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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 651–700 of 2050 papers

TitleStatusHype
Frame Fusion with Vehicle Motion Prediction for 3D Object Detection—0
Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization—0
AQuA: A Benchmarking Tool for Label Quality AssessmentCode1
LOVM: Language-Only Vision Model SelectionCode1
Conditional Matrix Flows for Gaussian Graphical ModelsCode1
Sliding Window Neural Generated Tracking Based on Measurement Model—0
Two-level histograms for dealing with outliers and heavy tail distributions—0
Gibbs-Based Information Criteria and the Over-Parameterized Regime—0
Stochastic Marginal Likelihood Gradients using Neural Tangent KernelsCode0
On Pitfalls of Test-Time AdaptationCode1
Bivariate Causal Discovery using Bayesian Model SelectionCode0
Data-Driven Online Model Selection With Regret Guarantees—0
Structured model selection via _1-_2 optimizationCode0
Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint AveragingCode0
Green Runner: A tool for efficient model selection from model repositories—0
Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID—0
Accurate generation of stochastic dynamics based on multi-model Generative Adversarial Networks—0
Rethinking the Evaluation Protocol of Domain GeneralizationCode1
Learning Relevant Contextual Variables Within Bayesian OptimizationCode0
Clustering Indices based Automatic Classification Model SelectionCode0
Automatic Model Selection with Large Language Models for ReasoningCode1
Unraveling Cold Start Enigmas in Predictive Analytics for OTT Media: Synergistic Meta-Insights and Multimodal Ensemble Mastery—0
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model—0
Ranking & Reweighting Improves Group Distributional Robustness—0
Boldness-Recalibration for Binary Event Predictions—0
fairml: A Statistician's Take on Fair Machine Learning Modelling—0
Strengthening structural baselines for graph classification using Local Topological ProfileCode0
Revealing Similar Semantics Inside CNNs: An Interpretable Concept-based Comparison of Feature Spaces—0
Limits of Model Selection under Transfer Learning—0
ALMERIA: Boosting pairwise molecular contrasts with scalable methods—0
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits—0
Uni-QSAR: an Auto-ML Tool for Molecular Property PredictionCode3
Sparse Private LASSO Logistic Regression—0
Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022—0
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs—0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Efficient Deep Reinforcement Learning Requires Regulating Overfitting—0
An XAI framework for robust and transparent data-driven wind turbine power curve modelsCode1
An Offline Metric for the Debiasedness of Click ModelsCode0
Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings—0
Modeling Transient Changes in Circadian Rhythms—0
The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges—0
Priors for symbolic regressionCode0
How Graph Structure and Label Dependencies Contribute to Node Classification in a Large Network of DocumentsCode0
A principled approach to model validation in domain generalizationCode0
You Only Train Once: Learning a General Anomaly Enhancement Network with Random Masks for Hyperspectral Anomaly DetectionCode1
HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging FaceCode6
Model Validation and Selection in Metabolic Flux Analysis and Flux Balance Analysis—0
Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models—0
AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting—0
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