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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 1901–1925 of 2050 papers

TitleStatusHype
Evaluating the Utility of Model Explanations for Model Development—0
Evaluating Word Embeddings on Low-Resource Languages—0
Evaluation of Model Selection for Kernel Fragment Recognition in Corn Silage—0
e-Values for Real-Time Residential Electricity Demand Forecast Model Selection—0
Evasion Attacks against Machine Learning at Test Time—0
Event Data Association via Robust Model Fitting for Event-based Object Tracking—0
Exact Dimensionality Selection for Bayesian PCA—0
Exact Post Model Selection Inference for Marginal Screening—0
Exact post-selection inference, with application to the lasso—0
Experiment Planning with Function Approximation—0
Expert Finding in Community Question Answering: A Review—0
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries—0
ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations—0
Explainable Adaptive Tree-based Model Selection for Time Series Forecasting—0
Explainable Multi-class Classification of Medical Data—0
Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models—0
Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference—0
Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology—0
Exploratory Hidden Markov Factor Models for Longitudinal Mobile Health Data: Application to Adverse Posttraumatic Neuropsychiatric Sequelae—0
Exploring Dynamic Novel View Synthesis Technologies for Cinematography—0
Exploring linguistic feature and model combination for speech recognition based automatic AD detection—0
Exploring the Potential of SSL Models for Sound Event Detection—0
Exploring the Potentials and Challenges of Using Large Language Models for the Analysis of Transcriptional Regulation of Long Non-coding RNAs—0
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition—0
Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects—0
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