# Cyanure: An Open-Source Toolbox for Empirical Risk Minimization for Python, C++, and soon more

arXiv: https://arxiv.org/abs/1912.08165 · HTML: https://mlsanity.com/paper/1912.08165/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2019.

## Built on by (most cited first, 16 of 16)

- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Masked Siamese Networks for Label-Efficient Learning](https://mlsanity.com/paper/2204.07141/) (2022)
- [Siamese Image Modeling for Self-Supervised Vision Representation Learning](https://mlsanity.com/paper/2206.01204/) (2022)
- [Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels](https://mlsanity.com/paper/2302.10586/) (2023)
- [Benchopt: Reproducible, efficient and collaborative optimization benchmarks](https://mlsanity.com/paper/2206.13424/) (2022)
- [MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations](https://mlsanity.com/paper/2402.10093/) (2024)
- [Light-weight probing of unsupervised representations for Reinforcement Learning](https://mlsanity.com/paper/2208.12345/) (2022)
- [Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget](https://mlsanity.com/paper/2304.10520/) (2023)
- [MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments](https://mlsanity.com/paper/2307.09361/) (2023)
- [Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual Features](https://mlsanity.com/paper/2407.17486/) (2024)
- [Differentially Private Representation Learning via Image Captioning](https://mlsanity.com/paper/2403.02506/) (2024)
- [No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representations](https://mlsanity.com/paper/2407.10964/) (2024)
- [Beyond Tikhonov: Faster Learning with Self-Concordant Losses via Iterative Regularization](https://mlsanity.com/paper/2106.08855/) (2021)
- [Benchmarking Low-Shot Robustness to Natural Distribution Shifts](https://mlsanity.com/paper/2304.11263/) (2023)
- [Complexity of Inexact Proximal Point Algorithm for minimizing convex functions with Holderian Growth](https://mlsanity.com/paper/2108.04482/) (2021)
- [On Inductive Biases for Machine Learning in Data Constrained Settings](https://mlsanity.com/paper/2302.10692/) (2023)

## Abstract (arXiv)

Cyanure is an open-source C++ software package with a Python interface. The goal of Cyanure is to provide state-of-the-art solvers for learning linear models, based on stochastic variance-reduced stochastic optimization with acceleration mechanisms. Cyanure can handle a large variety of loss functions (logistic, square, squared hinge, multinomial logistic) and regularization functions (l_2, l_1, elastic-net, fused Lasso, multi-task group Lasso). It provides a simple Python API, which is very close to that of scikit-learn, which should be extended to other languages such as R or Matlab in a near future.