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This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
The Conference on Parsimony and Learning (CPAL) is an annual research conference focused on addressing the parsimonious, low-dimensional structures that prevail in machine learning, signal processing, optimization, and beyond. We are interested in theories, algorithms, applications, hardware, and systems, as well as scientific foundations for learning with parsimony.
| Registration Type | What's included | Price |
|---|---|---|
| General Admission/Industry | Access to all academic sessions, coffee breaks | $425 |
| Student Pass (60% off) | Access to all academic sessions, coffee breaks | $170 |
| Tutorials–General Public | $275 | |
| Totorials–Students (60% off) | $110 |
Alison Gopnik, University of California, Berkeley
Doris Tsao, University of California, Berkeley
Fred Kjolstad, Stanford University
Jason Lee, Princeton University
Konrad Kording, University of Pennsylvania
Michael Unser, École Polytechnique Fédérale de Lausanne (EPFL)
Richard Baraniuk, Rice University
Yingyu Liang, University of Hong Kong, University of Wisconsin-Madison
Yuandong Tian, Meta AI Research
Visit the CPAL website for key dates, confirmed speakers, and more information.
