Awesome-OL: An Extensible Toolkit for Online Learning

• URL: http://arxiv.org/abs/2507.20144v1
• Date: Sun, 27 Jul 2025 06:34:37 GMT
• Title: Awesome-OL: An Extensible Toolkit for Online Learning
• Authors: Zeyi Liu, Songqiao Hu, Pengyu Han, Jiaming Liu, Xiao He,
• Abstract summary: Awesome-OL is a Python toolkit tailored for online learning research.<n>It provides a unified framework for reproducible comparisons, curated benchmark datasets, and multi-modal visualization.
• Score: 10.84664107715407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, online learning has attracted increasing attention due to its adaptive capability to process streaming and non-stationary data. To facilitate algorithm development and practical deployment in this area, we introduce Awesome-OL, an extensible Python toolkit tailored for online learning research. Awesome-OL integrates state-of-the-art algorithm, which provides a unified framework for reproducible comparisons, curated benchmark datasets, and multi-modal visualization. Built upon the scikit-multiflow open-source infrastructure, Awesome-OL emphasizes user-friendly interactions without compromising research flexibility or extensibility. The source code is publicly available at: https://github.com/liuzy0708/Awesome-OL.

Related papers

• OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning [57.89304342666846]
  We introduce OpenThinkIMG, the first open-source, comprehensive end-to-end framework for tool-augmented LVLMs.<n>We propose a novel reinforcement learning framework V-ToolRL to train LVLMs to learn adaptive policies for invoking external vision tools.<n>V-ToolRL enables LVLMs to autonomously discover optimal tool-usage strategies.
  arXiv  Detail & Related papers  (2025-05-13T14:35:51Z)
• OntoAligner: A Comprehensive Modular and Robust Python Toolkit for Ontology Alignment [0.4499833362998487]
  Ontology Alignment (OA) is fundamental for achieving interoperability across diverse knowledge systems.<n>We present OntoAligner, a comprehensive, modular, and robust Python toolkit for OA alignment.
  arXiv  Detail & Related papers  (2025-03-27T18:28:11Z)
• CapyMOA: Efficient Machine Learning for Data Streams in Python [11.597798770955425]
  CapyMOA is an open-source library for efficient machine learning on streaming data.<n>CapyMOA includes an architecture that allows integration with external frameworks such as MOA and PyTorch.
  arXiv  Detail & Related papers  (2025-02-11T10:20:04Z)
• Empowering Learning: Standalone, Browser-Only Courses for Seamless Education [0.0]
  We introduce PyGlide, a proof-of-concept open-source MOOC delivery system. We provide a user-friendly, step-by-step guide for PyGlide. We showcase PyGlide's practical application in a continuous integration pipeline on GitHub.
  arXiv  Detail & Related papers  (2023-11-12T20:59:52Z)
• Katakomba: Tools and Benchmarks for Data-Driven NetHack [52.0035089982277]
  NetHack is known as the frontier of reinforcement learning research. We argue that there are three major obstacles for adoption: resource-wise, implementation-wise, and benchmark-wise. We develop an open-source library that provides workflow fundamentals familiar to the offline reinforcement learning community.
  arXiv  Detail & Related papers  (2023-06-14T22:50:25Z)
• SequeL: A Continual Learning Library in PyTorch and JAX [50.33956216274694]
  SequeL is a library for Continual Learning that supports both PyTorch and JAX frameworks. It provides a unified interface for a wide range of Continual Learning algorithms, including regularization-based approaches, replay-based approaches, and hybrid approaches. We release SequeL as an open-source library, enabling researchers and developers to easily experiment and extend the library for their own purposes.
  arXiv  Detail & Related papers  (2023-04-21T10:00:22Z)
• PyRelationAL: a python library for active learning research and development [1.0061110876649197]
  Active learning (AL) is a sub-field of ML focused on the development of methods to iteratively and economically acquire data. Here, we introduce PyRelationAL, an open source library for AL research. We describe a modular toolkit based around a two step design methodology for composing pool-based active learning strategies.
  arXiv  Detail & Related papers  (2022-05-23T08:21:21Z)
• RvS: What is Essential for Offline RL via Supervised Learning? [77.91045677562802]
  Recent work has shown that supervised learning alone, without temporal difference (TD) learning, can be remarkably effective for offline RL. In every environment suite we consider simply maximizing likelihood with two-layer feedforward is competitive. They also probe the limits of existing RvS methods, which are comparatively weak on random data.
  arXiv  Detail & Related papers  (2021-12-20T18:55:16Z)
• Online Continual Learning with Natural Distribution Shifts: An Empirical Study with Visual Data [101.6195176510611]
  "Online" continual learning enables evaluating both information retention and online learning efficacy. In online continual learning, each incoming small batch of data is first used for testing and then added to the training set, making the problem truly online. We introduce a new benchmark for online continual visual learning that exhibits large scale and natural distribution shifts.
  arXiv  Detail & Related papers  (2021-08-20T06:17:20Z)
• OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning [107.6943868812716]
  In many practical applications, the situation is reversed: an agent may have access to large amounts of undirected offline experience data, while access to the online environment is severely limited. Our main insight is that, when presented with offline data composed of a variety of behaviors, an effective way to leverage this data is to extract a continuous space of recurring and temporally extended primitive behaviors. In addition to benefiting offline policy optimization, we show that performing offline primitive learning in this way can also be leveraged for improving few-shot imitation learning.
  arXiv  Detail & Related papers  (2020-10-26T14:31:08Z)
• PHOTONAI -- A Python API for Rapid Machine Learning Model Development [2.414341608751139]
  PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences.
  arXiv  Detail & Related papers  (2020-02-13T10:33:05Z)

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.