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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 1851–1875 of 2050 papers

TitleStatusHype
Improved Group Robustness via Classifier Retraining on Independent SplitsCode0
Nearest Neighbour Equilibrium ClusteringCode0
The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and SuggestionsCode0
BiasBed -- Rigorous Texture Bias EvaluationCode0
Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized RecommendationsCode0
ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy ModelsCode0
Pseudo-Labeling for Kernel Ridge Regression under Covariate ShiftCode0
Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell TimeCode0
Improving Subseasonal Forecasting in the Western U.S. with Machine LearningCode0
Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution ShiftCode0
In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised LearningCode0
IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmarkCode0
A survey of probabilistic generative frameworks for molecular simulationsCode0
Increasing certainty in systems biology models using Bayesian multimodel inferenceCode0
Referenced Thermodynamic Integration for Bayesian Model Selection: Application to COVID-19 Model SelectionCode0
Towards Measuring Representational Similarity of Large Language ModelsCode0
Indian Buffet process for model selection in convolved multiple-output Gaussian processesCode0
Individualized Prediction of COVID-19 Adverse outcomes with MLHOCode0
Neural Architecture Search with Bayesian Optimisation and Optimal TransportCode0
Neural Bayes inference for complex bivariate extremal dependence modelsCode0
Deep Learning and Linear Programming for Automated Ensemble Forecasting and InterpretationCode0
INFaaS: A Model-less and Managed Inference Serving SystemCode0
Neural Vector Spaces for Unsupervised Information RetrievalCode0
Reliable Time Prediction in the Markov Stochastic Block ModelCode0
Comparative Evaluation of Learning Models for Bionic Robots: Non-Linear Transfer Function IdentificationsCode0
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