1 citations · 1 across the 5 of their papers we have counts for
5 papers
How to Approximate Inference with Subtractive Mixture Models
Lena Zellinger, Nicola Branchini, Lennert De Smet +3
Classical mixture models (MMs) are widely used tractable proposals for approximate inference settings such as variational inference (VI) and importance sampling (IS). Recently, mix…
Even More Guarantees for Variational Inference in the Presence of Symmetries
Lena Zellinger, Antonio Vergari
When approximating an intractable density via variational inference (VI) the variational family is typically chosen as a simple parametric family that very likely does not contain…
Scalable Expectation Estimation with Subtractive Mixture Models
Lena Zellinger, Nicola Branchini, Víctor Elvira +1
Many Monte Carlo (MC) and importance sampling (IS) methods use mixture models (MMs) for their simplicity and ability to capture multimodal distributions. Recently, subtractive mixt…
Counterfactual Reasoning with Knowledge Graph Embeddings
Lena Zellinger, Andreas Stephan, Benjamin Roth
Knowledge graph embeddings (KGEs) were originally developed to infer true but missing facts in incomplete knowledge repositories. In this paper, we link knowledge graph completion…
Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal
Anastasiia Sedova, Lena Zellinger, Benjamin Roth
An accurate and substantial dataset is essential for training a reliable and well-performing model. However, even manually annotated datasets contain label errors, not to mention a…