# Multiview Representation Learning via Distributed Joint Latent Space Structuring

arXiv: https://arxiv.org/abs/2504.18455 · HTML: https://mlsanity.com/paper/2504.18455/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 58 of its 100 references that are in the map.
- First posted on arXiv 25 Apr 2025.

## Builds on (most cited first, 30 of 58)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data](https://mlsanity.com/paper/1703.11008/) (2017)
- [A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks](https://mlsanity.com/paper/1707.09564/) (2017)
- [Stronger generalization bounds for deep nets via a compression approach](https://mlsanity.com/paper/1802.05296/) (2018)
- [Information-theoretic analysis of generalization capability of learning algorithms](https://mlsanity.com/paper/1705.07809/) (2017)
- [Learning Robust Representations via Multi-View Information Bottleneck](https://mlsanity.com/paper/2002.07017/) (2020)
- [Reasoning About Generalization via Conditional Mutual Information](https://mlsanity.com/paper/2001.09122/) (2020)
- [Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates](https://mlsanity.com/paper/1911.02151/) (2019)
- [Tightening Mutual Information Based Bounds on Generalization Error](https://mlsanity.com/paper/1901.04609/) (2019)
- [Tighter risk certificates for neural networks](https://mlsanity.com/paper/2007.12911/) (2020)
- [Information-Theoretic Generalization Bounds for Stochastic Gradient Descent](https://mlsanity.com/paper/2102.00931/) (2021)
- [Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors](https://mlsanity.com/paper/1712.09376/) (2017)
- [The Conditional Entropy Bottleneck](https://mlsanity.com/paper/2002.05379/) (2020)
- [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)
- [Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks](https://mlsanity.com/paper/2111.13171/) (2021)
- [PAC-Bayes Analysis Beyond the Usual Bounds](https://mlsanity.com/paper/2006.13057/) (2020)
- [Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks](https://mlsanity.com/paper/2006.09313/) (2020)
- [A Strongly Quasiconvex PAC-Bayesian Bound](https://mlsanity.com/paper/1608.05610/) (2016)
- [Proper Learning, Helly Number, and an Optimal SVM Bound](https://mlsanity.com/paper/2005.11818/) (2020)
- [Information-theoretic generalization bounds for black-box learning algorithms](https://mlsanity.com/paper/2110.01584/) (2021)
- [Generalization Error Bounds Via R'enyi-, f-Divergences and Maximal Leakage](https://mlsanity.com/paper/1912.01439/) (2019)
- [In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors](https://mlsanity.com/paper/1912.04265/) (2019)
- [Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks](https://mlsanity.com/paper/2106.03795/) (2021)
- [On Information Plane Analyses of Neural Network Classifiers -- A Review](https://mlsanity.com/paper/2003.09671/) (2020)
- [Caveats for information bottleneck in deterministic scenarios](https://mlsanity.com/paper/1808.07593/) (2018)
- [Generalization bounds via distillation](https://mlsanity.com/paper/2104.05641/) (2021)

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

- [Information-theoretic Generalization Analysis for VQ-VAEs: A Role of Latent Variables](https://mlsanity.com/paper/2505.19470/) (2025)

## Abstract (arXiv)

We study distributed multiview representation learning, a problem in which K clients each observe a distinct but possibly statistically correlated view. The clients independently extract local representations from their views, which are then used by a central decoder for joint target estimation. One central difficulty is that, since the clients are not allowed to communicate with each other, they must autonomously decide what to encode. We study this coordination problem from a generalization error perspective. For both classification and regression tasks, we derive novel generalization bounds expressed in terms of the Minimum Description Length (MDL) of the joint latent variables across all views and across both training and test datasets. Our structure-aware bound reveals that statistical correlations among the extracted representations tighten the bound, providing theoretical grounding for the empirically observed benefits of cross-view feature alignment. Perhaps counterintuitively, our findings imply that encoders may benefit from extracting redundant representations. Motivated by these bounds, we introduce a data-dependent Gaussian product mixture prior that can be learned and applied in a fully distributed manner. The joint structure of this multiview prior captures inter-view dependencies that are typically discarded by marginal-only approaches. Comprehensive experiments across multiple datasets, encoder architectures, numbers of views, and distortion settings demonstrate the effectiveness of our proposed approach.