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@inproceedings{nokey,

title = {Holder++: Improving the Quality-Coherence Trade-off in Multimodal VAEs},

author = {Huyen Vo and Marı́a Martı́nez-Garcı́a and Isabel Valera},

url = {https://vothuckhanhhuyen.github.io/assets/pdf/Holder_ICML2026.pdf},

year  = {2026},

date = {2026-03-11},

urldate = {2026-03-11},

abstract = {Existing approaches for multimodal variational autoencoders (VAEs) face a trade-off between generative quality and coherence—i.e., they struggle to generate realistic and diverse samples that, at the same time, are semantically consistent across modalities. A recent work shows that using a simple approximation to Hölder pooling as an aggregation method improves coherence over the SOTA MMVAE+, despite assuming a single shared representation across all modalities. Yet, it slightly compromises sample diversity. Inspired by this insight, we propose Hölder++, a novel multimodal VAE that improves the generative quality-coherence trade-off through: (i) the first implementation of Hölder pooling without any approximation for multimodal VAEs; (ii) an extended architecture that models distinct shared and private (i.e., modality-specific) representations (Hölder+); and (iii) hierarchical inference that further enhances the disentanglement between the shared and private representations (Hölder++). Our experiments corroborate that Hölder++ consistently improves the generative quality-coherence trade-off, yields more structured latent spaces, and learns shared representations that are informative for downstream tasks.},

keywords = {huyen, isabel, maria},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{<LineBreak>vo2026hellinger,

title = {Hellinger Multimodal Variational Autoencoders},

author = {Huyen Thuc Khanh Vo and Isabel Valera},

url = {https://openreview.net/forum?id=mxHyYltMUa},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

booktitle = {The 29th International Conference on Artificial Intelligence and Statistics},

abstract = {Multimodal variational autoencoders (VAEs) are widely used for weakly supervised generative learning with multiple modalities. Predominant methods aggregate unimodal inference distributions using either a product of experts (PoE), a mixture of experts (MoE), or their combinations to approximate the joint posterior. In this work, we revisit multimodal inference through the lens of probabilistic opinion pooling, an optimization-based approach. We start from Hölder pooling with α=0.5, which corresponds to the unique symmetric member of the α-divergence family, and derive a moment-matching approximation, termed Hellinger. We then leverage such an approximation to propose HELVAE, a multimodal VAE that avoids sub-sampling, yielding an efficient yet effective model that: (i) learns more expressive latent representations as additional modalities are observed; and (ii) empirically achieves better trade-offs between generative coherence and quality, outperforming state-of-the-art multimodal VAE models.},

keywords = {huyen, isabel, spotlight},

pubstate = {published},

tppubtype = {inproceedings}

}