5 papers
Post-Training Augmentation Invariance
Keenan Eikenberry, Lizuo Liu, Yoonsang Lee
This work develops a framework for post-training augmentation invariance, in which our goal is to add invariance properties to a pretrained network without altering its behavior on…
Beyond Labels: Information-Efficient Human-in-the-Loop Learning using Ranking and Selection Queries
Belén MartÃn-Urcelay, Yoonsang Lee, Matthieu R. Bloch +1
Integrating human expertise into machine learning systems often reduces the role of experts to labeling oracles, a paradigm that limits the amount of information exchanged and fail…
An analysis of the derivative-free loss method for solving PDEs
Jihun Han, Yoonsang Lee
This study analyzes the derivative-free loss method to solve a certain class of elliptic PDEs and fluid problems using neural networks. The approach leverages the Feynman-Kac formu…
Structurally informed data assimilation in two dimensions
Tongtong Li, Anne Gelb, Yoonsang Lee
Accurate data assimilation (DA) for systems with piecewise-smooth or discontinuous state variables remains a significant challenge, as conventional covariance-based ensemble Kalman…
Nonlinear Bayesian Update via Ensemble Kernel Regression with Clustering and Subsampling
Yoonsang Lee
Nonlinear Bayesian update for a prior ensemble is proposed to extend traditional ensemble Kalman filtering to settings characterized by non-Gaussian priors and nonlinear measuremen…