Papers Model Selection
“Model Selection” 태그가 달린 논문 2,050편 · 필터 해제
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming
Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph is a signed graph with no cycles containing an odd num…
Graph LearningModel SelectionBehavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method Generation
Automating the enrichment of UML class diagrams with behavioral methods from natural language use cases is a significant challenge. This study evaluates nine large language models (LLMs) in augmenting a methodless UML di…
Model SelectionPrompt EngineeringvalidMachine-learning Growth at Risk
We analyse growth vulnerabilities in the US using quantile partial correlation regression, a selection-based machine-learning method that achieves model selection consistency under time series. We find that downside risk…
Model SelectionTime SeriesDeSocial: Blockchain-based Decentralized Social Networks
Web 2.0 social platforms are inherently centralized, with user data and algorithmic decisions controlled by the platform. However, users can only passively receive social predictions without being able to choose the unde…
Model SelectionPredictionpared: Model selection using multi-objective optimization
Motivation: Model selection is a ubiquitous challenge in statistics. For penalized models, model selection typically entails tuning hyperparameters to maximize a measure of fit or minimize out-of-sample prediction error.…
modelModel SelectionDynamically Learned Test-Time Model Routing in Language Model Zoos with Service Level Guarantees
Open-weight LLM zoos provide access to numerous high-quality models, but selecting the appropriate model for specific tasks remains challenging and requires technical expertise. Most users simply want factually correct, …
Language ModelingLanguage ModellingmodelModel Selection+1Weighted Leave-One-Out Cross Validation
We present a weighted version of Leave-One-Out (LOO) cross-validation for estimating the Integrated Squared Error (ISE) when approximating an unknown function by a predictor that depends linearly on evaluations of the fu…
Model SelectionOptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter Optimization
Hyperparameter optimization (HPO) is a critical yet challenging aspect of machine learning model development, significantly impacting model performance and generalization. Traditional HPO methods often struggle with high…
AutoMLDecision MakingHyperparameter OptimizationModel SelectionAssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science
Large language models (LLMs) have advanced the automation of data science workflows. Yet it remains unclear whether they can critically leverage external domain knowledge as human data scientists do in practice. To answe…
BenchmarkingFeature EngineeringModel SelectionPromptWise: Online Learning for Cost-Aware Prompt Assignment in Generative Models
The rapid advancement of generative AI models has provided users with numerous options to address their prompts. When selecting a generative AI model for a given prompt, users should consider not only the performance of …
Code GenerationModel SelectionNavigating Pitfalls: Evaluating LLMs in Machine Learning Programming Education
The rapid advancement of Large Language Models (LLMs) has opened new avenues in education. This study examines the use of LLMs in supporting learning in machine learning education; in particular, it focuses on the abilit…
Model SelectionHandling Symbolic Language in Student Texts: A Comparative Study of NLP Embedding Models
Recent advancements in Natural Language Processing (NLP) have facilitated the analysis of student-generated language products in learning analytics (LA), particularly through the use of NLP embedding models. Yet when it …
Model SelectionLearning Latent Variable Models via Jarzynski-adjusted Langevin Algorithm
We utilise a sampler originating from nonequilibrium statistical mechanics, termed here Jarzynski-adjusted Langevin algorithm (JALA), to build statistical estimation methods in latent variable models. We achieve this by …
Computational EfficiencyModel SelectionTowards more transferable adversarial attack in black-box manner
Adversarial attacks have become a well-explored domain, frequently serving as evaluation baselines for model robustness. Among these, black-box attacks based on transferability have received significant attention due to …
Adversarial AttackAdversarial PurificationDenoisingInductive Bias+1Multi-Output Gaussian Processes for Graph-Structured Data
Graph-structured data is a type of data to be obtained associated with a graph structure where vertices and edges describe some kind of data correlation. This paper proposes a regression method on graph-structured data, …
Gaussian ProcessesModel SelectionLASSO-ODE: A framework for mechanistic model identifiability and selection in disease transmission modeling
To be fully useful for public health practice, models for epidemic response must be able to do more than predict -- it is also important to incorporate the mechanisms underlying transmission dynamics to enable policymake…
Model Selectionparameter estimationTime SeriesTime Series AnalysisLCDB 1.1: A Database Illustrating Learning Curves Are More Ill-Behaved Than Previously Thought
Sample-wise learning curves plot performance versus training set size. They are useful for studying scaling laws and speeding up hyperparameter tuning and model selection. Learning curves are often assumed to be well-beh…
Model SelectionSecond-Order Convergence in Private Stochastic Non-Convex Optimization
We investigate the problem of finding second-order stationary points (SOSP) in differentially private (DP) stochastic non-convex optimization. Existing methods suffer from two key limitations: (i) inaccurate convergence …
Model SelectionMultiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One
Model ensemble is a useful approach in reinforcement learning (RL) for training effective agents. Despite wide success of RL, training effective agents remains difficult due to the multitude of factors requiring careful …
Model SelectionReinforcement Learning (RL)In-Domain African Languages Translation Using LLMs and Multi-armed Bandits
Neural Machine Translation (NMT) systems face significant challenges when working with low-resource languages, particularly in domain adaptation tasks. These difficulties arise due to limited training data and suboptimal…
Domain AdaptationMachine TranslationModel SelectionMulti-Armed Bandits+3