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 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/
22 Sept 2025
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 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/
7 Mar 2025
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. Check out Nyckel at: https://www.nyckel.com/
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Public platform figures. Ratings count people who left a rating, not total listeners.
Apple Podcasts (US)
5.0 / 5
12 ratings
Spotify
5.0 / 5
10 ratings
Podcast Authority Score: 37 / 100
A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.
Quality
41
Social presence
0
YouTube
0
Engagement
82
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