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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 1326–1350 of 2050 papers

TitleStatusHype
Regret Balancing for Bandit and RL Model Selection—0
Multi-split Optimized Bagging Ensemble Model Selection for Multi-class Educational Data Mining—0
Virtual Reference Feedback Tuning with data-driven reference model selection—0
Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach—0
Speedy Performance Estimation for Neural Architecture SearchCode0
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective—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
Fuzzy c-Means Clustering for Persistence DiagramsCode1
DGSAC: Density Guided Sampling and Consensus—0
Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction—0
Variational Inference and Learning of Piecewise-linear Dynamical Systems—0
Learning Opinion Dynamics From Social TracesCode1
Unsupervised Discretization by Two-dimensional MDL-based HistogramCode0
Uncertainty Based Camera Model SelectionCode1
Quantized Neural Networks: Characterization and Holistic Optimization—0
Sig-SDEs model for quantitative finance—0
Solution Path Algorithm for Twin Multi-class Support Vector MachineCode0
Revealing consensus and dissensus between network partitions—0
Selective Inference for Latent Block Models—0
Global sensitivity analysis informed model reduction and selection applied to a Valsalva maneuver model—0
Learning Equations from Biological Data with Limited Time SamplesCode0
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law—0
Marginal likelihood computation for model selection and hypothesis testing: an extensive review—0
Forward utilities and Mean-field games under relative performance concerns—0
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