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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 681–690 of 2050 papers

TitleStatusHype
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits—0
Uni-QSAR: an Auto-ML Tool for Molecular Property PredictionCode3
Sparse Private LASSO Logistic Regression—0
Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022—0
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs—0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Efficient Deep Reinforcement Learning Requires Regulating Overfitting—0
An Offline Metric for the Debiasedness of Click ModelsCode0
An XAI framework for robust and transparent data-driven wind turbine power curve modelsCode1
Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings—0
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