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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 1701–1725 of 2050 papers

TitleStatusHype
Scalable Ensemble Diversification for OOD Generalization and DetectionCode0
Conformal online model aggregationCode0
Parsimonious Bayesian deep networksCode0
FedScore: A privacy-preserving framework for federated scoring system developmentCode0
Scalable Marginal Likelihood Estimation for Model Selection in Deep LearningCode0
fETSmcs: Feature-based ETS model component selectionCode0
Subject-driven Text-to-Image Generation via Preference-based Reinforcement LearningCode0
Parsimony-Enhanced Sparse Bayesian Learning for Robust Discovery of Partial Differential EquationsCode0
FiCo-ITR: bridging fine-grained and coarse-grained image-text retrieval for comparative performance analysisCode0
FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithmsCode0
Finding the Homology of Decision Boundaries with Active LearningCode0
Partially Hidden Markov Chain Linear Autoregressive model: inference and forecastingCode0
Valid causal inference with unobserved confounding in high-dimensional settingsCode0
Finite Mixtures of Multivariate Poisson-Log Normal Factor Analyzers for Clustering Count DataCode0
Effective Stabilized Self-Training on Few-Labeled Graph DataCode0
Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained ModelsCode0
Model selection and parameter inference in phylogenetics using Nested SamplingCode0
Unsupervised detection and fitness estimation of emerging SARS-CoV-2 variants. Application to wastewater samples (ANRS0160)Code0
Patched RTC: evaluating LLMs for diverse software development tasksCode0
Flexible, Non-parametric Modeling Using Regularized Neural NetworksCode0
SubStrat: A Subset-Based Strategy for Faster AutoMLCode0
AnyLoss: Transforming Classification Metrics into Loss FunctionsCode0
Dynamic Interpretability for Model Comparison via Decision RulesCode0
Unsupervised Discretization by Two-dimensional MDL-based HistogramCode0
CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing FlowsCode0
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