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Probably Approximately Correct Learners
Chara Podimata
3 episodes
3 days ago
Welcome to Probably Approximately Correct Learners, a podcast from the Learning Theory Alliance team. In this podcast, we will dive deep into the minds of leading researchers in Machine Learning! Join us for engaging interviews that explore a diverse range of topics—from groundbreaking research findings to the experiences and insights that shape life beyond academia. Whether you're a seasoned expert or just starting your journey in the field, this podcast is your gateway to understanding the evolving landscape of Machine Learning. Tune in and broaden your perspective with each episode!
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Education
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All content for Probably Approximately Correct Learners is the property of Chara Podimata and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
Welcome to Probably Approximately Correct Learners, a podcast from the Learning Theory Alliance team. In this podcast, we will dive deep into the minds of leading researchers in Machine Learning! Join us for engaging interviews that explore a diverse range of topics—from groundbreaking research findings to the experiences and insights that shape life beyond academia. Whether you're a seasoned expert or just starting your journey in the field, this podcast is your gateway to understanding the evolving landscape of Machine Learning. Tune in and broaden your perspective with each episode!
Show more...
Education
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Ep. 2: Clément Canonne
Probably Approximately Correct Learners
40 minutes 28 seconds
4 months ago
Ep. 2: Clément Canonne

Welcome to our second Probably Approximately Correct Learners episode! In this episode, Chara chats with Professor Clément Canonne.


Clément Canonne is a Senior Lecturer in the School of Computer Science of the University of Sydney, an ARC DECRA Fellow, and a 2023 NSW Young Tall Poppy. He obtained his Ph.D. in 2017 from Columbia University, before joining Stanford as a Motwani Postdoctoral Fellow, then IBM Research as a Goldstine Postdoctoral Fellow. His research interests span distribution testing and learning theory; focusing, in particular, on differential privacy, and the computational aspects of learning and statistical inference subject to resource or information constraints. He really likes elephants and wombats.

Probably Approximately Correct Learners
Welcome to Probably Approximately Correct Learners, a podcast from the Learning Theory Alliance team. In this podcast, we will dive deep into the minds of leading researchers in Machine Learning! Join us for engaging interviews that explore a diverse range of topics—from groundbreaking research findings to the experiences and insights that shape life beyond academia. Whether you're a seasoned expert or just starting your journey in the field, this podcast is your gateway to understanding the evolving landscape of Machine Learning. Tune in and broaden your perspective with each episode!