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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 1801–1850 of 2050 papers

TitleStatusHype
Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model—0
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models—0
Detecting Signs of Model Change with Continuous Model Selection Based on Descriptive Dimensionality—0
Detection and Evaluation of Clusters within Sequential Data—0
Detection of intensity bursts using Hawkes processes: an application to high frequency financial data—0
Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables—0
Determination of Latent Dimensionality in International Trade Flow—0
Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling—0
DGSAC: Density Guided Sampling and Consensus—0
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach—0
Differential Description Length for Hyperparameter Selection in Machine Learning—0
Differentially Private Generalized Linear Models Revisited—0
Differentially Private Learning with Margin Guarantees—0
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks—0
DiffusionGPT: LLM-Driven Text-to-Image Generation System—0
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning—0
Dimensionality Dependent PAC-Bayes Margin Bound—0
Dimensionality Detection and Integration of Multiple Data Sources via the GP-LVM—0
Dimension-free Relaxation Times of Informed MCMC Samplers on Discrete Spaces—0
Dimension Independent Generalization Error by Stochastic Gradient Descent—0
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation—0
Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle—0
Dirichlet process mixture of Gaussian process functional regressions and its variational EM algorithm—0
Dirichlet Process Parsimonious Mixtures for clustering—0
Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound—0
Discriminative Clustering by Regularized Information Maximization—0
Disentangling Factors of Variations Using Few Labels—0
Disentangling Factors of Variation Using Few Labels—0
Distributed Bayesian Piecewise Sparse Linear Models—0
Distributed filtered hyperinterpolation for noisy data on the sphere—0
Distribution-free Deviation Bounds and The Role of Domain Knowledge in Learning via Model Selection with Cross-validation Risk Estimation—0
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization—0
Domain adaptation in practice: Lessons from a real-world information extraction pipeline—0
Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization—0
Dominant Drivers of National Inflation—0
Dual-stage optimizer for systematic overestimation adjustment applied to multi-objective genetic algorithms for biomarker selection—0
Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation—0
Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale—0
Doubly robust off-policy evaluation with shrinkage—0
Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models—0
Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation—0
DSDE: Using Proportion Estimation to Improve Model Selection for Out-of-Distribution Detection—0
Dynamically Learned Test-Time Model Routing in Language Model Zoos with Service Level Guarantees—0
Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference—0
Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting—0
Dynamic Model Selection for Prediction Under a Budget—0
Dynamics of Transient Structure in In-Context Linear Regression Transformers—0
Eagle: Efficient Training-Free Router for Multi-LLM Inference—0
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads—0
Easy Transfer Learning By Exploiting Intra-domain Structures—0
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