# Xtreme Margin: A Tunable Loss Function for Binary Classification Problems

arXiv: https://arxiv.org/abs/2211.00176 · HTML: https://mlsanity.com/paper/2211.00176/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 31 Oct 2022.

## Builds on (most cited first, 16 of 16)

- [Learning Sparse Neural Networks through L0 Regularization](https://mlsanity.com/paper/1712.01312/) (2017)
- [ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions](https://mlsanity.com/paper/2003.03488/) (2020)
- [Variance-Reduced Methods for Machine Learning](https://mlsanity.com/paper/2010.00892/) (2020)
- [A Modern Take on the Bias-Variance Tradeoff in Neural Networks](https://mlsanity.com/paper/1810.08591/) (2018)
- [The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling](https://mlsanity.com/paper/2001.00570/) (2020)
- [Deep vs. shallow networks : An approximation theory perspective](https://mlsanity.com/paper/1608.03287/) (2016)
- [Understanding the Loss Surface of Neural Networks for Binary Classification](https://mlsanity.com/paper/1803.00909/) (2018)
- [A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization](https://mlsanity.com/paper/1906.02314/) (2019)
- [On the Convergence of Shallow Neural Network Training with Randomly Masked Neurons](https://mlsanity.com/paper/2112.02668/) (2021)
- [Learning with Smooth Hinge Losses](https://mlsanity.com/paper/2103.00233/) (2021)
- [Accelerating Deep Convolutional Networks using low-precision and sparsity](https://mlsanity.com/paper/1610.00324/) (2016)
- [A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification](https://mlsanity.com/paper/2009.13935/) (2020)
- [Training Compact Neural Networks with Binary Weights and Low Precision Activations](https://mlsanity.com/paper/1808.02631/) (2018)
- [A generalized quadratic loss for SVM and Deep Neural Networks](https://mlsanity.com/paper/2102.07606/) (2021)
- [Regularization approaches for support vector machines with applications to biomedical data](https://mlsanity.com/paper/1710.10600/) (2017)
- [Optimizing Shallow Networks for Binary Classification](https://mlsanity.com/paper/1905.10161/) (2019)

## Built on by (most cited first, 1 of 1)

- [Fast KVzip: Efficient and Accurate LLM Inference with Gated KV Eviction](https://mlsanity.com/paper/2601.17668/) (2026)

## Abstract (arXiv)

Loss functions drive the optimization of machine learning algorithms. The choice of a loss function can have a significant impact on the training of a model, and how the model learns the data. Binary classification is one of the major pillars of machine learning problems, used in medical imaging to failure detection applications. The most commonly used surrogate loss functions for binary classification include the binary cross-entropy and the hinge loss functions, which form the focus of our study. In this paper, we provide an overview of a novel loss function, the Xtreme Margin loss function. Unlike the binary cross-entropy and the hinge loss functions, this loss function provides researchers and practitioners flexibility with their training process, from maximizing precision and AUC score to maximizing conditional accuracy for a particular class, through tunable hyperparameters λ1 and λ2, i.e., changing their values will alter the training of a model.