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Found 6 papers, 0 papers with code
Latent Schr{ö}dinger Bridge Diffusion Model for Generative Learning
Our theoretical analysis encompasses the establishment of end-to-end error analysis for learning distributions via the latent Schr{\"o}dinger bridge diffusion model.
An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning models
In our empirical study on the MIMIC-III dataset, we show that the two core explanations - SHAP values and variable rankings fluctuate when using different background datasets acquired from random sampling, indicating that users cannot unquestioningly trust the one-shot interpretation from SHAP.
A Data-Driven Line Search Rule for Support Recovery in High-dimensional Data Analysis
In this work, we consider the algorithm to the (nonlinear) regression problems with $\ell_0$ penalty.
Convergence Analysis of Schr{ö}dinger-F{ö}llmer Sampler without Convexity
Schr\"{o}dinger-F\"{o}llmer sampler (SFS) is a novel and efficient approach for sampling from possibly unnormalized distributions without ergodicity.
On Newton Screening
Based on this KKT system, a built-in working set with a relatively small size is first determined using the sum of primal and dual variables generated from the previous iteration, then the primal variable is updated by solving a least-squares problem on the working set and the dual variable updated based on a closed-form expression.
A Support Detection and Root Finding Approach for Learning High-dimensional Generalized Linear Models
Feature selection is important for modeling high-dimensional data, where the number of variables can be much larger than the sample size.
