11 papers
Multimodal Alignment Through Joint Kernel Entropic Gromov--Wasserstein Optimal Transport
Yixuan Florence Wu, Yilun Zhu, Naichen Shi
We study the problem of aligning data from multiple modalities into a shared representation space, focusing on settings where strong pretrained unimodal encoders are available but…
Generative Bayesian Filtering for State Estimation
Lei Cao, Sihang Feng, Jixin Yan +2
The state of a dynamic system evolves over time, switching among several latent modes that govern its observable behavior. Filtering methods infer the latent state from observation…
It Takes One to Bias Them All: Breaking Bad with One-Shot GRPO
Naihao Deng, Yilun Zhu, Naichen Shi +2
Warning: This paper contains several toxic and offensive statements. Modern large language models (LLMs) are typically aligned through large-scale post-training to ensure fair and…
LMT: A Bayesian Framework for Causal Discovery from Textual Alarm Records in Manufacturing Systems
Xiaofeng Xiao, Jianhong Chen, Qiuzhuang Sun +2
Textual event records, such as alarm logs, have become an increasingly common data source in engineering and manufacturing systems. Beyond identifying correlations or recurring pat…
SURGE: Approximation and Training Free Particle Filter for Diffusion Surrogate
Lifu Wei, Yinuo Ren, Naichen Shi +1
Data assimilation (DA) addresses the problem of sequentially estimating the state of a dynamical system from noisy and incomplete observations. In this work, we employ a diffusion…
Causal Discovery from Heteroscedastic Stochastic Dynamical Systems under Imperfect Physical Models
Jianhong Chen, Naichen Shi, Xubo Yue
Causal discovery is a data-driven paradigm for analyzing complex systems, while physics-based models, such as ordinary differential equations (ODEs), provide mechanistic structure…