Latest Articles for Algorithm Development

This paper discusses combining federated and contrastive learning to enhance data privacy and model performance.

Examining graph coloring, especially in bounded-diameter graphs and odd cycles.

Exploring synchronization tasks in a fully-anonymous shared-memory model.

G-RRT* improves robot pathfinding in complex environments through efficient sampling.

This article discusses early stopping to improve model selection efficiency in machine learning.

A new approach improves learning efficiency in reinforcement learning through sequence compression.

A new method enhances solutions for complex MIP challenges.

A look at the role of minimizers in string sampling and analysis.

Learn how edge-geodetic sets help monitor network connections.

A look into online learning algorithms and expert interactions.

A new framework enhances data layout for better performance in analytics systems.

DynaSeg improves image segmentation without relying on labeled data.

Exploring how consensus can be achieved despite unknown participants and faults.

A new method improves DBSCAN's efficiency for high-dimensional datasets and streaming data.

This study presents a new method for managing urban transport efficiently.

This study focuses on existentially quantified variables in QBF, aiming for efficient solutions.

MOCCA algorithm enhances MRI imaging speed and quality using incomplete data.

A method for effective and controlled data transfer between different domains.

This paper presents new algorithms for managing interval selection in continuous data streams.

A new approach combines privacy protections with contextual bandit learning.

A novel approach to influence maximization in complex networks.

Developing algorithms for effective triangle sampling in graph data streams.

A novel approach for tackling nonlinear optimization under fuzzy conditions.

New methods improve accuracy in join size estimation while protecting sensitive data.

New methods improve efficiency in identifying communities within evolving graphs.

A look into nonsmooth optimization and its local subgradient variations.

A new method enhances search efficiency for multi-objective optimization challenges.

This algorithm improves camera positioning accuracy using points and lines.

A new method enhances optimization in costly high-dimensional problems.

A framework that enhances reinforcement learning by incorporating essential constraints.

A study on improving task allocation fairness for service providers and tasks.

A new approach to finding independent sets in graphs using any starting configuration.

GMASK offers a flexible solution for approximate similarity search across various datasets.

This work explores LLMs' role in improving reinforcement learning with reward shaping.

A new method enhances efficiency in document ranking tasks.

New methods enhance reliability and efficiency in machine learning distributed systems.

FedSC improves model training while maintaining user privacy in federated learning.

Exploring the PAC-Bayesian framework's impact on machine learning generalization.

Improved strategies for efficient job scheduling on parallel machines.

A study on improving prediction accuracy for regression models.