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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 1551–1575 of 2050 papers

TitleStatusHype
A Statistical Framework for Model Selection in LSTM Networks—0
A Statistical-Modelling Approach to Feedforward Neural Network Model Selection—0
A Statistical Theory of Deep Learning via Proximal Splitting—0
A Strong Baseline for Batch Imitation Learning—0
On the Existence of Simpler Machine Learning Models—0
A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation—0
A study on the distribution of social biases in self-supervised learning visual models—0
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance—0
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification—0
A Survey on Theoretical Advances of Community Detection in Networks—0
A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Example—0
Asymmetrically Weighted CCA And Hierarchical Kernel Sentence Embedding For Image & Text Retrieval—0
A Symmetry-based Framework for Model Selection of Coral Reef Population Growth Models—0
Asymptotic Accuracy of Distribution-Based Estimation for Latent Variables—0
Asymptotic Model Selection for Directed Networks with Hidden Variables—0
Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection—0
A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data—0
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data—0
A Tractable Fully Bayesian Method for the Stochastic Block Model—0
A Two-step Metropolis Hastings Method for Bayesian Empirical Likelihood Computation with Application to Bayesian Model Selection—0
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs—0
A Unified Approach to Routing and Cascading for LLMs—0
A Unified Dynamic Approach to Sparse Model Selection—0
A Unified Framework for Tuning Hyperparameters in Clustering Problems—0
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation—0
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