Multi-Agent Automated Machine Learning

• URL: http://arxiv.org/abs/2210.09084v1
• Date: Mon, 17 Oct 2022 13:32:59 GMT
• Title: Multi-Agent Automated Machine Learning
• Authors: Zhaozhi Wang, Kefan Su, Jian Zhang, Huizhu Jia, Qixiang Ye, Xiaodong Xie, and Zongqing Lu
• Abstract summary: We propose multi-agent automated machine learning (MA2ML) to handle joint optimization of modules in automated machine learning (AutoML) MA2ML explicitly assigns credit to each agent according to its marginal contribution to enhance cooperation among modules, and incorporates off-policy learning to improve search efficiency. Experiments show that MA2ML yields the state-of-the-art top-1 accuracy on ImageNet under constraints of computational cost.
• Score: 54.14038920246645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose multi-agent automated machine learning (MA2ML) with the aim to effectively handle joint optimization of modules in automated machine learning (AutoML). MA2ML takes each machine learning module, such as data augmentation (AUG), neural architecture search (NAS), or hyper-parameters (HPO), as an agent and the final performance as the reward, to formulate a multi-agent reinforcement learning problem. MA2ML explicitly assigns credit to each agent according to its marginal contribution to enhance cooperation among modules, and incorporates off-policy learning to improve search efficiency. Theoretically, MA2ML guarantees monotonic improvement of joint optimization. Extensive experiments show that MA2ML yields the state-of-the-art top-1 accuracy on ImageNet under constraints of computational cost, e.g., $79.7\%/80.5\%$ with FLOPs fewer than 600M/800M. Extensive ablation studies verify the benefits of credit assignment and off-policy learning of MA2ML.

Related papers

• SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning [14.702694298483445]
  Tree-Search Enhanced LLM Agents (SELA) is an agent-based system that leverages Monte Carlo Tree Search (MCTS) to optimize the AutoML process. SELA represents pipeline configurations as trees, enabling agents to conduct experiments intelligently and iteratively refine their strategies. In an extensive evaluation across 20 machine learning datasets, we compare the performance of traditional and agent-based AutoML methods.
  arXiv  Detail & Related papers  (2024-10-22T17:56:08Z)
• AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML [56.565200973244146]
  Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline. Recent works have started exploiting large language models (LLM) to lessen such burden. This paper proposes AutoML-Agent, a novel multi-agent framework tailored for full-pipeline AutoML.
  arXiv  Detail & Related papers  (2024-10-03T20:01:09Z)
• Position: A Call to Action for a Human-Centered AutoML Paradigm [83.78883610871867]
  Automated machine learning (AutoML) was formed around the fundamental objectives of automatically and efficiently configuring machine learning (ML) We argue that a key to unlocking AutoML's full potential lies in addressing the currently underexplored aspect of user interaction with AutoML systems.
  arXiv  Detail & Related papers  (2024-06-05T15:05:24Z)
• AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision Tasks [39.71649832548044]
  AutoMMLab is a general-purpose LLM-empowered AutoML system that follows user's language instructions. The proposed AutoMMLab system effectively employs LLMs as the bridge to connect AutoML and OpenMMLab community. Experiments show that our AutoMMLab system is versatile and covers a wide range of mainstream tasks.
  arXiv  Detail & Related papers  (2024-02-23T14:38:19Z)
• Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm [8.461537684562776]
  A multi-microgrid (MMG) system consists of multiple renewable energy microgrids belonging to different operating entities. This paper proposes a MMG collaborative optimization scheduling model based on a multi-agent centralized training distributed execution framework.
  arXiv  Detail & Related papers  (2023-04-01T22:44:52Z)
• Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning [56.17603785248675]
  Model-agnostic meta-learning (MAML) has become a popular research area. Existing MAML algorithms rely on the episode' idea by sampling a few tasks and data points to update the meta-model at each iteration. This paper proposes memory-based algorithms for MAML that converge with vanishing error.
  arXiv  Detail & Related papers  (2021-06-09T08:47:58Z)
• MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning [61.28547338576706]
  Population-based multi-agent reinforcement learning (PB-MARL) refers to the series of methods nested with reinforcement learning (RL) algorithms. We present MALib, a scalable and efficient computing framework for PB-MARL.
  arXiv  Detail & Related papers  (2021-06-05T03:27:08Z)
• Softmax with Regularization: Better Value Estimation in Multi-Agent Reinforcement Learning [72.28520951105207]
  Overestimation in $Q$-learning is an important problem that has been extensively studied in single-agent reinforcement learning. We propose a novel regularization-based update scheme that penalizes large joint action-values deviating from a baseline. We show that our method provides a consistent performance improvement on a set of challenging StarCraft II micromanagement tasks.
  arXiv  Detail & Related papers  (2021-03-22T14:18:39Z)
• MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models [4.84279798426797]
  Multi-robot systems can benefit from reinforcement learning (RL) algorithms that learn behaviours in a small number of trials. We present a novel multi-agent model-based RL algorithm: Multi-Agent Model-Based Policy Optimization (MAMBPO)
  arXiv  Detail & Related papers  (2021-03-05T13:37:23Z)
• Robusta: Robust AutoML for Feature Selection via Reinforcement Learning [24.24652530951966]
  We propose the first robust AutoML framework, Robusta--based on reinforcement learning (RL) We show that the framework is able to improve the model robustness by up to 22% while maintaining competitive accuracy on benign samples.
  arXiv  Detail & Related papers  (2021-01-15T03:12:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.