AutoML is dead an LLMs have killed it? MLGym is a benchmark and framework testing this theory. Roberta Raileanu and Deepak Nathani discuss how well current LLMs are doing at solving ML tasks, what the biggest roadblocks are, and what that means for AutoML generally.

Check out the paper: https://arxiv.org/pdf/2502.14499

More on Roberta: https://rraileanu.github.io/

More on Deepak: https://dnathani.net/

Where and how can we use foundation models in AutoML? Richard Song, researcher at Google DeepMind, has some answers. Starting off from his position paper on leveraging foundation models for optimization, we chat about what makes foundation models valuable for AutoML, how the next steps could look like, but also why the community is not currently embracing the topic as much as it could.

Paper Link: https://arxiv.org/abs/2405.03547

Richard's website: https://xingyousong.github.io/

Oscar Beijbom is talking about what it's like to run an AutoML startup: Nyckel. Beyond that, we chat about the differences between academia and industry, what truly matters in application and more.

Check out Nyckel at: https://www.nyckel.com/

Colin White, head of research at Abacus AI, takes us on a tour of Neural Architecture Search: its origins, important paradigms and the future of NAS in the age of LLMs. If you're looking for a broad overview of NAS, this is the podcast for you!

There are so many great foundation models in many different domains - but how do you choose one for your specific problem? And how can you best finetune it? Sebastian Pineda has an answer: Quicktune can help select the best model and tune it for specific use cases. Listen to find out when this will be a Huggingface feature and if hyperparameter optimization is even important in finetuning models (spoiler: very much so)!

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 topics we dive into.

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 here: https://twitter.com/The_Eimer
Connect with her on LinkedIn here: https://www.linkedin.com/in/theresa-eimer-a724b5b0/

As you'll hear in the episode, she's also one of the co-organizers of COSEAL, which you can learn more about here: https://www.coseal.net/