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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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TitleStatusHype
Fusion Subspace Clustering for Incomplete Data—0
Fast Instrument Learning with Faster RatesCode0
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems—0
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures—0
Interpretable Machine Learning for Self-Service High-Risk Decision-Making—0
Differentially Private Generalized Linear Models Revisited—0
Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained ModelsCode0
Identification of Physical Processes and Unknown Parameters of 3D Groundwater Contaminant Problems via Theory-guided U-net—0
Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual TransferCode0
Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts—0
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