# Supervised Stochastic Gradient Algorithms for Multi-Trial Source Separation

arXiv: https://arxiv.org/abs/2508.20618 · HTML: https://mlsanity.com/paper/2508.20618/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [Deep Variational Generative Models for Audio-visual Speech Separation](https://mlsanity.com/paper/2008.07191/) (2020)
- [SPEED: Scalable Preprocessing of EEG Data for Self-Supervised Learning](https://mlsanity.com/paper/2408.08065/) (2024)
- [Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing](https://mlsanity.com/paper/2202.02951/) (2022)
- [MultiView Independent Component Analysis with Delays](https://mlsanity.com/paper/2312.00484/) (2023)
- [Stochastic algorithms with descent guarantees for ICA](https://mlsanity.com/paper/1805.10054/) (2018)
- [Accelerating Auxiliary Function-based Independent Vector Analysis](https://mlsanity.com/paper/2009.09402/) (2020)
- [Block Coordinate Descent Algorithms for Auxiliary-Function-Based Independent Vector Extraction](https://mlsanity.com/paper/2010.08959/) (2020)
- [Independent Deeply Learned Tensor Analysis for Determined Audio Source Separation](https://mlsanity.com/paper/2106.05529/) (2021)
- [ISS2: An Extension of Iterative Source Steering Algorithm for Majorization-Minimization-Based Independent Vector Analysis](https://mlsanity.com/paper/2202.00875/) (2022)
- [Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis](https://mlsanity.com/paper/2410.23595/) (2024)

## Abstract (arXiv)

We develop a stochastic algorithm for independent component analysis that incorporates multi-trial supervision, which is available in many scientific contexts. The method blends a proximal gradient-type algorithm in the space of invertible matrices with joint learning of a prediction model through backpropagation. We illustrate the proposed algorithm on synthetic and real data experiments. In particular, owing to the additional supervision, we observe an increased success rate of the non-convex optimization and the improved interpretability of the independent components.