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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 401–450 of 2050 papers

TitleStatusHype
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
DECODE: Domain-aware Continual Domain Expansion for Motion PredictionCode0
Statistical inference for quantum singular models—0
An AutoML-based approach for Network Intrusion Detection—0
BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices—0
Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions—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
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
Deep Learning Models for UAV-Assisted Bridge Inspection: A YOLO Benchmark Analysis—0
Rising Rested Bandits: Lower Bounds and Efficient Algorithms—0
GeMID: Generalizable Models for IoT Device IdentificationCode0
Exploring the Potentials and Challenges of Using Large Language Models for the Analysis of Transcriptional Regulation of Long Non-coding RNAs—0
Energy-Aware Dynamic Neural Inference—0
DSDE: Using Proportion Estimation to Improve Model Selection for Out-of-Distribution Detection—0
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference—0
Dirichlet process mixtures of block g priors for model selection and prediction in linear modelsCode0
Efficient Model Compression for Bayesian Neural Networks—0
MESS+: Energy-Optimal Inferencing in Language Model Zoos with Service Level Guarantees—0
Leveraging LLMs for MT in Crisis Scenarios: a blueprint for low-resource languages—0
Model-free Estimation of Latent Structure via Multiscale Nonparametric Maximum Likelihood—0
Power side-channel leakage localization through adversarial training of deep neural networksCode0
Bayesian Regression for Predicting Subscription to Bank Term Deposits in Direct Marketing Campaigns—0
Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flows—0
Learning the Regularization Strength for Deep Fine-Tuning via a Data-Emphasized Variational ObjectiveCode0
Towards Better Open-Ended Text Generation: A Multicriteria Evaluation FrameworkCode0
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness—0
Stabilizing black-box model selection with the inflated argmax—0
e-Values for Real-Time Residential Electricity Demand Forecast Model Selection—0
TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment—0
SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning—0
High-Fidelity Transfer of Functional Priors for Wide Bayesian Neural Networks by Learning ActivationsCode0
Towards Unsupervised Validation of Anomaly-Detection Models—0
ORSO: Accelerating Reward Design via Online Reward Selection and Policy OptimizationCode0
All models are wrong, some are useful: Model Selection with Limited LabelsCode0
A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective LandscapesCode0
Transformers4NewsRec: A Transformer-based News Recommendation Framework—0
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks—0
A Unified Approach to Routing and Cascading for LLMs—0
Impact of Missing Values in Machine Learning: A Comprehensive Analysis—0
Decision-Aware Predictive Model Selection for Workforce Allocation—0
UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language ModelsCode0
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