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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 701–750 of 2050 papers

TitleStatusHype
Bootstrap based asymptotic refinements for high-dimensional nonlinear models—0
PyVBMC: Efficient Bayesian inference in PythonCode1
Distribution-free Deviation Bounds and The Role of Domain Knowledge in Learning via Model Selection with Cross-validation Risk Estimation—0
Deploying Offline Reinforcement Learning with Human Feedback—0
Solar Power Prediction Using Machine Learning—0
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning—0
Machine learning for sports betting: should model selection be based on accuracy or calibration?Code0
A variational synthesis of evolutionary and developmental dynamics—0
Training Machine Learning Models to Characterize Temporal Evolution of Disadvantaged Communities—0
Searching for Effective Neural Network Architectures for Heart Murmur Detection from PhonocardiogramCode1
Ensemble Reinforcement Learning: A Survey—0
Online simulator-based experimental design for cognitive model selectionCode0
Eryn : A multi-purpose sampler for Bayesian inferenceCode1
Bayesian CART models for insurance claims frequency—0
In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised LearningCode0
Hyperparameter Tuning and Model Evaluation in Causal Effect EstimationCode0
A Vision for Semantically Enriched Data Science—0
FedScore: A privacy-preserving framework for federated scoring system developmentCode0
Quantifying & Modeling Multimodal Interactions: An Information Decomposition FrameworkCode1
Change is Hard: A Closer Look at Subpopulation ShiftCode1
A novel efficient Multi-view traffic-related object detection framework—0
Detecting Signs of Model Change with Continuous Model Selection Based on Descriptive Dimensionality—0
Pseudo-Labeling for Kernel Ridge Regression under Covariate ShiftCode0
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles—0
Estimating Optimal Policy Value in General Linear Contextual Bandits—0
Evaluating Representations with Readout Model Switching—0
Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR ImagesCode1
Infinite Action Contextual Bandits with Reusable Data ExhaustCode0
Linear Bandits with Memory: from Rotting to Rising—0
Best Arm Identification for Stochastic Rising BanditsCode0
When mitigating bias is unfair: multiplicity and arbitrariness in algorithmic group fairnessCode0
Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLPCode0
What are the mechanisms underlying metacognitive learning?—0
Fast Linear Model Trees by PILOT—0
Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection—0
On the Limitation and Experience Replay for GNNs in Continual Learning—0
A Strong Baseline for Batch Imitation Learning—0
Penalized Quasi-likelihood Estimation and Model Selection in Time Series Models with Parameters on the Boundary—0
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect EstimationCode0
Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust—0
Empirical analysis in limit order book modeling for Nikkei 225 Stocks with Cox-type intensities—0
MLOps with enhanced performance control and observability—0
Dirichlet process mixture of Gaussian process functional regressions and its variational EM algorithm—0
How to select predictive models for causal inference?—0
Straight-Through meets Sparse Recovery: the Support Exploration Algorithm—0
Revisiting Bellman Errors for Offline Model SelectionCode0
Specializing Smaller Language Models towards Multi-Step ReasoningCode2
A Deep Learning Method for Comparing Bayesian Hierarchical ModelsCode0
Warlock: an automated computational workflow for simulating spatially structured tumour evolutionCode0
Data thinning for convolution-closed distributionsCode1
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