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Model Selection
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Topic Modeling and Link-Prediction for Material Property Discovery
Advanced Financial Reasoning at Scale: A Comprehensive Evaluation of Large Language Models on CFA Level III
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale
Leveraging Predictive Equivalence in Decision Trees
The use of cross validation in the analysis of designed experiments
Gradient Boosting for Spatial Regression Models with Autoregressive Disturbances
Evaluating Generalization and Representation Stability in Small LMs via Prompting, Fine-Tuning and Out-of-Distribution Prompts
Large Language Models for History, Philosophy, and Sociology of Science: Interpretive Uses, Methodological Challenges, and Critical Perspectives
The Sample Complexity of Parameter-Free Stochastic Convex Optimization
Estimating the Number of Components in Panel Data Finite Mixture Regression Models with an Application to Production Function Heterogeneity
Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement Learning
A Statistical Framework for Model Selection in LSTM Networks
Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques
Tuning the Right Foundation Models is What you Need for Partial Label Learning
Nonlinear Causal Discovery for Grouped Data
Generating Automotive Code: Large Language Models for Software Development and Verification in Safety-Critical Systems
Crowd-SFT: Crowdsourcing for LLM Alignment
Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks
Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks
Selecting for Less Discriminatory Algorithms: A Relational Search Framework for Navigating Fairness-Accuracy Trade-offs in Practice
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming
Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method Generation
Machine-learning Growth at Risk
DeSocial: Blockchain-based Decentralized Social Networks
pared: Model selection using multi-objective optimization
Weighted Leave-One-Out Cross Validation
Dynamically Learned Test-Time Model Routing in Language Model Zoos with Service Level Guarantees
OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter Optimization
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science
PromptWise: Online Learning for Cost-Aware Prompt Assignment in Generative Models
Handling Symbolic Language in Student Texts: A Comparative Study of NLP Embedding Models
Towards more transferable adversarial attack in black-box manner
Navigating Pitfalls: Evaluating LLMs in Machine Learning Programming Education
Learning Latent Variable Models via Jarzynski-adjusted Langevin Algorithm
Multi-Output Gaussian Processes for Graph-Structured Data
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