Yesterday, Yudong Luo presented his NeurIPS paper "Uncertainty-Aware RL for Risk-Sensitive Player Evaluation in Sports Game" at Uncertainty in AI reading group !

https://uncertainty-reading-group.github.io/2022-01-09-talk/

Co-author: Guiliang Liu, Oliver Schulte, Pascal Poupart

https://www.youtube.com/watch?v=QCAVcKnxbV0

You can sample nodes for scalable #GNN #training. But how do you do #scalable #inference?

In our latest paper (Oral #LogConference
) we introduce influence-based mini-batching (#IBMB) for both fast inference and training, achieving up to 130x and 17x speedups, respectively!

1/8 in 🧵

Nikita Durasov presented his NeurIPS paper "Masksembles for Uncertainty Estimation" yesterday at Uncertainty in AI reading group. Check out the recording !

https://uncertainty-reading-group.github.io/2021-12-12-talk/

Co-author: Timur Bagautdinov, Pierre Baque, Pascal Fua

https://www.youtube.com/watch?v=dL5R7gzBPEc

#Introduction

Hi, I’m Aleks :wave:

I’m faculty at the CISPA Helmholtz Center for Information Security where I lead the group on trustworthy #AI.

I’m broadly interested in #Trustworthy #MachineLearning, that is models and algorithms that are not only accurate or efficient but also robust, privacy-preserving, fair, uncertainty-aware, and interpretable. I have a special focus on graph-based models such as graph neural networks.

I'm happy to be here. Looking forward to great discussions 🎉

#introduction

Hi all!

I'm a research scientist joining #Google Research in a few weeks. I've written my PhD thesis at TU Munich, and interned at #DeepMind and #FAIR.

I've done research on #MachineLearning for graphs and molecules, but want to focus more on safety aspects in the future.

I am currently visiting #Berkeley, and will soon be based in #Zurich.

#introduction

I am a Ph.D. student in #MachineLearning. My research interests cover #uncertainty / #robustness in machine learning, #hierarchical / #causal inference, and #efficient machine learning :)