6 papers
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…
Deep Time-series Forecasting Needs Kernelized Moment Balancing
Licheng Pan, Hao Wang, Haocheng Yang +7
Deep time-series forecasting can be formulated as a distribution balancing problem aimed at aligning the distribution of the forecasts and ground truths. According to Imbens' crite…
Relaxing Probabilistic Latent Variable Models' Specification via Infinite-Horizon Optimal Control
Zhichao Chen, Hao Wang, Licheng Pan +6
In this paper, we address the issue of model specification in probabilistic latent variable models (PLVMs) using an infinite-horizon optimal control approach. Traditional PLVMs rel…
Mixture of Low Rank Adaptation with Partial Parameter Sharing for Time Series Forecasting
Licheng Pan, Zhichao Chen, Haoxuan Li +5
Multi-task forecasting has become the standard approach for time-series forecasting (TSF). However, we show that it suffers from an Expressiveness Bottleneck, where predictions at…
Understanding and Mitigating Overrefusal in LLMs from an Unveiling Perspective of Safety Decision Boundary
Licheng Pan, Yongqi Tong, Xin Zhang +3
Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet they often refuse to answer legitimate queries--a phenomenon known as overr…
DeepFilter: A Transformer-style Framework for Accurate and Efficient Process Monitoring
Hao Wang, Zhichao Chen, Licheng Pan +4
The process monitoring task is characterized by stringent demands for accuracy and efficiency. Current transformer-based methods, characterized by self-attention for temporal fusio…