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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 1076–1100 of 2050 papers

TitleStatusHype
Ranking & Reweighting Improves Group Distributional Robustness—0
Rate-adaptive model selection over a collection of black-box contextual bandit algorithms—0
Rational kernel-based interpolation for complex-valued frequency response functions—0
Rational-WENO: A lightweight, physically-consistent three-point weighted essentially non-oscillatory scheme—0
Realistic Evaluation of Deep Partial-Label Learning Algorithms—0
Realistic Evaluation of Test-Time Adaptation Algorithms: Unsupervised Hyperparameter Selection—0
Real-time Monocular Depth Estimation with Sparse Supervision on Mobile—0
Reassessing Large Language Model Boolean Query Generation for Systematic Reviews—0
Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood—0
Recommending Pre-Trained Models for IoT Devices—0
Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence—0
Reduced-order modeling using Dynamic Mode Decomposition and Least Angle Regression—0
ReeM: Ensemble Building Thermodynamics Model for Efficient HVAC Control via Hierarchical Reinforcement Learning—0
Reframing Neural Networks: Deep Structure in Overcomplete Representations—0
Region Detection in Markov Random Fields: Gaussian Case—0
Regret Balancing for Bandit and RL Model Selection—0
Regret Bound Balancing and Elimination for Model Selection in Bandits and RL—0
Regularity Normalization: Constraining Implicit Space with Minimum Description Length—0
Regularization for Cox's proportional hazards model with NP-dimensionality—0
Regularized Bilinear Discriminant Analysis for Multivariate Time Series Data—0
Regularized DeepIV with Model Selection—0
Regularized Modal Regression with Applications in Cognitive Impairment Prediction—0
Reinforcement Learning based Dynamic Model Selection for Short-Term Load Forecasting—0
Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting—0
Reinforcement Learning for Machine Learning Model Deployment: Evaluating Multi-Armed Bandits in ML Ops Environments—0
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