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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 201–225 of 2050 papers

TitleStatusHype
Evaluating Language Models as Synthetic Data GeneratorsCode1
Noncommutative Model Selection and the Data-Driven Estimation of Real Cohomology Groups—0
Noncommutative Model Selection for Data Clustering and Dimension Reduction Using Relative von Neumann Entropy—0
On the relative performance of some parametric and nonparametric estimators of option prices—0
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs—0
SPAgent: Adaptive Task Decomposition and Model Selection for General Video Generation and Editing—0
Optimized Conformal Selection: Powerful Selective Inference After Conformity Score OptimizationCode0
DECODE: Domain-aware Continual Domain Expansion for Motion PredictionCode0
ER2Score: LLM-based Explainable and Customizable Metric for Assessing Radiology Reports with Reward-Control Loss—0
AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive ModellingCode0
Statistical inference for quantum singular models—0
An AutoML-based approach for Network Intrusion Detection—0
Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions—0
BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices—0
LLM4DS: Evaluating Large Language Models for Data Science Code Generation—0
A Novel Approach to Eliminating Hallucinations in Large Language Model-Assisted Causal Discovery—0
Continuous Bayesian Model Selection for Multivariate Causal Discovery—0
A survey of probabilistic generative frameworks for molecular simulationsCode0
Evaluating Gender Bias in Large Language Models—0
LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language InterpretationCode2
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A SurveyCode0
Mitigating covariate shift in non-colocated data with learned parameter priors—0
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning—0
UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter Calibration—0
Model Selection for Average Reward RL with Application to Utility Maximization in Repeated Games—0
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