activity
20242026
collaborators

8 papers

cs.LG2026

Slack More, Predict Better: Proximal Relaxation for Probabilistic Latent Variable Model-based Soft Sensors

Zehua Zou, Yiran Ma, Yulong Zhang +5

Nonlinear Probabilistic Latent Variable Models (NPLVMs) are a cornerstone of soft sensor modeling due to their capacity for uncertainty delineation. However, conventional NPLVMs ar…

cs.CV2026

Towards Stable Self-Supervised Object Representations in Unconstrained Egocentric Video

Yuting Tan, Xilong Cheng, Yunxiao Qin +2

Humans develop visual intelligence through perceiving and interacting with their environment - a self-supervised learning process grounded in egocentric experience. Inspired by thi…

cs.LG2026

Analyzing and Improving Diffusion Models for Time-Series Data Imputation: A Proximal Recursion Perspective

Zhichao Chen, Hao Wang, Fangyikang Wang +5

Diffusion models (DMs) have shown promise for Time-Series Data Imputation (TSDI); however, their performance remains inconsistent in complex scenarios. We attribute this to two pri…

cs.LG2026

Rethinking the Flow-Based Gradual Domain Adaptation: A Semi-Dual Optimal Transport Perspective

Zhichao Chen, Zhan Zhuang, Yunfei Teng +6

Gradual domain adaptation (GDA) aims to mitigate domain shift by progressively adapting models from the source domain to the target domain via intermediate domains. However, real i…

cs.CL2025

PsyMem: Fine-grained psychological alignment and Explicit Memory Control for Advanced Role-Playing LLMs

Xilong Cheng, Yunxiao Qin, Yuting Tan +4

Existing LLM-based role-playing methods often rely on superficial textual descriptions or simplistic metrics, inadequately modeling both intrinsic and extrinsic character dimension…

cs.HC2025

A Review of Behavioral Closed-Loop Paradigm from Sensing to Intervention for Ingestion Health

Jun Fang, Yanuo Zhou, Ka I Chan +8

Ingestive behavior plays a critical role in health, yet many existing interventions remain limited to static guidance or manual self-tracking. With the increasing integration of se…