Model Selection

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Overview

Model selection studies how to choose among candidate models or configurations so predictions generalize while meeting goals such as interpretability, cost, or speed. Common approaches include cross-validation, information criteria such as AIC, BIC, and minimum description length, Bayesian methods, and regularization for selecting features or model complexity. For large model pools, active evaluation, bandits, meta-learning, and routing systems allocate tests or queries to identify promising models, ensembles, or task-specific specialists. A central challenge is that selection can overfit validation data, and rankings can change with the evaluation protocol or deployment setting, so no single criterion works everywhere.

Latest papers

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96 papers21 papers in the last four weeks, up 133% on the four weeks before. 0.1% of all new papers.

  1. Large Language Model Selection with Limited Annotations

    Oct 10, 2025Yavuz Durmazkeser, Patrik Okanovic, Andreas Kirsch +2Model SelectionLLM Evaluation

    TU Delft · ETH Zurich

  2. Delphos: A reinforcement learning framework for assisting discrete choice model specification

    Jun 6, 2025Gabriel Nova, Stephane Hess, Sander van CranenburghDiscrete Choice ModelingModel Selection

    CityAI Lab, Transport and Logistics Group, Delft University of Technology, The Netherlands · Choice Modelling Centre - Institute for Transport Studies, University of Leeds, England

  3. Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian Optimization Perspective

    Feb 17, 2025Yu-Wei Yang, Wei Hung, Yun-Ming Chan +2Model SelectionOffline RL

    Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan · Applied Machine Learning, Meta AI, Menlo Park, CA, USA

  4. Optimized Conformal Selection: Powerful Selective Inference After Data-Dependent Score Construction

    Nov 27, 2024Tian Bai, Ying JinConformal PredictionSelective Inference

    Department of Mathematics and Statistics, McGill University · Data Science Initiative and Department of Health Care Policy, Harvard University

  5. Efficient Cross-Validation for Sparse Linear Regression

    Jun 26, 2023Ryan Cory-Wright, Andrés GómezModel SelectionCross-Validation

    Department of Analytics, Marketing and Operations, Imperial Business School, London, UK · Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, CA

  6. Distribution-free Deviation Bounds and The Role of Domain Knowledge in Learning via Model Selection with Cross-validation Risk Estimation

    Mar 15, 2023Diego Marcondes, Cláudia PeixotoModel SelectionCross-Validation