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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 276–300 of 2050 papers

TitleStatusHype
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance—0
Active Learning for Undirected Graphical Model Selection—0
A study on the distribution of social biases in self-supervised learning visual models—0
A Latent Gaussian Mixture Model for Clustering Longitudinal Data—0
A Bayesian Perspective on Training Speed and Model Selection—0
Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach—0
A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation—0
On the Existence of Simpler Machine Learning Models—0
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?—0
A Strong Baseline for Batch Imitation Learning—0
A Statistical Theory of Deep Learning via Proximal Splitting—0
A Large Scale Evaluation of Distributional Semantic Models: Parameters, Interactions and Model Selection—0
Active Learning Algorithms for Graphical Model Selection—0
A Statistical-Modelling Approach to Feedforward Neural Network Model Selection—0
A Statistical Framework for Model Selection in LSTM Networks—0
A Junction Tree Framework for Undirected Graphical Model Selection—0
A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization—0
Active Comparison of Prediction Models—0
Quantitative Overfitting Management for Human-in-the-loop ML Application Development with ease.ml/meter—0
Black-box Selective Inference via Bootstrapping—0
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science—0
Action-State Dependent Dynamic Model Selection—0
On The Stability of Interpretable Models—0
A spectral clustering-type algorithm for the consistent estimation of the Hurst distribution in moderately high dimensions—0
AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models—0
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