The AutoML Podcast AutoML Media

Designing algorithms by hand is hard, so Chris Lu and Matthew Jackson talk about how to meta-learn them for reinforcement learning. Many of the concepts in this episode are interesting to meta-learning approaches as a whole, though: "how expressive can we be and still perform well?", "how can we get the necessary data to generalize?" and "how do we make the resulting algorithm easy to apply in practice?" are problems that come up for any learning-based approach to AutoML and some of the...

AutoML can be a tool for good, but there are pitfalls along the way. Rahul Sharma and David Selby tell us about how AutoML systems can be used to give us false impressions about explainability metrics of ML systems - maliciously, but also on accident. While this episode isn't talking about a new exciting AutoML method, it can tell us a lot about what can go wrong in applying AutoML and what we should think about when we build tools for ML novices to use.

In today's episode, we're introducing the very special Theresa Eimer to the show. Theresa will be taking over the hosting of many of the future episodes. Theresa has already recorded multiple episodes and we are stoked to air those shortly.We also spend a few moments explaining my relative absence in the last few months (since the war in the middle east erupted) and what I'm up to now.Theresa, we are all so excited to be doing this together!To learn more about Theresa,Follow her on Twitter he...

Today we're talking with Nick Erickson from AutoGluon.We discuss AutoGluon's fascinating origin story, its unique point of view, the science and engineering behind some of its unique contributions, Amazon's Machine Learning University, AutoGluon's multi-layer stack ensembler in all its detail, their feature preprocessing pipeline, their feature type inference, their adaptive approach to early stopping, controlling for inference speeds, the different multi-modal architectures, the ML culture a...

Today we're talking with Joseph Giovanelli about his work on integrating logic and argumentation into AutoML systems.Joseph is a PhD student at the University of Bologna. He was more recently in Hannover working on ethics and fairness with Marius’ team.The paper he published presents his framework, HAMLET, which stands for Human-centric AutoML via Logic and Argumentation. It allows a user to iteratively specify constraints in a formal manner and, once defined, those constraints become logical...

Today we're talking with Caitlin Owen, a post-doc at the University of Otago about her work on error decomposition.She recently published a paper titled "Towards Explainable AutoML Using Error Decomposition" about how a more granular view of the components of error can lead the construction of better AutoML systems. Read her paper here: https://link.springer.com/chapter/10.1007/978-3-031-22695-3_13Follow her on Twitter here: @CaitAshfordOwenConnect with her on LinkedIn here: https://www.linke...