5 papers
From Observations to States: Latent Time Series Forecasting
Jie Yang, Yifan Hu, Yuante Li +3
Deep learning has achieved strong performance in Time Series Forecasting (TSF). However, we identify a critical representation paradox, termed Latent Chaos: models with accurate pr…
Glocal Information Bottleneck for Time Series Imputation
Jie Yang, Kexin Zhang, Guibin Zhang +2
Time Series Imputation (TSI), which aims to recover missing values in temporal data, remains a fundamental challenge due to the complex and often high-rate missingness in real-worl…
Population-Aligned Persona Generation for LLM-based Social Simulation
Zhengyu Hu, Jianxun Lian, Zheyuan Xiao +7
Recent advances in large language models (LLMs) have enabled human-like social simulations at unprecedented scale and fidelity, offering new opportunities for computational social…
POLO: Preference-Guided Multi-Turn Reinforcement Learning for Lead Optimization
Ziqing Wang, Yibo Wen, William Pattie +6
Lead optimization in drug discovery requires efficiently navigating vast chemical space through iterative cycles to enhance molecular properties while preserving structural similar…
Cross-Domain Conditional Diffusion Models for Time Series Imputation
Kexin Zhang, Baoyu Jing, K. Selçuk Candan +4
Cross-domain time series imputation is an underexplored data-centric research task that presents significant challenges, particularly when the target domain suffers from high missi…