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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 851–875 of 2050 papers

TitleStatusHype
Two-Stage Robust and Sparse Distributed Statistical Inference for Large-Scale Data—0
Adaptive LASSO estimation for functional hidden dynamic geostatistical model—0
Boosting with copula-based components—0
An Optimal Likelihood Free Method for Biological Model Selection—0
A Case for Dataset Specific Profiling—0
Interpreting and predicting the economy flows: A time-varying parameter global vector autoregressive integrated the machine learning model—0
Model selection with Gini indices under auto-calibration—0
Robust Output Analysis with Monte-Carlo Methodology—0
Label-Only Membership Inference Attack against Node-Level Graph Neural Networks—0
SecretGen: Privacy Recovery on Pre-Trained Models via Distribution DiscriminationCode0
Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference—0
Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection—0
Correcting Model Bias with Sparse Implicit Processes—0
Have we been Naive to Select Machine Learning Models? Noisy Data are here to Stay!—0
Cost-Effective Online Contextual Model Selection—0
Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier SystemCode0
FIB: A Method for Evaluation of Feature Impact Balance in Multi-Dimensional Data—0
A Statistical-Modelling Approach to Feedforward Neural Network Model Selection—0
Model Selection in Reinforcement Learning with General Function Approximations—0
Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning—0
Degrees of Freedom and Information Criteria for the Synthetic Control Method—0
AaltoNLP at SemEval-2022 Task 11: Ensembling Task-adaptive Pretrained Transformers for Multilingual Complex NER—0
Lookback for Learning to Branch—0
Best of Both Worlds Model Selection—0
Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting—0
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