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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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Showing 1361–1370 of 2050 papers

TitleStatusHype
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective—0
Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach—0
Virtual Reference Feedback Tuning with data-driven reference model selection—0
Rate-adaptive model selection over a collection of black-box contextual bandit algorithms—0
Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits—0
DGSAC: Density Guided Sampling and Consensus—0
Unsupervised Discretization by Two-dimensional MDL-based HistogramCode0
Variational Inference and Learning of Piecewise-linear Dynamical Systems—0
Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction—0
Quantized Neural Networks: Characterization and Holistic Optimization—0
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