Friday, May 1, 202610:00 am - 4:00 pm
Featuring perspectives on how concepts like “learning” and “intelligence” can be formally defined and studied, the program highlights the rich problems, structures, and connections shaping this field. Designed for mathematically-inclined researchers, this workshop welcomes participants who are curious about theoretical machine learning and AI, whether new to the area or looking to deepen their understanding.
This event is part of the Frontiers in Data Science and AI initiative at the Data Science Institute, Columbia University.
REGISTER
REGISTRATION DEADLINE: The Columbia Morningside campus is open to the Columbia community. If you do not have an active CUID, the deadline to register is at 12:00 PM the day before the event.
Symposia Location: Davis Auditorium (CEPSR) – 4th Floor (Campus Level)Address: 530 W 120th St, New York, NY 10027
The symposia will be split into two parts, running from 10:00 AM – 12:00 PM and 1:00 PM – 3:00 PM. The event lunch (12:00 PM) and closing reception (3:00 PM) will take place next door in Mudd 407.
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9:45 AM: Doors Open for Attendees (15 min prior)
10:00 AM – 10:05 AM: Part One Introductions: Daniel Hsu, Associate Professor of Computer Science, Columbia Engineering (5 min)
10:05 AM – 10:55 AM: Presentation: Christos H. Papadimitriou, Donovan Family Professor of Computer Science; Provost’s Senior Faculty Teaching Scholar, Columbia Engineering (40 min talk; 10 min questions) – presenting remotely
10:55 AM – 11:05 AM: Break (10 min)
11:05 AM – 11:55 AM: Presentation: Elisenda Grigsby, Professor, Math Department, Boston College (40 min talk; 10 min questions)
12:00 PM – 1:00 PM: Lunch for Attendees in Mudd 407
1:00 PM: Part Two Introductions: Daniel Hsu, Associate Professor of Computer Science, Columbia Engineering (5 min)
1:05 PM – 1:55 PM: Presentation: Speaker to be announced soon (40 min talk; 10 min questions)
1:55 PM – 2:00 PM: Break (5 min)
2:00 PM – 2:50 PM: Presentation: Joan Bruna Estrach, Professor of Computer Science, Data Science, and Mathematics, Courant Institute, New York University (40 min talk; 10 min questions)
2:50 PM – 3:00 PM: Research Project Discussion: Kriste Krstovski, Associate Research Scientist, Data Science Institute (10 min)
3:00 PM – 4:00 PM: Reception for Attendees in Mudd 407
Listed in order of program:
DSI Frontiers Awardee: Daniel HsuAssociate Professor of Computer Science, Columbia Engineering
Christos H. PapadimitriouDonovan Family Professor of Computer Science; Provost’s Senior Faculty Teaching Scholar, Columbia Engineering
Elisenda GrigsbyProfessor, Math Department, Boston College
Talk Title: The Geometry, Topology, and Combinatorics of Neural Networks
Abstract: Deep neural networks are a class of parameterized functions that have proven remarkably successful at making predictions about unseen data from finite labeled data sets. They do so even in settings when classical intuition suggests that they ought to be overfitting (memorizing) the data. I will begin by advertising one of the theoretical questions animating the field: how do internal symmetries of the models impact their learning dynamics? Along the way I will emphasize the many ways in which geometry, topology, and combinatorics play a role in the field.
Joan Bruna EstrachProfessor of Computer Science, Data Science, and Mathematics, Courant Institute, New York University
Talk Title: A Dynamic Perspective on High-Dimensional Learning
Abstract: Modern machine learning systems surprise us again and again by their ability to extract complex information out of high-dimensional observations, defying our intuition anchored in the curse of dimensionality. In this talk I will describe a mathematical framework aiming to address this challenge, in which measure transport takes centerpiece. I will first illustrate training dynamics and describe how functions with hidden low-dimensional structure can be provably learnt using gradient-descent methods. Then we will illustrate measure transport in the context of generative models, emphasizing their role in inverse problems.
Kriste KrstovskiAssociate Research Scientist, Data Science Institute