Learning theory
The Learning theory team at Google tackles fundamental learning theory problems significant to Google.
About the team
We are dedicated to advancing the theoretical foundations of machine learning (ML). Our team has extensive expertise in a variety of areas, including learning theory, statistical learning theory, optimization, decision making under uncertainty, reinforcement learning, and theory and algorithms in general. Our mission is twofold: to foster a principled understanding of ML techniques and to leverage this knowledge in designing highly effective algorithms. Ultimately, we aim to deploy these algorithms to achieve significant impact on Google, the wider academic community, and the scientific field of ML as a whole.
Team focus summaries
Featured publications
Some of our locations
Some of our people
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Mehryar Mohri
- Economics and Electronic Commerce
- Education Innovation
- Algorithms and Theory
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Satyen Kale
- Algorithms and Theory
- Machine Intelligence
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Claudio Gentile
- Machine Intelligence
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Christoph Dann
- Algorithms and Theory
- Machine Intelligence
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Manfred K. Warmuth
- Machine Intelligence
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Teodor Vanislavov Marinov
- Algorithms and Theory
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Julian Zimmert
- Algorithms and Theory
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Stefani Karp
- Algorithms and Theory
- Machine Intelligence
