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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 51–60 of 2050 papers

TitleStatusHype
Thompson Sampling-like Algorithms for Stochastic Rising Bandits—0
IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting—0
MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model SelectionCode0
A Systematic Analysis of Base Model Choice for Reward Modeling—0
Supervised Models Can Generalize Also When Trained on Random LabelCode0
PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework—0
Reassessing Large Language Model Boolean Query Generation for Systematic Reviews—0
Tuning for Trustworthiness -- Balancing Performance and Explanation Consistency in Neural Network Optimization—0
Uncertainty Profiles for LLMs: Uncertainty Source Decomposition and Adaptive Model-Metric Selection—0
A Sparse Bayesian Learning Algorithm for Estimation of Interaction Kernels in Motsch-Tadmor Model—0
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