NYU Tandon ECE Seminar Series on Modern AI: Arthur Gretton next MONDAY

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Anna Choromanska

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Mar 8, 2024, 1:58:49 PM3/8/24
to Anna Choromanska
Dear All,

The second speaker of the Spring 2024 NYU Tandon ECE Seminar Series on Modern AI is Arthur Gretton from Gatsby Computational Neuroscience Unit. He will speak on the 11th of March at 11.00 am. The event is held in-person and also broadcasted via zoom:


The details of the event are provided below. NYU Tandon is looking forward to seeing you all!!!


Department of Electrical and Computer Engineering

NYU Tandon - Born Anywhere, Made in Brooklyn

Monday, March 11, 2024
Time: 11:00 AM
In-person: 370 Jay Street, Room 1201
Zoom 

Contact: ece-anno...@nyu.edu

Register

Arthur Gretton
Gatsby Computational Neuroscience Unit, Director of the Centre for Computational Statistics and Machine Learning (CSML) at UCL

 

Arthur Gretton is a Professor with the Gatsby Computational Neuroscience Unit, and director of the Centre for Computational Statistics and Machine Learning (CSML) at UCL; and a research scientist at Google Deepmind. His recent research interests include causal inference and representation learning, design and training of generative models, and nonparametric hypothesis testing.
 

Arthur has been an associate editor at IEEE Transactions on Pattern Analysis and Machine Intelligence, an Action Editor for JMLR, a Senior Area Chair for NeurIPS (2018,2021) and ICML  (2022), a member of the COLT Program Committee in 2013, and a member of Royal Statistical Society Research Section Committee since January 2020. Arthur was program co-chair for AISTATS in 2016, tutorials co-chair for ICML 2018, workshops co-chair for ICML 2019, program co-chair for the Dali workshop in 2019, and co-organsier of the Machine Learning Summer School 2019 in London.

Learning to act in noisy contexts using deep proxy learning
 

We consider problem of evaluating the expected outcome of an action or policy, using off-policy observations of user actions, where the relevant context is noisy/anonymized. This scenario might arise due to privacy constraints, data bandwidth restrictions, or intrinsic properties of the setting.
 

We will employ the recently developed tool of proxy causal learning to address this problem. In brief, two noisy views of the context are used: one prior to the user action, and one subsequent to it, and influenced by the action. This pair of views will allow us to recover the average causal effect of an action under reasonable assumptions. As a key benefit of the proxy approach, we need never explicitly model or recover the hidden context. Our implementation employs learned neural net representations for both the action and context, allowing each to be complex and high dimensional (images, text). We demonstrate the deep proxy learning method in a setting where the action is an image, and show that we outperform an autoencoder-based alternative. 



This event is free and open to the public.

The Seminar Series in Modern Artificial Intelligence is held at NYU Tandon School of Engineering and is hosted by the Department of Electrical and Computer Engineering. Organized by Professor Anna Choromanska, the series aims to bring together faculty and students to discuss the most important research trends in the world of AI. The speakers include world-renowned experts whose research is making an immense impact on the development of new machine learning techniques and technologies and helping to build a better, smarter, more-connected world.

 


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NYU Tandon School of Engineering, 1 MetroTech Center, 19th Floor, Brooklyn, NY 11201


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--
Anna Choromanska

Associate Professor

Alfred P. Sloan Fellow

Department of Electrical and Computer Engineering

NYU Tandon School of Engineering

New York University

Room 802

370 Jay Street

New York, NY 11201, USA

Office phone: 646.997.0269

ac5455 at nyu dot edu

achoroma at gmail dot com

https://engineering.nyu.edu/faculty/anna-choromanska


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