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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 376–400 of 2050 papers

TitleStatusHype
Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias—0
Can Pre-training Indicators Reliably Predict Fine-tuning Outcomes of LLMs?—0
Black-box Selective Inference via Bootstrapping—0
Can We Use Gradient Norm as a Measure of Generalization Error for Model Selection in Practice?—0
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews—0
Capitalizing on a Crisis: A Computational Analysis of all Five Million British Firms During the Covid-19 Pandemic—0
Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing—0
Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis—0
Carbon Intensity-Aware Adaptive Inference of DNNs—0
Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach—0
SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era—0
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods—0
Cats & Co: Categorical Time Series Coclustering—0
Causal Covariate Shift Correction using Fisher information penalty—0
Causal Discovery in Hawkes Processes by Minimum Description Length—0
A Review of Cross-Sectional Matrix Exponential Spatial Models—0
Causal Falling Rule Lists—0
Causal Q-Aggregation for CATE Model Selection—0
Advancements in Natural Language Processing: Exploring Transformer-Based Architectures for Text Understanding—0
Bitcoin Forecasting Using ARIMA and PROPHET—0
An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework—0
Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor—0
A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence—0
A Rule-Based Epidemiological Modelling Framework—0
Binary Matrix Factorization via Dictionary Learning—0
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