Ahmad Esmaeili

@article{esmaeili2024hybrid,
  abbr = {ACM TOIT},
  bibtex_show = {false},
  title = {Hybrid Algorithm Selection and Hyperparameter Tuning on Distributed Machine Learning Resources: A Hierarchical Agent-based Approach},
  author = {Esmaeili, Ahmad and Rayz, Julia M. and Matson, Eric T.},
  volume = {24},
  number = {4},
  pages = {1--30},
  year = {2024},
  journal = {ACM Transactions on Internet Technology},
  publisher = {ACM New York, NY, USA},
  selected = {true}
}

@article{ghorrati2024multi,
  abbr = {IEEE Access},
  bibtex_show = {false},
  title = {A Multi-Section Hierarchical Deep Neural Network Model for Time Series Classification: Applied To Wearable Sensor-Based Human Activity Recognition},
  author = {Ghorrati, Zahra and Esmaeili, Ahmad and Matson, Eric T},
  journal = {IEEE Access},
  volume = {12},
  number = {},
  pages = {137851-137869},
  year = {2024},
  publisher = {IEEE},
  selected = {true}
}

@inproceedings{esmaeili2023holonic,
  abbr = {AAMAS},
  bibtex_show = {false},
  author = {Esmaeili, Ahmad and Ghorrati, Zahra and Matson, Eric T.},
  title = {Holonic Learning: A Flexible Agent-based Distributed Machine Learning Framework},
  year = {2024},
  isbn = {9798400704864},
  publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
  address = {Richland, SC},
  booktitle = {Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems},
  pages = {525–533},
  numpages = {9},
  location = {Auckland, New Zealand},
  series = {AAMAS '24},
  selected = {true}
}

@article{esmaeili2023agentbased,
  abbr = {Systems},
  bibtex_show = {false},
  title = {Agent-based Collaborative Random Search for Hyper-parameter Tuning and Global Function Optimization},
  author = {Esmaeili, Ahmad and Ghorrati, Zahra and Matson, Eric T.},
  journal = {Systems},
  volume = {11},
  year = {2023},
  number = {5},
  article-number = {228},
  issn = {2079-8954},
  selected = {true}
}

@article{esmaeili2022hamlet,
  abbr = {ACM TAAS},
  bibtex_show = {false},
  title = {HAMLET: A Hierarchical Agent-based Machine Learning Platform},
  author = {Esmaeili, A. and Gallager, J. C. and Springer, J. A. and Matson, E. T.},
  journal = {ACM Transactions on Autonomous and Adaptive Systems, },
  %abstract = {Hierarchical Multi-agent Systems provide convenient and relevant ways to analyze, model, and simulate complex systems composed of a large number of entities that interact at different levels of abstraction. In this article, we introduce HAMLET (Hierarchical Agent-based Machine LEarning plaTform), a hybrid machine learning platform based on hierarchical multi-agent systems, to facilitate the research and democratization of geographically and/or locally distributed machine learning entities. The proposed system models machine learning solutions as a hypergraph and autonomously sets up a multi-level structure of heterogeneous agents based on their innate capabilities and learned skills. HAMLET aids the design and management of machine learning systems and provides analytical capabilities for research communities to assess the existing and/or new algorithms/datasets through flexible and customizable queries. The proposed hybrid machine learning platform does not assume restrictions on the type of learning algorithms/datasets and is theoretically proven to be sound and complete with polynomial computational requirements. Additionally, it is examined empirically on 120 training and 4 generalized batch testing tasks performed on 24 machine learning algorithms and 9 standard datasets. The provided experimental results not only establish confidence in the platform’s consistency and correctness but also demonstrate its testing and analytical capacity},
  issue_date = {December 2021},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  volume = {16},
  number = {3-4},
  issn = {1556-4665},
  year = {2022},
  month = jul,
  articleno = {9},
  numpages = {46},
  %code = {https://github.com/aesmaeili/HAMLET},
  selected = {true}
}