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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 1341–1350 of 2050 papers

TitleStatusHype
Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models—0
Understanding the double descent curve in Machine Learning—0
Understanding the Limits of Deep Tabular Methods with Temporal Shift—0
Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review—0
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction—0
UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning—0
UniTN End-to-End Discourse Parser for CoNLL 2016 Shared Task—0
Universal and data-adaptive algorithms for model selection in linear contextual bandits—0
Universal Approximation of Edge Density in Large Graphs—0
Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks—0
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