6 citations · 6 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
TSI-Bench: Benchmarking Time Series Imputation
Wenjie Du, Jun Wang, Linglong Qian +12
Effective imputation is a crucial preprocessing step for time series analysis. Despite the development of numerous deep learning algorithms for time series imputation, the communit…