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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 811–820 of 2050 papers

TitleStatusHype
Strengthening structural baselines for graph classification using Local Topological ProfileCode0
Revealing Similar Semantics Inside CNNs: An Interpretable Concept-based Comparison of Feature Spaces—0
Limits of Model Selection under Transfer Learning—0
ALMERIA: Boosting pairwise molecular contrasts with scalable methods—0
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits—0
Sparse Private LASSO Logistic Regression—0
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs—0
Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022—0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Efficient Deep Reinforcement Learning Requires Regulating Overfitting—0
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