Important: Note that this guest talk is on Thursday 10.15-11.00
Title
Tree-based generative modeling with applications in explainable AI
Abstract
We present adversarial random forests, a provably consistent tree-based machine learning method designed for density estimation and generative modeling. Inspired by generative adversarial networks, our method employs a recursive procedure in which trees gradually learn structural properties of the data through alternating rounds of generation and discrimination. We achieve comparable or superior performance to state-of-the-art probabilistic circuits and deep learning models on various tabular data benchmarks while executing about two orders of magnitude faster on average. In this talk, we provide an overview of the methodology, show benchmark results for density estimation and generative modeling, and introduce new methods for conditional sampling. The latter allows applications in missing data imputation and explainable AI, e.g., in conditional feature importance and counterfactual explanations.
Bio
Marvin Wright, Professor of Machine Learning in Statistics, Bremen University and Leibniz Institute
Practical
Integreat guest talks are open for attendance for everybody.
For those unable to attend in person: https://uio.zoom.us/j/62535744013
Point of contacts:
- Organiser and moderator: Professor Ingrid K. Glad, University of Oslo
- Integreat Administrative Leader Maria Dikova, University of Oslo