August 2022 - Bnc-faculty-all-list

Your meeting recap - Webinar by Prof. on David I. Inouye "Unifying and Advancing the Science of Deep Distribution Alignment"
by Fred from Fireflies 30 Aug '22

30 Aug '22

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Webinar by Prof. on David I. Inouye "Unifying and Advancing the Science of Deep Distribution Alignment"
by Gupta, Sumeet Kumar 25 Aug '22

25 Aug '22

Unifying and Advancing the Science of Deep Distribution Alignment Prof. David I. Inouye Purdue University Tuesday, August 30, 2022: 4 PM-5 PM Zoom link: https://purdue-edu.zoom.us/j/3164232249 Abstract Distribution alignment has the opposite objective of classification. While classification finds a representation that separates two distributions, alignment finds a representation that brings together two distributions. Alignment has been used in many recent machine learning applications including domain generalization, causal discovery, and fair representation learning. Despite these important applications, distribution alignment research lacks a unified and systematic conceptual framework and has primarily focused on GAN-based adversarial alignment for images. To address this gap, I will present a unifying alignment framework that encompasses alignment concepts, measures, algorithms, and applications. Specifically, I will formalize the definition of distribution alignment, develop novel non-adversarial alignment measures and algorithms, and discuss alignment applications in causal discovery and domain generalization. Ultimately, this work aims to advance the science of distribution alignment to enable the next generation of contextually aware and robust AI systems. Bio Prof. David I. Inouye is an assistant professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University. He leads the Probabilistic and Understandable Machine Learning Lab, which focuses on the fundamentals of distribution alignment, probabilistic models, and explainable AI. More recently, he is interested in distribution alignment including new alignment algorithms, measures, and applications such as causality and domain generalization. On the explainable AI side, he is interested in distribution shift explanations and tractable uncertainty quantification. Previously, he was a postdoc at Carnegie Mellon University working with Prof. Pradeep Ravikumar. He completed his Computer Science PhD at The University of Texas at Austin in 2017 advised by Prof. Inderjit Dhillon and Prof. Pradeep Ravikumar. He was awarded the NSF Graduate Research Fellowship (NSF GRFP) Host Sumeet Kumar Gupta, guptask(a)purdue.edu, 765 494 3484

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Canceled: Webinar by Prof. David Inouye (Purdue) on "Unifying and Advancing the Science of Deep Distribution Alignment"
by Gupta, Sumeet Kumar 25 Aug '22

25 Aug '22

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Webinar by Prof. David Inouye (Purdue) on "Unifying and Advancing the Science of Deep Distribution Alignment"
by Gupta, Sumeet Kumar 25 Aug '22

25 Aug '22

Unifying and Advancing the Science of Deep Distribution Alignment Prof. David I. Inouye Purdue University Tuesday, August 30, 2022: 4 PM-5 PM Zoom link: https://purdue-edu.zoom.us/j/3164232249 Abstract Distribution alignment has the opposite objective of classification. While classification finds a representation that separates two distributions, alignment finds a representation that brings together two distributions. Alignment has been used in many recent machine learning applications including domain generalization, causal discovery, and fair representation learning. Despite these important applications, distribution alignment research lacks a unified and systematic conceptual framework and has primarily focused on GAN-based adversarial alignment for images. To address this gap, I will present a unifying alignment framework that encompasses alignment concepts, measures, algorithms, and applications. Specifically, I will formalize the definition of distribution alignment, develop novel non-adversarial alignment measures and algorithms, and discuss alignment applications in causal discovery and domain generalization. Ultimately, this work aims to advance the science of distribution alignment to enable the next generation of contextually aware and robust AI systems. Bio Prof. David I. Inouye is an assistant professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University. He leads the Probabilistic and Understandable Machine Learning Lab, which focuses on the fundamentals of distribution alignment, probabilistic models, and explainable AI. More recently, he is interested in distribution alignment including new alignment algorithms, measures, and applications such as causality and domain generalization. On the explainable AI side, he is interested in distribution shift explanations and tractable uncertainty quantification. Previously, he was a postdoc at Carnegie Mellon University working with Prof. Pradeep Ravikumar. He completed his Computer Science PhD at The University of Texas at Austin in 2017 advised by Prof. Inderjit Dhillon and Prof. Pradeep Ravikumar. He was awarded the NSF Graduate Research Fellowship (NSF GRFP) Host Sumeet Kumar Gupta, guptask(a)purdue.edu, 765 494 3484 ________________________________________________________________________________ Microsoft Teams meeting Join on your computer or mobile app Click here to join the meeting<https://teams.microsoft.com/l/meetup-join/19%3ameeting_MjJlNjRlYmQtMTc4Ny00…> Meeting ID: 226 572 305 065 Passcode: 5zUnQM Download Teams<https://www.microsoft.com/en-us/microsoft-teams/download-app> | Join on the web<https://www.microsoft.com/microsoft-teams/join-a-meeting> Join with a video conferencing device purdue(a)m.webex.com Video Conference ID: 115 134 392 0 Alternate VTC instructions<https://www.webex.com/msteams?confid=1151343920&tenantkey=purdue&domain=m.w…> Learn More<https://aka.ms/JoinTeamsMeeting> | Meeting options<https://teams.microsoft.com/meetingOptions/?organizerId=c2039060-5561-407c-…> ________________________________________________________________________________

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