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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 1651–1700 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
Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing—0
Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis—0
Carbon Intensity-Aware Adaptive Inference of DNNs—0
SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era—0
Cats & Co: Categorical Time Series Coclustering—0
Causal Covariate Shift Correction using Fisher information penalty—0
Causal Discovery in Hawkes Processes by Minimum Description Length—0
Causal Falling Rule Lists—0
Causal Q-Aggregation for CATE Model Selection—0
Choice modelling in the age of machine learning - discussion paper—0
Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation—0
Choosing a Model, Shaping a Future: Comparing LLM Perspectives on Sustainability and its Relationship with AI—0
Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion—0
CLAMS: A System for Zero-Shot Model Selection for Clustering—0
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection—0
Classification Performance Metric for Imbalance Data Based on Recall and Selectivity Normalized in Class Labels—0
Classification with Scattering Operators—0
Classification with Sparse Overlapping Groups—0
client2vec: Towards Systematic Baselines for Banking Applications—0
Clipper: A Low-Latency Online Prediction Serving System—0
Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization—0
Closing the gap between open-source and commercial large language models for medical evidence summarization—0
Clustering-Based Validation Splits for Model Selection under Domain Shift—0
Clustering Discrete-Valued Time Series—0
Clustering evolving data using kernel-based methods—0
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