5 papers · 1 filter
Retrieval-Based In-Context Learning: A Domain Adaptation Framework
Yilun Zhu, Naihao Deng, Yingcong Li +2
In-context retrieval (ICR) is a retrieval-based form of in-context learning (ICL) in which demonstrations are retrieved from a source database based on similarity to the query, rat…
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…
Scalable Mean-Field Variational Inference via Preconditioned Primal-Dual Optimization
Jinhua Lyu, Tianmin Yu, Ying Ma +1
In this work, we investigate the large-scale mean-field variational inference (MFVI) problem from a mini-batch primal-dual perspective. By reformulating MFVI as a constrained finit…
Coupled Flow Matching
Wenxi Cai, Yuheng Wang, Naichen Shi
We introduce Coupled Flow Matching (CPFM), a framework that integrates controllable dimensionality reduction and high-fidelity reconstruction. CPFM learns coupled continuous flows…
Calibrated Principal Component Regression
Yixuan Florence Wu, Yilun Zhu, Lei Cao +1
We propose a new method for statistical inference in generalized linear models. In the overparameterized regime, Principal Component Regression (PCR) reduces variance by projecting…