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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 11–20 of 2050 papers

TitleStatusHype
Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement LearningCode2
A Statistical Framework for Model Selection in LSTM Networks—0
Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques—0
Tuning the Right Foundation Models is What you Need for Partial Label LearningCode1
Nonlinear Causal Discovery for Grouped Data—0
Generating Automotive Code: Large Language Models for Software Development and Verification in Safety-Critical Systems—0
Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks—0
Crowd-SFT: Crowdsourcing for LLM Alignment—0
Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks—0
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming—0
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