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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 176–200 of 2050 papers

TitleStatusHype
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein EngineeringCode1
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label LearningCode1
Laplace Redux -- Effortless Bayesian Deep LearningCode1
Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian QuadratureCode1
Automatic Model Selection with Large Language Models for ReasoningCode1
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter TuningCode1
LIBTwinSVM: A Library for Twin Support Vector MachinesCode1
Machine-Guided Discovery of a Real-World Rogue Wave ModelCode1
Machine Learning for Dynamic Resource Allocation in Network Function VirtualizationCode1
A General Model for Aggregating Annotations Across Simple, Complex, and Multi-Object Annotation TasksCode1
Mind the Gap: Evaluating Patch Embeddings from General-Purpose and Histopathology Foundation Models for Cell Segmentation and ClassificationCode1
AQuA: A Benchmarking Tool for Label Quality AssessmentCode1
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare SettingsCode1
Automating Outlier Detection via Meta-LearningCode1
Bayesian Model Selection, the Marginal Likelihood, and GeneralizationCode1
BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion ModelsCode1
Online Active Model Selection for Pre-trained ClassifiersCode1
Counterfactual Learning of Stochastic Policies with Continuous Actions: from Models to Offline EvaluationCode1
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular dataCode1
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD GeneralizationCode1
PARAGEN : A Parallel Generation ToolkitCode1
A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of PovertyCode1
Population Based Training of Neural NetworksCode1
Quantifying & Modeling Multimodal Interactions: An Information Decomposition FrameworkCode1
One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image SegmentationCode1
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