MLGym: A New Framework and Benchmark for Advancing AI Research Agents
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...
31 Okt 20251h 28min
Leverage Foundational Models for Black-Box Optimization
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 optimizati...
22 Sep 202556min
Nyckel - Building an AutoML Startup
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. Ch...
7 Mars 20251h 20min
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How
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 c...
8 Aug 202453min
Discovering Temporally-Aware Reinforcement Learning Algorithms
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-learni...
24 Juni 202451min
X Hacking: The Threat of Misguided AutoML
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...
27 Maj 202454min
Introduction To New Co-Host, Theresa Eimer
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 episod...
27 Maj 202413min
