7 papers
Temporal Variational Implicit Neural Representations
Batuhan Koyuncu, Rachael DeVries, Ole Winther +1
We introduce Temporal Variational Implicit Neural Representations (TV-INRs), a probabilistic framework for modeling irregular multivariate time series that enables efficient and ac…
Hölder++: Improving the Quality-Coherence Trade-off in Multimodal VAEs
Huyen Vo, MarÃa MartÃnez-GarcÃa, Isabel Valera
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 s…
Hellinger Multimodal Variational Autoencoders
Huyen Vo, Isabel Valera
Multimodal variational autoencoders (VAEs) are widely used for weakly supervised generative learning with multiple modalities. Predominant methods aggregate unimodal inference dist…
Per-Domain Generalizing Policies: On Learning Efficient and Robust Q-Value Functions (Extended Version with Technical Appendix)
Nicola J. Müller, Moritz Oster, Isabel Valera +2
Learning per-domain generalizing policies is a key challenge in learning for planning. Standard approaches learn state-value functions represented as graph neural networks using su…
Bridging Fairness and Explainability: Can Input-Based Explanations Promote Fairness in Hate Speech Detection?
Yifan Wang, Mayank Jobanputra, Ji-Ung Lee +3
Natural language processing (NLP) models often replicate or amplify social bias from training data, raising concerns about fairness. At the same time, their black-box nature makes…
COPA: Comparing the incomparable in multi-objective model evaluation
Adrián Javaloy, Antonio Vergari, Isabel Valera
In machine learning (ML), we often need to choose one among hundreds of trained ML models at hand, based on various objectives such as accuracy, robustness, fairness or scalability…