5 citations · 7 across the 12 of their papers we have counts for
5 papers · 1 filter
SocraticPO: Policy Optimization via Interactive Guidance
Zirui Liu, Tingyue Pan, Jie Ouyang +8
Reinforcement learning (RL) for large language models usually supervises reasoning with scalar outcome rewards, such as binary correctness. Such rewards provide an optimization dir…
StaTS: Spectral Trajectory Schedule Learning for Adaptive Time Series Forecasting with Frequency Guided Denoiser
Jintao Zhang, Zirui Liu, Mingyue Cheng +3
Diffusion models have been used for probabilistic time series forecasting and show strong potential. However, fixed noise schedules often produce intermediate states that are hard…
Towards Stable and Structured Time Series Generation with Perturbation-Aware Flow Matching
Jintao Zhang, Mingyue Cheng, Zirui Liu +3
Time series generation is critical for a wide range of applications, which greatly supports downstream analytical and decision-making tasks. However, the inherent temporal heteroge…
Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching
Huibo Xu, Runlong Yu, Likang Wu +2
Existing generative models for time series forecasting often transform simple priors (typically Gaussian) into complex data distributions. However, their sampling initialization, i…
NeuTSFlow: Modeling Continuous Functions Behind Time Series Forecasting
Huibo Xu, Likang Wu, Xianquan Wang +4
Time series forecasting is a fundamental task with broad applications, yet conventional methods often treat data as discrete sequences, overlooking their origin as noisy samples of…