learning-to-collaborate
Collaboration optimizer
A research project focused on developing methods for learning to collaborate in multi-objective optimization problems using machine learning and deep learning techniques.
11 stars
1 watching
2 forks
Language: Python
last commit: over 4 years agoRelated projects:
| Repository | Description | Stars |
|---|---|---|
| An algorithm that optimizes collaboration in federated learning by clustering clients into non-overlapping coalitions based on data quantity and pairwise distribution distances. | 16 | |
| An optimization framework designed to address heterogeneity in federated learning across distributed networks | 655 | |
| A Python package for gradient-based function optimization in machine learning | 181 | |
| A Python library for multiobjective optimization algorithms and analysis tools. | 579 | |
| An open-source project providing hardware accelerated, batchable and differentiable optimizers in JAX for deep learning. | 941 | |
| A comprehensive framework for solving optimization problems without gradient calculations. | 575 | |
| A tool that extracts and optimizes high-level code snippets into LLVM IR using MLIR for high-level synthesis | 37 | |
| An implementation of federated learning optimized for training on renewable energy sources and spare compute capacity to minimize carbon emissions. | 19 | |
| Automates optimization of technical indicators for machine learning models in finance | 421 | |
| An algorithm for Federated Learning with heterogeneous data, designed to optimize deep neural networks and improve performance | 2 | |
| An open-source software project implementing collaborative learning in bandits to achieve near-optimal results | 1 | |
| An optimization library based on nature-inspired meta-heuristic algorithms. | 609 | |
| A tool for training federated learning models with adaptive gradient balancing to handle class imbalance in multi-client scenarios. | 14 | |
| An optimization algorithm implementation in Matlab. | 83 | |
| Software framework for designing and executing sequential learning experiments in materials discovery | 60 |