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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 351–375 of 2050 papers

TitleStatusHype
Boosting for Efficient Model Selection for Syntactic Parsing—0
Boosted Zero-Shot Learning with Semantic Correlation Regularization—0
A Base Model Selection Methodology for Efficient Fine-Tuning—0
A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection—0
Blockout: Dynamic Model Selection for Hierarchical Deep Networks—0
Adversarial Negotiation Dynamics in Generative Language Models—0
Boosting with Structural Sparsity: A Differential Inclusion Approach—0
Bootstrap based asymptotic refinements for high-dimensional nonlinear models—0
Blocked Clusterwise Regression—0
Boxer: Interactive Comparison of Classifier Results—0
Block-diagonal covariance selection for high-dimensional Gaussian graphical models—0
Breaking the bonds of weak coupling: the dynamic causal modelling of oscillator amplitudes—0
Bridge the Modality and Capability Gaps in Vision-Language Model Selection—0
Bridging AIC and BIC: a new criterion for autoregression—0
Bridging factor and sparse models—0
Bridging Information Criteria and Parameter Shrinkage for Model Selection—0
Bridging the Bosphorus: Advancing Turkish Large Language Models through Strategies for Low-Resource Language Adaptation and Benchmarking—0
Clustering evolving data using kernel-based methods—0
Budgeted Online Model Selection and Fine-Tuning via Federated Learning—0
Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics—0
Clustering - What Both Theoreticians and Practitioners are Doing Wrong—0
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure—0
A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study—0
Combined l_1 and greedy l_0 penalized least squares for linear model selection—0
Black-box Selective Inference via Bootstrapping—0
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