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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 1301–1325 of 2050 papers

TitleStatusHype
Inferring Latent dimension of Linear Dynamical System with Minimum Description Length—0
Inferring Network Structure From Data—0
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes—0
Information-based inference for singular models and finite sample sizes: A frequentist information criterion—0
Information criteria for non-normalized models—0
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization—0
Informative Bayesian model selection for RR Lyrae star classifiers—0
Instruction-Guided Autoregressive Neural Network Parameter Generation—0
Interactive Visual Exploration of Latent Space (IVELS) for peptide auto-encoder model selection—0
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features—0
Interpretability in Linear Brain Decoding—0
Interpretable Machine Learning for Discovery: Statistical Challenges \& Opportunities—0
Interpretable Machine Learning for Self-Service High-Risk Decision-Making—0
Interpretable Models for Understanding Immersive Simulations—0
Interpreting and predicting the economy flows: A time-varying parameter global vector autoregressive integrated the machine learning model—0
MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks—0
Inverse Moment Methods for Sufficient Forecasting using High-Dimensional Predictors—0
Model Selection for Inverse Reinforcement Learning via Structural Risk Minimization—0
Is F_1 Score Suboptimal for Cybersecurity Models? Introducing C_score, a Cost-Aware Alternative for Model Assessment—0
Ising Model Selection Using _1-Regularized Linear Regression: A Statistical Mechanics Analysis—0
Is it worth it? Budget-related evaluation metrics for model selection—0
Is K-fold cross validation the best model selection method for Machine Learning?—0
IW-GAE: Importance Weighted Group Accuracy Estimation for Improved Calibration and Model Selection in Unsupervised Domain Adaptation—0
Joint Continuous and Discrete Model Selection via Submodularity—0
JULIA: Joint Multi-linear and Nonlinear Identification for Tensor Completion—0
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