Black-Box Variational Inference
Mixture Models, Efficient Learning, and Applications
Time: Mon 2025-06-02 14.00
Location: F3 (Flodis), Lindstedtsvägen 26 & 28, Campus
Language: English
Subject area: Computer Science
Doctoral student: Alexandra Hotti , Beräkningsvetenskap och beräkningsteknik (CST)
Opponent: Associate Professor Maurizio Filippone, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Supervisor: Professor Jens Lagergren, Beräkningsvetenskap och beräkningsteknik (CST)
QC 20250521
Abstract
We advance Black-Box Variational Inference (BBVI) by improving its flexibility, scalability, and applicability to real-world challenges. In Paper I, we demonstrate that integrating mixture-based variational distributions into VAEs—leveraging adaptive importance sampling—enhances posterior expressiveness and mitigates mode collapse in applications such as image and single- cell analysis. Paper II introduces MISVAE, along with two novel ELBO estimators—Some-to-All and Some-to-Some—which enable efficient training with hundreds of mixture components and achieve state-of-the-art performance on the MNIST and Fashion-MNIST datasets. Paper III shifts focus to real-world applications by presenting the Klarna Product Page Dataset, a diverse benchmark for web element nomination, where we achieve strong performance by benchmarking GNNs in combination with GPT-4. Additionally, the dataset has been leveraged in generative modeling tasks, facilitating the learning of latent web page representations and the generation of complex web interfaces using VAEs. Finally, Paper IV provides new smoothness results and gradient variance bounds for BBVI under non-linear scale parameterizations, highlighting advantages in large-data regimes. Collectively, these contributions extend the frontiers of BBVI for tackling high-dimensional, structured data in both theory and practice.