1 citations · 1 across the 6 of their papers we have counts for
7 papers
MIRA: Medical Time Series Foundation Model for Real-World Health Data
Hao Li, Bowen Deng, Chang Xu +8
A unified foundation model for medical time series -- pretrained on open access and ethics board-approved medical corpora -- offers the potential to reduce annotation burdens, mini…
BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modeling
Hao Li, Yu-Hao Huang, Chang Xu +5
Time-series Generation (TSG) is a prominent research area with broad applications in simulations, data augmentation, and counterfactual analysis. While existing methods have shown…
TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts
Yu-Hao Huang, Chang Xu, Yueying Wu +2
Time series generation models are crucial for applications like data augmentation and privacy preservation. Most existing time series generation models are typically designed to ge…
InvDiff: Invariant Guidance for Bias Mitigation in Diffusion Models
Min Hou, Yueying Wu, Chang Xu +4
As one of the most successful generative models, diffusion models have demonstrated remarkable efficacy in synthesizing high-quality images. These models learn the underlying high-…
Controllable Financial Market Generation with Diffusion Guided Meta Agent
Yu-Hao Huang, Chang Xu, Yang Liu +3
Generative modeling has transformed many fields, such as language and visual modeling, while its application in financial markets remains under-explored. As the minimal unit within…
NumLLM: Numeric-Sensitive Large Language Model for Chinese Finance
Huan-Yi Su, Ke Wu, Yu-Hao Huang +1
Recently, many works have proposed various financial large language models (FinLLMs) by pre-training from scratch or fine-tuning open-sourced LLMs on financial corpora. However, ex…
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process
Xinyao Fan, Yueying Wu, Chang Xu +3
Recently, diffusion probabilistic models have attracted attention in generative time series forecasting due to their remarkable capacity to generate high-fidelity samples. However,…