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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 261–270 of 2050 papers

TitleStatusHype
UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language ModelsCode0
Leveraging free energy in pretraining model selection for improved fine-tuning—0
Parameter Choice and Neuro-Symbolic Approaches for Deep Domain-Invariant Learning—0
Trained Models Tell Us How to Make Them Robust to Spurious Correlation without Group AnnotationCode0
SePPO: Semi-Policy Preference Optimization for Diffusion AlignmentCode1
LLMProxy: Reducing Cost to Access Large Language Models—0
Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling—0
MLP-KAN: Unifying Deep Representation and Function LearningCode0
OOD-Chameleon: Is Algorithm Selection for OOD Generalization Learnable?—0
Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization—0
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