4 papers
Feature Priming in Online Linear Regression: Sparse-Regret Lower Bounds and a Tight Univariate Rate
Huibo Xu, Shi Fu, Qixin Zhang +1
In high-dimensional online prediction, the best predictor may depend on only a few features, so regret should scale with sparsity rather than the ambient dimension. Feature priming…
From Entity Reliability to Clean Feedback: An Entity-Aware Denoising Framework Beyond Interaction-Level Signals
Ze Liu, Xianquan Wang, Shuochen Liu +5
Implicit feedback is central to modern recommender systems but is inherently noisy, often impairing model training and degrading user experience. At scale, such noise can mislead l…
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…