Papers Ensemble Pruning
“Ensemble Pruning” 태그가 달린 논문 27편 · 필터 해제
LLM-TOPLA: Efficient LLM Ensemble by Maximising Diversity
Combining large language models during training or at inference time has shown substantial performance gain over component LLMs. This paper presents LLM-TOPLA, a diversity-optimized LLM ensemble method with three unique …
DiversityEnsemble PruningGSM8KMMLURobust Few-Shot Ensemble Learning with Focal Diversity-Based Pruning
This paper presents FusionShot, a focal diversity optimized few-shot ensemble learning approach for boosting the robustness and generalization performance of pre-trained few-shot models. The paper makes three original co…
DiversityEnsemble LearningEnsemble PruningFew-Shot LearningLiquid Democracy for Low-Cost Ensemble Pruning
We argue that there is a strong connection between ensemble learning and a delegative voting paradigm -- liquid democracy -- that can be leveraged to reduce ensemble training costs. We present an incremental training pro…
Ensemble LearningEnsemble PruningHierarchical Pruning of Deep Ensembles with Focal Diversity
Deep neural network ensembles combine the wisdom of multiple deep neural networks to improve the generalizability and robustness over individual networks. It has gained increasing popularity to study deep ensemble techni…
Decision MakingDiversityEnsemble PruningAutoselection of the Ensemble of Convolutional Neural Networks with Second-Order Cone Programming
Ensemble techniques are frequently encountered in machine learning and engineering problems since the method combines different models and produces an optimal predictive solution. The ensemble concept can be adapted to d…
Deep LearningDiversityEnsemble PruningA Robust Hypothesis Test for Tree Ensemble Pruning
Gradient boosted decision trees are some of the most popular algorithms in applied machine learning. They are a flexible and powerful tool that can robustly fit to any tabular dataset in a scalable and computationally ef…
Ensemble PruningEnsemble pruning via an integer programming approach with diversity constraints
Ensemble learning combines multiple classifiers in the hope of obtaining better predictive performance. Empirical studies have shown that ensemble pruning, that is, choosing an appropriate subset of the available classif…
Binary ClassificationDiversityEnsemble LearningEnsemble PruningThe Shapley Value in Machine Learning
Over the last few years, the Shapley value, a solution concept from cooperative game theory, has found numerous applications in machine learning. In this paper, we first discuss fundamental concepts of cooperative game t…
BIG-bench Machine LearningData ValuationEnsemble Pruningfeature selection+3Boosting Deep Ensemble Performance with Hierarchical Pruning
Deep neural network ensembles have become attractive learning techniques with better generalizability over individual models. Some mission critical applications may require a large number of deep neural networks to achie…
Decision MakingDiversityEnsemble PruningImage ClassificationConceptually Diverse Base Model Selection for Meta-Learners in Concept Drifting Data Streams
Meta-learners and ensembles aim to combine a set of relevant yet diverse base models to improve predictive performance. However, determining an appropriate set of base models is challenging, especially in online environm…
ClusteringEnsemble PruningModel SelectionTransfer LearningImproving the Accuracy-Memory Trade-Off of Random Forests Via Leaf-Refinement
Random Forests (RF) are among the state-of-the-art in many machine learning applications. With the ongoing integration of ML models into everyday life, the deployment and continuous application of models becomes more and…
Ensemble PruningLearn Together, Stop Apart: a Novel Approach to Ensemble Pruning
Gradient boosting is the most popular method of constructing ensembles that allow getting state-of-the-art results on many tasks. One of the critical parameters affecting the quality of the learned model is the number of…
Ensemble PruningOn-the-Fly Ensemble Pruning in Evolving Data Streams
Ensemble pruning is the process of selecting a subset of componentclassifiers from an ensemble which performs at least as well as theoriginal ensemble while reducing storage and computational costs.Ensemble pruning in da…
Ensemble PruningBoosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics
Neural network ensembles are gaining popularity by harnessing the complementary wisdom of multiple base models. Ensemble teams with high diversity promote high failure independence, which is effective for boosting th…
DiversityEnsemble LearningEnsemble PruningImage ClassificationThe Shapley Value of Classifiers in Ensemble Games
What is the value of an individual model in an ensemble of binary classifiers? We answer this question by introducing a class of transferable utility cooperative games called \textit{ensemble games}. In machine learning …
ClassificationEnsemble PruningGraph ClassificationWhen does Diversity Help Generalization in Classification Ensembles?
Ensembles, as a widely used and effective technique in the machine learning community, succeed within a key element -- "diversity." The relationship between diversity and generalization, unfortunately, is not entirely un…
ClassificationDiversityEnsemble PruningGeneral Classification+1Sub-Architecture Ensemble Pruning in Neural Architecture Search
Neural architecture search (NAS) is gaining more and more attention in recent years due to its flexibility and remarkable capability to reduce the burden of neural network design. To achieve better performance, however, …
DiversityEnsemble LearningEnsemble PruningNeural Architecture SearchEnsemble Pruning via Margin Maximization
Ensemble models refer to methods that combine a typically large number of classifiers into a compound prediction. The output of an ensemble method is the result of fitting a base-learning algorithm to a given data set, a…
DiversityEnsemble PruningThe MBPEP: a deep ensemble pruning algorithm providing high quality uncertainty prediction
Machine learning algorithms have been effectively applied into various real world tasks. However, it is difficult to provide high-quality machine learning solutions to accommodate an unknown distribution of input dataset…
BIG-bench Machine LearningEnsemble PruningPredictionEnsemble Pruning based on Objection Maximization with a General Distributed Framework
Ensemble pruning, selecting a subset of individual learners from an original ensemble, alleviates the deficiencies of ensemble learning on the cost of time and space. Accuracy and diversity serve as two crucial factors w…
DiversityEnsemble LearningEnsemble Pruning