activity
20242026
most citedPERSCEN: Learning Personalized Interaction Pattern and Scenario Preference for Multi-Scenario Matching

3 citations · 3 across the 7 of their papers we have counts for

collaborators

9 papers

cs.AI2026

PSPA-Bench: A Personalized Benchmark for Smartphone GUI Agent

Hongyi Nie, Xunyuan Liu, Yudong Bai +4

Smartphone GUI agents execute tasks by operating directly on app interfaces, offering a path to broad capability without deep system integration. However, real-world smartphone use…

cs.LG2026

DGNet: Discrete Green Networks for Data-Efficient Learning of Spatiotemporal PDEs

Yingjie Tan, Quanming Yao, Yaqing Wang

Spatiotemporal partial differential equations (PDEs) underpin a wide range of scientific and engineering applications. Neural PDE solvers offer a promising alternative to classical…

cs.LG2026

Self-Generative Adversarial Fine-Tuning for Large Language Models

Shiguang Wu, Yaqing Wang, Quanming Yao

Fine-tuning large language models (LLMs) for alignment typically relies on supervised fine-tuning or reinforcement learning from human feedback, both limited by the cost and scarci…

cs.AI2025

LLM-Empowered Representation Learning for Emerging Item Recommendation

Ziying Zhang, Quanming Yao, Yaqing Wang

In this work, we tackle the challenge of recommending emerging items, whose interactions gradually accumulate over time. Existing methods often overlook this dynamic process, typic…

cs.LG2025

Spectral Alignment as Predictor of Loss Explosion in Neural Network Training

Haiquan Qiu, You Wu, Yingjie Tan +2

Loss explosions in training deep neural networks can nullify multi-million dollar training runs. Conventional monitoring metrics like weight and gradient norms are often lagging an…

cs.LG2025

Attending on Multilevel Structure of Proteins enables Accurate Prediction of Cold-Start Drug-Target Interactions

Ziying Zhang, Yaqing Wang, Yuxuan Sun +2

Cold-start drug-target interaction (DTI) prediction focuses on interaction between novel drugs and proteins. Previous methods typically learn transferable interaction patterns betw…