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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 201–250 of 2050 papers

TitleStatusHype
Online learning techniques for prediction of temporal tabular datasets with regime changesCode1
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy LabelsCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
Scalable Diverse Model Selection for Accessible Transfer LearningCode1
Searching for Effective Neural Network Architectures for Heart Murmur Detection from PhonocardiogramCode1
Assumption-lean inference for generalised linear model parametersCode1
Exploiting BERT for End-to-End Aspect-based Sentiment AnalysisCode1
A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engineCode1
SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse ModalitiesCode1
OTCE: A Transferability Metric for Cross-Domain Cross-Task RepresentationsCode1
An Algorithmic Framework for Computing Validation Performance Bounds by Using Suboptimal Models—0
A Case for Dataset Specific Profiling—0
Bayesian optimization for automated model selection—0
Nonlinear Causal Discovery for Grouped Data—0
A multi-stage machine learning model on diagnosis of esophageal manometry—0
Bayesian Nonparametrics: An Alternative to Deep Learning—0
Bayesian Optimization for Selecting Efficient Machine Learning Models—0
Absolute convergence and error thresholds in non-active adaptive sampling—0
A Multi-objective Exploratory Procedure for Regression Model Selection—0
Bayesian Model Selection via Mean-Field Variational Approximation—0
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process—0
AutoAI-TS: AutoAI for Time Series Forecasting—0
A model selection approach for clustering a multinomial sequence with non-negative factorization—0
Bayesian Network Models for Adaptive Testing—0
Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach—0
A ModelOps-based Framework for Intelligent Medical Knowledge Extraction—0
SpiKernel: A Kernel Size Exploration Methodology for Improving Accuracy of the Embedded Spiking Neural Network Systems—0
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning—0
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs—0
A Two-step Metropolis Hastings Method for Bayesian Empirical Likelihood Computation with Application to Bayesian Model Selection—0
A Meta-learning based Distribution System Load Forecasting Model Selection Framework—0
A Tractable Fully Bayesian Method for the Stochastic Block Model—0
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits—0
IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting—0
A Unified Approach to Routing and Cascading for LLMs—0
A Unified Dynamic Approach to Sparse Model Selection—0
A Unified Framework for Tuning Hyperparameters in Clustering Problems—0
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation—0
Bayesian Model Selection Methods for Mutual and Symmetric k-Nearest Neighbor Classification—0
A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays with Subarray Sampling—0
AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID—0
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data—0
Automated Model Selection for Generalized Linear Models—0
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach—0
Automated Model Selection for Time-Series Anomaly Detection—0
Automated Model Selection with Bayesian Quadrature—0
Adaptive Bayesian Linear Regression for Automated Machine Learning—0
Bayesian model selection consistency and oracle inequality with intractable marginal likelihood—0
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