4 citations · 4 across the 9 of their papers we have counts for
9 papers
Curriculum-Aware Interpolate-then-Refine: Learned Physiological Time-Series Imputation under Realistic Missingness
Yu-Chao Huang, Haochen Zhang, Nicholas Konz +1
Imputing physiological time series (arterial blood pressure, blood glucose, etc.) is essential for addressing the missingness that pervades clinical data. Yet modern imputation met…
CAST: Context- and Anomaly Structure-Conditioned Time Series Anomaly Generation
Haochen Zhang, Jie Peng, Songyuan Sui +3
Anomalous time series play a critical role in safety-critical domains, yet they are inherently scarce, heterogeneous, and costly to obtain. Existing time series generation methods…
Represent, Then Generate: Multimodal-Conditioned Time-Series Generation under Irregular Missingness
Haochen Zhang, Jiaheng Guo, Yu-Chao Huang +2
Continuous physiological time series underpin modern clinical monitoring, yet many of the most informative signals are invasive, expensive, or simply unavailable for a given patien…
TRUST: A Framework for Decentralized AI Service v.0.1
Yu-Chao Huang, Zhen Tan, Mohan Zhang +3
Large Reasoning Models (LRMs) and Multi-Agent Systems (MAS) in high-stakes domains demand reliable verification, yet centralized approaches suffer four limitations: (1) Robustness,…
On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality
Jerry Yao-Chieh Hu, Weimin Wu, Yi-Chen Lee +3
We investigate the approximation and estimation rates of conditional diffusion transformers (DiTs) with classifier-free guidance. We present a comprehensive analysis for ``in-conte…
Test-Time Training with Quantum Auto-Encoder: From Distribution Shift to Noisy Quantum Circuits
Damien Jian, Yu-Chao Huang, Hsi-Sheng Goan
In this paper, we propose test-time training with the quantum auto-encoder (QTTT). QTTT adapts to (1) data distribution shifts between training and testing data and (2) quantum cir…
L2O-: Learning to Optimize Parameterized Quantum Circuits with Fubini-Study Metric Tensor
Yu-Chao Huang, Hsi-Sheng Goan
Before the advent of fault-tolerant quantum computers, variational quantum algorithms (VQAs) play a crucial role in noisy intermediate-scale quantum (NISQ) machines. Conventionally…
Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization
Yu-Min Tseng, Yu-Chao Huang, Teng-Yun Hsiao +4
The concept of persona, originally adopted in dialogue literature, has re-surged as a promising framework for tailoring large language models (LLMs) to specific context (e.g., pers…
BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model
Chenwei Xu, Yu-Chao Huang, Jerry Yao-Chieh Hu +4
We introduce the \textbf{B}i-Directional \textbf{S}parse \textbf{Hop}field Network (\textbf{BiSHop}), a novel end-to-end framework for deep tabular learning. BiSHop handles the two…