challenging setting in Multimodal Recommendations (MMRec), where some item modalities are missing due to various factors. Recentl…

cs.LG2024★ 2 cited

ParamReL: Learning Parameter Space Representation via Progressively Encoding Bayesian Flow Networks

Zhangkai Wu, Xuhui Fan, Jin Li +3

The recently proposed Bayesian Flow Networks~(BFNs) show great potential in modeling parameter spaces, offering a unified strategy for handling continuous, discretized, and discret…

cs.IR2024★ 1 cited

Causal Learning for Trustworthy Recommender Systems: A Survey

Jin Li, Shoujin Wang, Qi Zhang +5

Recommender Systems (RS) have significantly advanced online content filtering and personalized decision-making. However, emerging vulnerabilities in RS have catalyzed a paradigm sh…