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Found 4 papers, 3 papers with code
Learning Robust Statistics for Simulation-based Inference under Model Misspecification
Simulation-based inference (SBI) methods such as approximate Bayesian computation (ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating statistics to infer parameters of intractable likelihood models.
Minimal Learning Machine for Multi-Label Learning
Distance-based supervised method, the minimal learning machine, constructs a predictive model from data by learning a mapping between input and output distance matrices.
Distill n' Explain: explaining graph neural networks using simple surrogates
We also propose FastDnX, a faster version of DnX that leverages the linear decomposition of our surrogate model.
Neural Network Out-of-Distribution Detection for Regression Tasks
Neural network out-of-distribution (OOD) detection aims to identify when a model is unable to generalize to new inputs, either due to covariate shift or anomalous data.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
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