# Multi-view Information Bottleneck Without Variational Approximation

arXiv: https://arxiv.org/abs/2204.10530 · HTML: https://mlsanity.com/paper/2204.10530/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 22 Apr 2022.

## Builds on (most cited first, 12 of 12)

- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018)
- [Learning Robust Representations via Multi-View Information Bottleneck](https://mlsanity.com/paper/2002.07017/) (2020)
- [On Deep Multi-View Representation Learning: Objectives and Optimization](https://mlsanity.com/paper/1602.01024/) (2016)
- [Information Dropout: Learning Optimal Representations Through Noisy Computation](https://mlsanity.com/paper/1611.01353/) (2016)
- [Nonlinear Information Bottleneck](https://mlsanity.com/paper/1705.02436/) (2017)
- [Learning Representations for Neural Network-Based Classification Using the Information Bottleneck Principle](https://mlsanity.com/paper/1802.09766/) (2018)
- [Reconsidering Representation Alignment for Multi-view Clustering](https://mlsanity.com/paper/2103.07738/) (2021)
- [A Variational Information Bottleneck Approach to Multi-Omics Data Integration](https://mlsanity.com/paper/2102.03014/) (2021)
- [Distributed Variational Representation Learning](https://mlsanity.com/paper/1807.04193/) (2018)
- [Measuring Dependence with Matrix-based Entropy Functional](https://mlsanity.com/paper/2101.10160/) (2021)
- [Deep Deterministic Information Bottleneck with Matrix-based Entropy Functional](https://mlsanity.com/paper/2102.00533/) (2021)

## Built on by (most cited first, 5 of 5)

- [DiME: Maximizing Mutual Information by a Difference of Matrix-Based Entropies](https://mlsanity.com/paper/2301.08164/) (2023)
- [Discovering Common Information in Multi-view Data](https://mlsanity.com/paper/2406.15043/) (2024)
- [Explainable Multimodal Regression via Information Decomposition](https://mlsanity.com/paper/2512.22102/) (2025)
- [Uncover and Unlearn Nuisances: Agnostic Fully Test-Time Adaptation](https://mlsanity.com/paper/2511.12491/) (2025)
- [SMILE: Self-Explainable Multimodal Information Bottleneck for Medical Diagnosis](https://mlsanity.com/paper/2609.05174/) (2026)

## Abstract (arXiv)

By "intelligently" fusing the complementary information across different views, multi-view learning is able to improve the performance of classification tasks. In this work, we extend the information bottleneck principle to a supervised multi-view learning scenario and use the recently proposed matrix-based R'enyi's α-order entropy functional to optimize the resulting objective directly, without the necessity of variational approximation or adversarial training. Empirical results in both synthetic and real-world datasets suggest that our method enjoys improved robustness to noise and redundant information in each view, especially given limited training samples. Code is available at~urlhttps://github.com/archy666/MEIB.