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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 1051–1075 of 2050 papers

TitleStatusHype
Progressive Sampling-Based Bayesian Optimization for Efficient and Automatic Machine Learning Model Selection—0
Prompt Design Matters for Computational Social Science Tasks but in Unpredictable Ways—0
PromptWise: Online Learning for Cost-Aware Prompt Assignment in Generative Models—0
Propagation Regularizer for Semi-Supervised Learning With Extremely Scarce Labeled Samples—0
Proximity Operator of the Matrix Perspective Function and its Applications—0
Pseudo Label Selection is a Decision Problem—0
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems—0
Pushing the limits of fairness impossibility: Who's the fairest of them all?—0
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs—0
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection—0
Qualitative inequalities for squared partial correlations of a Gaussian random vector—0
Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice—0
Revealing Similar Semantics Inside CNNs: An Interpretable Concept-based Comparison of Feature Spaces—0
Quantifying the Capability Boundary of DeepSeek Models: An Application-Driven Performance Analysis—0
Quantifying Uncertainty and Variability in Machine Learning: Confidence Intervals for Quantiles in Performance Metric Distributions—0
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles—0
Quantile Factor Models—0
Quantile universal threshold: model selection at the detection edge for high-dimensional linear regression—0
Quantized Neural Networks: Characterization and Holistic Optimization—0
Quantum Machine Learning in Log-based Anomaly Detection: Challenges and Opportunities—0
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models—0
Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies—0
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption—0
Random Models for Fuzzy Clustering Similarity Measures—0
Ranking pre-trained segmentation models for zero-shot transferability—0
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