BackboneLearn: A Library for Scaling Mixed-Integer Optimization-Based Machine Learning

• URL: http://arxiv.org/abs/2311.13695v1
• Date: Wed, 22 Nov 2023 21:07:45 GMT
• Title: BackboneLearn: A Library for Scaling Mixed-Integer Optimization-Based Machine Learning
• Authors: Vassilis Digalakis Jr and Christos Ziakas
• Abstract summary: BackboneLearn is a framework for scaling mixed-integer optimization problems with indicator variables to high-dimensional problems. BackboneLearn is built in Python and is user-friendly and easily implementable. The source code of BackboneLearn is available on GitHub.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present BackboneLearn: an open-source software package and framework for scaling mixed-integer optimization (MIO) problems with indicator variables to high-dimensional problems. This optimization paradigm can naturally be used to formulate fundamental problems in interpretable supervised learning (e.g., sparse regression and decision trees), in unsupervised learning (e.g., clustering), and beyond; BackboneLearn solves the aforementioned problems faster than exact methods and with higher accuracy than commonly used heuristics. The package is built in Python and is user-friendly and easily extensible: users can directly implement a backbone algorithm for their MIO problem at hand. The source code of BackboneLearn is available on GitHub (link: https://github.com/chziakas/backbone_learn).

Related papers

• ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription [3.293021585117505]
  ODTLearn is an open-source Python package for learning optimal decision trees. It provides methods for learning optimal decision trees for high-stakes predictive and prescriptive tasks.
  arXiv  Detail & Related papers  (2023-07-28T17:37:47Z)
• 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)
• pyGSL: A Graph Structure Learning Toolkit [14.000763778781547]
  pyGSL is a Python library that provides efficient implementations of state-of-the-art graph structure learning models. pyGSL is written in GPU-friendly ways, allowing one to scale to much larger network tasks.
  arXiv  Detail & Related papers  (2022-11-07T14:23:10Z)
• hyperbox-brain: A Toolbox for Hyperbox-based Machine Learning Algorithms [9.061408029414455]
  hyperbox-brain is an open-source Python library implementing the leading hyperbox-based machine learning algorithms. hyperbox-brain exposes a unified API which closely follows and is compatible with the renowned scikit-learn and numpy toolboxes.
  arXiv  Detail & Related papers  (2022-10-06T06:40:07Z)
• problexity -- an open-source Python library for binary classification problem complexity assessment [0.0]
  The classification problem's complexity assessment is an essential element of many topics in the supervised learning domain. The tools currently available for the academic community, which would enable the calculation of problem complexity measures, are available only as libraries of the C++ and R languages. This paper describes the software module that allows for the estimation of 22 complexity measures for the Python language.
  arXiv  Detail & Related papers  (2022-07-14T07:32:15Z)
• IMBENS: Ensemble Class-imbalanced Learning in Python [26.007498723608155]
  imbens is an open-source Python toolbox for implementing and deploying ensemble learning algorithms on class-imbalanced data. imbens is released under the MIT open-source license and can be installed from Python Package Index (PyPI)
  arXiv  Detail & Related papers  (2021-11-24T20:14:20Z)
• Simple Stochastic and Online Gradient DescentAlgorithms for Pairwise Learning [65.54757265434465]
  Pairwise learning refers to learning tasks where the loss function depends on a pair instances. Online descent (OGD) is a popular approach to handle streaming data in pairwise learning. In this paper, we propose simple and online descent to methods for pairwise learning.
  arXiv  Detail & Related papers  (2021-11-23T18:10:48Z)
• Solo-learn: A Library of Self-supervised Methods for Visual Representation Learning [83.02597612195966]
  solo-learn is a library of self-supervised methods for visual representation learning. Implemented in Python, using Pytorch and Pytorch lightning, the library fits both research and industry needs.
  arXiv  Detail & Related papers  (2021-08-03T22:19:55Z)
• Fast Few-Shot Classification by Few-Iteration Meta-Learning [173.32497326674775]
  We introduce a fast optimization-based meta-learning method for few-shot classification. Our strategy enables important aspects of the base learner objective to be learned during meta-training. We perform a comprehensive experimental analysis, demonstrating the speed and effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-10-01T15:59:31Z)
• BOML: A Modularized Bilevel Optimization Library in Python for Meta Learning [52.90643948602659]
  BOML is a modularized optimization library that unifies several meta-learning algorithms into a common bilevel optimization framework. It provides a hierarchical optimization pipeline together with a variety of iteration modules, which can be used to solve the mainstream categories of meta-learning methods.
  arXiv  Detail & Related papers  (2020-09-28T14:21:55Z)
• Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python [77.33905890197269]
  We describe a new library which implements a unified pathwise coordinate optimization for a variety of sparse learning problems. The library is coded in R++ and has user-friendly sparse experiments.
  arXiv  Detail & Related papers  (2020-06-27T02:39:24Z)

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.