Summary of How Useful Is Intermittent, Asynchronous Expert Feedback For Bayesian Optimization?, by Agustinus Kristiadi et al.
How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization?
by Agustinus Kristiadi, Felix Strieth-Kalthoff, Sriram Ganapathi Subramanian, Vincent Fortuin, Pascal Poupart, Geoff Pleiss
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the potential of incorporating randomly arriving expert feedback into Bayesian optimization (BO) to improve automated scientific discovery. The authors aim to address the limitations of prior works that require human input at each iteration, which is incompatible with the self-driving lab concept. Instead, they propose a non-blocking approach where expert feedback is gathered and learned through an additional computing thread, allowing for the incorporation of Bayesian preference models into the BO loop. Experimental results on toy and chemistry datasets suggest that even small amounts of intermittent feedback can be useful in improving or constraining BO. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to make computers learn better by using human input, but only a little bit at a time. Right now, computer programs need humans to tell them what to do every step of the way, which isn’t very efficient. The researchers wanted to find a way to get some human input, but not have it slow down the process too much. They came up with an idea where computers can learn from human feedback, even if it’s just a little bit at a time, and use that information to make better decisions. This could be useful in making computer labs more efficient and cost-effective. |
Keywords
» Artificial intelligence » Optimization
