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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 91–100 of 2050 papers

TitleStatusHype
Model Selection via MCRB Optimization—0
Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical SystemsCode0
Instruction-Guided Autoregressive Neural Network Parameter Generation—0
Efficient Model Selection for Time Series Forecasting via LLMs—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
Neural Bayes inference for complex bivariate extremal dependence modelsCode0
Reinforcement Learning for Machine Learning Model Deployment: Evaluating Multi-Armed Bandits in ML Ops Environments—0
Collab: Controlled Decoding using Mixture of Agents for LLM Alignment—0
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