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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 1651–1675 of 2050 papers

TitleStatusHype
Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach—0
Black-box Selective Inference via Bootstrapping—0
Block-diagonal covariance selection for high-dimensional Gaussian graphical models—0
Blocked Clusterwise Regression—0
Blockout: Dynamic Model Selection for Hierarchical Deep Networks—0
Boosted Zero-Shot Learning with Semantic Correlation Regularization—0
Boosting for Efficient Model Selection for Syntactic Parsing—0
Boosting with Structural Sparsity: A Differential Inclusion Approach—0
Bootstrap based asymptotic refinements for high-dimensional nonlinear models—0
Bootstrapped Adaptive Threshold Selection for Statistical Model Selection and Estimation—0
Boxer: Interactive Comparison of Classifier Results—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
Budgeted Online Model Selection and Fine-Tuning via Federated Learning—0
Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics—0
Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscosity—0
Boldness-Recalibration for Binary Event Predictions—0
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
Can We Use Gradient Norm as a Measure of Generalization Error for Model Selection in Practice?—0
Capitalizing on a Crisis: A Computational Analysis of all Five Million British Firms During the Covid-19 Pandemic—0
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