activity
20242026
collaborators

6 papers

math.CO2026

Intersecting families of loose spanning hypertrees

Yury Person, Thomas Schweser

A loose hypertree is a hypergraph whose incidence graph is a tree. Let , , and . We determine the largest -intersecting families of lo…

cs.IR2025

Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering

Yu Zhang, Yiwen Zhang, Yi Zhang +2

Personalized recommendation is widely used in the web applications, and graph contrastive learning (GCL) has gradually become a dominant approach in recommender systems, primarily…

stat.ML2025

Testing Conditional Mean Independence Using Generative Neural Networks

Yi Zhang, Linjun Huang, Yun Yang +1

Conditional mean independence (CMI) testing is crucial for statistical tasks including model determination and variable importance evaluation. In this work, we introduce a novel po…

stat.ME2024

Doubly Robust Conditional Independence Testing with Generative Neural Networks

Yi Zhang, Linjun Huang, Yun Yang +1

This article addresses the problem of testing the conditional independence of two generic random vectors and given a third random vector , which plays an important role…

cs.IR2024

Adaptive Fusion Self-supervised Learning for Recommendation

Yu Zhang, Lei Sang, Yi Zhang +2

Self-supervised learning (SSL) has recently attracted significant attention in the field of recommender systems. Contrastive learning (CL) stands out as a major SSL paradigm due to…

cs.IR2024

TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation

Honghao Li, Qiuze Ru, Yiwen Zhang +3

Effective feature interaction modeling is critical for enhancing the accuracy of click-through rate (CTR) prediction in industrial recommender systems. Most of the current deep CTR…