From 2021 to 2026 I worked as an Applied Scientist at Amazon, where I applied machine learning methods to problems in search, product media and reviews.
I received a PhD in Machine Learning from the School of Informatics, University of Edinburgh, advised by Iain Murray. For my PhD I worked on normalizing flows for generative modelling/density estimation, and Bayesian inference for robust predictive models. I completed an applied machine learning internship with Amazon (Search), and a machine learning research internship with Google Research, where I worked on generative modelling of human motion with normalizing flows.
For my MSc thesis I worked on generative video modelling via latent space transitions. Before diving deeper into machine learning, I worked on recommendations at Amazon and studied Computer Science at the University of Manchester, where my final-year project was on evolutionary computation.
Research
Clustering Context in Off-Policy Evaluation
Daniel Guzman Olivares,
Philipp Schmidt,
Jacek Golebiowski,
Artur Bekasov
AISTATS, 2025
[arXiv]
Learning Action Embeddings for Off-Policy Evaluation
Matej Cief,
Jacek Golebiowski,
Philipp Schmidt,
Ziawasch Abedjan,
Artur Bekasov
ECIR, 2024
[arXiv]
Variational Boosted Soft Trees
Tristan Cinquin,
Tammo Rukat,
Philipp Schmidt,
Martin Wistuba,
Artur Bekasov
AISTATS, 2023
[arXiv]
Accurate and Reliable Probabilistic Modeling with High-dimensional Data
Artur Bekasov
PhD thesis
University of Edinburgh, 2022
[Edinburgh Research Archive]
Ordering Dimensions with Nested Dropout Normalizing Flows
Artur Bekasov, Iain Murray
Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, ICML, 2020
Selected for a spotlight
[arXiv] [Code] [Virtual poster talk]
Neural Spline Flows
Conor Durkan*, Artur Bekasov*, Iain Murray, George Papamakarios
NeurIPS, 2019
* Equal contribution
[NeurIPS] [arXiv] [Code]
Cubic-Spline Flows
Conor Durkan*, Artur Bekasov*, Iain Murray, George Papamakarios
Workshop on Invertible Neural Nets and Normalizing Flows, ICML, 2019
* Equal contribution. Selected for a contributed talk
[arXiv] [Code]
Bayesian Adversarial Spheres: Bayesian Inference and Adversarial Examples in a Noiseless Setting
Artur Bekasov, Iain Murray
Bayesian Deep Learning Workshop, NeurIPS, 2018
Selected for a spotlight
[arXiv] [Poster]
See more at my Google Scholar page.
Open Source
See more at my GitHub page.
Teaching
While at the University of Edinburgh, I provided teaching support for:
- Machine Learning and Pattern Recognition
- Probabilistic Modelling and Reasoning
- Computer Programming for Speech and Language
Reviewing
I reviewed for the following conferences:
...and the following workshops:
- Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (ICML)
- Deep Generative Models for Highly Structured Data (ICLR)
- Real-World Constrained and Preference-Aligned Flow and Diffusion-Based Models (ICLR)
Links
Contact
- Email: artur@abksv.me