[24] Martin Arjovsky - Out Of Distribution Generalization In Machine Learning The Thesis Review podcast

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[24] Martin Arjovsky - Out of Distribution Generalization in Machine Learning

4y ago 1:02:48

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Content provided by The Thesis Review and Sean Welleck. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Thesis Review and Sean Welleck or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Martin Arjovsky is a postdoctoral researcher at INRIA. His research focuses on generative modeling, generalization, and exploration in RL. Martin's PhD thesis is titled "Out of Distribution Generalization in Machine Learning", which he completed in 2019 at New York University. We discuss his work on the influential Wasserstein GAN early in his PhD, then discuss his thesis work on out-of-distribution generalization which focused on causal invariance and invariant risk minimization. Episode notes: https://cs.nyu.edu/~welleck/episode24.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview

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49 episodes

#Science #Thesis Review #Sean Welleck

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[24] Martin Arjovsky - Out of Distribution Generalization in Machine Learning

The Thesis Review

published 4y ago

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Content provided by The Thesis Review and Sean Welleck. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Thesis Review and Sean Welleck or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Martin Arjovsky is a postdoctoral researcher at INRIA. His research focuses on generative modeling, generalization, and exploration in RL. Martin's PhD thesis is titled "Out of Distribution Generalization in Machine Learning", which he completed in 2019 at New York University. We discuss his work on the influential Wasserstein GAN early in his PhD, then discuss his thesis work on out-of-distribution generalization which focused on causal invariance and invariant risk minimization. Episode notes: https://cs.nyu.edu/~welleck/episode24.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview

… continue reading

49 episodes

#Science #Thesis Review #Sean Welleck

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