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/
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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/
Leverage Foundational Models for Black-Box Optimization
The AutoML Podcast
56 minutes
1 month ago
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/
The AutoML Podcast
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/
