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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 226–250 of 2050 papers

TitleStatusHype
A ModelOps-based Framework for Intelligent Medical Knowledge Extraction—0
SpiKernel: A Kernel Size Exploration Methodology for Improving Accuracy of the Embedded Spiking Neural Network Systems—0
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning—0
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs—0
A Two-step Metropolis Hastings Method for Bayesian Empirical Likelihood Computation with Application to Bayesian Model Selection—0
A Meta-learning based Distribution System Load Forecasting Model Selection Framework—0
A Tractable Fully Bayesian Method for the Stochastic Block Model—0
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits—0
IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting—0
A Unified Approach to Routing and Cascading for LLMs—0
A Unified Dynamic Approach to Sparse Model Selection—0
A Unified Framework for Tuning Hyperparameters in Clustering Problems—0
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation—0
AutoAI-TS: AutoAI for Time Series Forecasting—0
Bayesian Model Selection for Identifying Markov Equivalent Causal Graphs—0
AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
Bayesian Model Selection Methods for Mutual and Symmetric k-Nearest Neighbor Classification—0
A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays with Subarray Sampling—0
Automated Model Selection for Generalized Linear Models—0
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach—0
Automated Model Selection for Time-Series Anomaly Detection—0
Automated Model Selection with Bayesian Quadrature—0
Adaptive Bayesian Linear Regression for Automated Machine Learning—0
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data—0
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