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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 291–300 of 2050 papers

TitleStatusHype
What the HellaSwag? On the Validity of Common-Sense Reasoning BenchmarksCode0
Has the Creativity of Large-Language Models peaked? An analysis of inter- and intra-LLM variability—0
Robust Social Planning—0
Model Selection via MCRB Optimization—0
Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical SystemsCode0
Efficient Model Selection for Time Series Forecasting via LLMs—0
Instruction-Guided Autoregressive Neural Network Parameter Generation—0
Optimizing Humor Generation in Large Language Models: Temperature Configurations and Architectural Trade-offs—0
Why risk matters for protein binder design—0
AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting—0
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