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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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TitleStatusHype
Topic Modeling and Link-Prediction for Material Property Discovery—0
Advanced Financial Reasoning at Scale: A Comprehensive Evaluation of Large Language Models on CFA Level III—0
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at ScaleCode0
Leveraging Predictive Equivalence in Decision TreesCode0
The use of cross validation in the analysis of designed experimentsCode0
Gradient Boosting for Spatial Regression Models with Autoregressive Disturbances—0
Evaluating Generalization and Representation Stability in Small LMs via Prompting, Fine-Tuning and Out-of-Distribution Prompts—0
Large Language Models for History, Philosophy, and Sociology of Science: Interpretive Uses, Methodological Challenges, and Critical Perspectives—0
The Sample Complexity of Parameter-Free Stochastic Convex Optimization—0
Estimating the Number of Components in Panel Data Finite Mixture Regression Models with an Application to Production Function Heterogeneity—0
Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement LearningCode2
A Statistical Framework for Model Selection in LSTM Networks—0
Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques—0
Tuning the Right Foundation Models is What you Need for Partial Label LearningCode1
Nonlinear Causal Discovery for Grouped Data—0
Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks—0
Generating Automotive Code: Large Language Models for Software Development and Verification in Safety-Critical Systems—0
Crowd-SFT: Crowdsourcing for LLM Alignment—0
Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks—0
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming—0
Selecting for Less Discriminatory Algorithms: A Relational Search Framework for Navigating Fairness-Accuracy Trade-offs in Practice—0
Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method GenerationCode0
Machine-learning Growth at Risk—0
pared: Model selection using multi-objective optimizationCode0
DeSocial: Blockchain-based Decentralized Social NetworksCode1
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