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14 Program Highlights »
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Tutorial
Mon Dec 3rd 08:30 -- 10:30 AM @ Room 517 CD
Scalable Bayesian Inference
In Tutorials Session 1
David Dunson
Tutorial
Mon Dec 3rd 08:30 -- 10:30 AM @ Room 220 E
Visualization for Machine Learning
In Tutorials Session 1
Fernanda Viégas · Martin Wattenberg
Tutorial
Mon Dec 3rd 08:30 -- 10:30 AM @ Room 220 CD
Adversarial Robustness: Theory and Practice
In Tutorials Session 1
J. Zico Kolter · Aleksander Madry
Break
Mon Dec 3rd 10:30 -- 11:00 AM @
Coffee Break
Tutorial
Mon Dec 3rd 11:00 AM -- 01:00 PM @ Room 517 CD
Negative Dependence, Stable Polynomials, and All That
In Tutorials Session 2
Suvrit Sra · Stefanie Jegelka
Tutorial
Mon Dec 3rd 11:00 AM -- 01:00 PM @ Room 220 CD
Unsupervised Deep Learning
In Tutorials Session 2
Alex Graves · Marc'Aurelio Ranzato
Tutorial
Mon Dec 3rd 11:00 AM -- 01:00 PM @ Room 220 E
Common Pitfalls for Studying the Human Side of Machine Learning
In Tutorials Session 2
Deirdre Mulligan · Nitin Kohli · Joshua A. Kroll
Tutorial
Mon Dec 3rd 02:30 -- 04:30 PM @ Room 220 E
Statistical Learning Theory: a Hitchhiker's Guide
In Tutorials Session 3
John Shawe-Taylor · Omar Rivasplata
Tutorial
Mon Dec 3rd 02:30 -- 04:30 PM @ Room 517 CD
Counterfactual Inference
In Tutorials Session 3
Susan Athey
Tutorial
Mon Dec 3rd 02:30 -- 04:30 PM @ Room 220 CD
Automatic Machine Learning
In Tutorials Session 3
Frank Hutter · Joaquin Vanschoren
Break
Mon Dec 3rd 04:30 -- 05:00 PM @
Coffee Break
Break
Mon Dec 3rd 05:00 -- 05:30 PM @
Opening Remarks
Invited Talk
Mon Dec 3rd 05:30 -- 06:20 PM @ Room 220 CD
Accountability and Algorithmic Bias: Why Diversity and Inclusion Matters
Laura Gomez
Break
Mon Dec 3rd 06:30 -- 08:30 PM @
Opening Reception