Papers Model Selection
“Model Selection” 태그가 달린 논문 2,050편 · 필터 해제
Topic Modeling and Link-Prediction for Material Property Discovery
Link prediction infers missing or future relations between graph nodes, based on connection patterns. Scientific literature networks and knowledge graphs are typically large, sparse, and noisy, and often contain missing …
Knowledge GraphsLink PredictionModel Selectionscientific discoveryAdvanced Financial Reasoning at Scale: A Comprehensive Evaluation of Large Language Models on CFA Level III
As financial institutions increasingly adopt Large Language Models (LLMs), rigorous domain-specific evaluation becomes critical for responsible deployment. This paper presents a comprehensive benchmark evaluating 23 stat…
Model SelectionMultiple-choicemTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale
Multivariate time series anomaly detection (MTS-AD) is critical in domains like healthcare, cybersecurity, and industrial monitoring, yet remains challenging due to complex inter-variable dependencies, temporal dynamics,…
Anomaly DetectionBenchmarkingLanguage ModelingLanguage Modelling+4The use of cross validation in the analysis of designed experiments
Cross-validation (CV) is a common method to tune machine learning methods and can be used for model selection in regression as well. Because of the structured nature of small, traditional experimental designs, the litera…
Model SelectionLeveraging Predictive Equivalence in Decision Trees
Decision trees are widely used for interpretable machine learning due to their clearly structured reasoning process. However, this structure belies a challenge we refer to as predictive equivalence: a given tree's decisi…
Interpretable Machine LearningMissing ValuesModel SelectionEvaluating Generalization and Representation Stability in Small LMs via Prompting, Fine-Tuning and Out-of-Distribution Prompts
We investigate the generalization capabilities of small language models under two popular adaptation paradigms: few-shot prompting and supervised fine-tuning. While prompting is often favored for its parameter efficiency…
Model SelectionGradient Boosting for Spatial Regression Models with Autoregressive Disturbances
Researchers in urban and regional studies increasingly deal with spatial data that reflects geographic location and spatial relationships. As a framework for dealing with the unique nature of spatial data, various spatia…
Model SelectionregressionVariable SelectionLarge Language Models for History, Philosophy, and Sociology of Science: Interpretive Uses, Methodological Challenges, and Critical Perspectives
This paper explores the use of large language models (LLMs) as research tools in the history, philosophy, and sociology of science (HPSS). LLMs are remarkably effective at processing unstructured text and inferring meani…
Model SelectionPhilosophyRetrieval-augmented GenerationSociologyThe Sample Complexity of Parameter-Free Stochastic Convex Optimization
We study the sample complexity of stochastic convex optimization when problem parameters, e.g., the distance to optimality, are unknown. We pursue two strategies. First, we develop a reliable model selection method that …
Few-Shot LearningModel SelectionPrompt EngineeringStochastic OptimizationEstimating the Number of Components in Panel Data Finite Mixture Regression Models with an Application to Production Function Heterogeneity
This paper develops statistical methods for determining the number of components in panel data finite mixture regression models with regression errors independently distributed as normal or more flexible normal mixtures.…
Model SelectionRouter-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement Learning
The rapid emergence of diverse large language models (LLMs) has spurred the development of LLM routers that assign user queries to the most suitable model. However, existing LLM routers typically perform a single-round, …
Model SelectionReinforcement Learning (RL)A Statistical Framework for Model Selection in LSTM Networks
Long Short-Term Memory (LSTM) neural network models have become the cornerstone for sequential data modeling in numerous applications, ranging from natural language processing to time series forecasting. Despite their su…
Model SelectionTime Series ForecastingTowards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques
Recent progress in Language Models (LMs) has dramatically advanced the field of natural language processing (NLP), excelling at tasks like text generation, summarization, and question answering. However, their inference …
BenchmarkingModel SelectionQuestion AnsweringText GenerationNonlinear Causal Discovery for Grouped Data
Inferring cause-effect relationships from observational data has gained significant attention in recent years, but most methods are limited to scalar random variables. In many important domains, including neuroscience, p…
Causal DiscoveryGraph LearningModel SelectionTuning the Right Foundation Models is What you Need for Partial Label Learning
Partial label learning (PLL) seeks to train generalizable classifiers from datasets with inexact supervision, a common challenge in real-world applications. Existing studies have developed numerous approaches to progress…
Model SelectionPartial Label LearningCrowd-SFT: Crowdsourcing for LLM Alignment
Large Language Models (LLMs) increasingly rely on Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) to align model responses with human preferences. While RLHF employs a reinforcement lea…
FairnessModel Selectionreinforcement-learningReinforcement LearningGenerating Automotive Code: Large Language Models for Software Development and Verification in Safety-Critical Systems
Developing safety-critical automotive software presents significant challenges due to increasing system complexity and strict regulatory demands. This paper proposes a novel framework integrating Generative Artificial In…
BenchmarkingCode GenerationModel Selectiontest driven developmentFine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks
Sign Language Recognition (SLR) involves the automatic identification and classification of sign gestures from images or video, converting them into text or speech to improve accessibility for the hearing-impaired commun…
Data AugmentationModel SelectionSign Language RecognitionUniversal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks
Recommender systems are pivotal in delivering personalized experiences across industries, yet their adoption and scalability remain hindered by the need for extensive dataset- and task-specific configurations. Existing s…
Feature EngineeringModel SelectionRecommendation SystemsSelecting for Less Discriminatory Algorithms: A Relational Search Framework for Navigating Fairness-Accuracy Trade-offs in Practice
As machine learning models are increasingly embedded into society through high-stakes decision-making, selecting the right algorithm for a given task, audience, and sector presents a critical challenge, particularly in t…
FairnessHyperparameter OptimizationModel Selection