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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 1826–1850 of 2050 papers

TitleStatusHype
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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