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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 1061–1070 of 2050 papers

TitleStatusHype
Two-level histograms for dealing with outliers and heavy tail distributions—0
Two-Stage Robust and Sparse Distributed Statistical Inference for Large-Scale Data—0
Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust—0
Uncertainty Profiles for LLMs: Uncertainty Source Decomposition and Adaptive Model-Metric Selection—0
Understanding and Estimating the Adaptability of Domain-Invariant Representations—0
Understanding prompt engineering may not require rethinking generalization—0
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
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