Deep learning is the engine powering many of the recent successes of artificial intelligence. These advances stem from a research effort spanning academia and industry; this effort is not limited only to computer science, statistics, and optimization, but also involves neuroscience, physics, and essentially all of the sciences. Despite this intense research activity, however, a satisfactory understanding of deep learning methodology – and, more importantly, its failure modes – continues to elude us. As deep learning enters sensitive domains, such as autonomous driving and healthcare, the need for building this kind of understanding becomes even more pressing.
The goal of this program is to address this need by aligning and focusing theoretical and applied researchers on the common purpose of building empirically-relevant theoretical foundations of deep learning. Specifically, the intention is to identify and make progress on challenges that, on one hand, are key to guiding the real-world use of deep learning and, on the other hand, can be approached using theoretical methodology.
The program will focus on the following four themes:
- Optimization: how and why can deep models be fit to observed (training) data?
- Generalization: why do these trained models work well on similar but unobserved (test) data?
- Robustness: how can we analyze and improve the performance of these models when applied outside their intended conditions?
- Generative methods: how can deep learning be used to model probability distributions?
An integral feature of the program will be bridging activities that aim to strengthen the connections between academia and industry. In particular, in addition to workshops and other weekly events, the program will host weekly bridging days that bring together local Bay Area industry researchers and regular program participants.
sympa [at] lists [dot] simons [dot] berkeley [dot] edu (body: subscribe%20dl2019announcements%40lists.simons.berkeley.edu) (Click here to subscribe to our announcements email list for this program).
Samy Bengio (Google; technical advisor), Aleksander Mądry (Massachusetts Institute of Technology), Elchanan Mossel (Massachusetts Institute of Technology), Matus Telgarsky (University of Illinois, Urbana-Champaign)
List of participants (tentative list, including organizers):
Raman Arora (Johns Hopkins University), Peter Bartlett (UC Berkeley), Misha Belkin (Ohio State University), Shai Ben-David (University of Waterloo), Emma Brunskill (Stanford University), Costis Daskalakis (MIT), Alex Dimakis (University of Texas at Austin), Alyosha Efros (UC Berkeley), Laurent El Ghaoui (UC Berkeley), Dylan Foster (MIT), Suriya Gunasekar (Toyota Technology Institute, Chicago), Boris Hanin (Texas A&M), Moritz Hardt (UC Berkeley), Daniel Hsu (Columbia University), Varun Jog (University of Wisconsin, Madison), Mike Jordan (UC Berkeley), Adam Klivans (University of Texas at Austin), Jason Lee (University of Southern California), Po-Ling Loh (University of Wisconsin, Madison), Tengyu Ma (Stanford University), Aleksander Mądry (MIT), Michael Mahoney (International Computer Science Institute and UC Berkeley), Elchanan Mossel (MIT), Ali Rahimi (Google), Ben Recht (UC Berkeley), Andrej Risteski (MIT), Daniel Roy (University of Toronto), Sushant Sachdeva (University of Toronto), Anant Sahai (UC Berkeley), Colin Sandon (MIT), Peter Sarnak (IAS), Dana Scott (UC Berkeley), Yaron Singer (Harvard University), Aarti Singh (Carnegie Mellon University), Mahdi Soltanolkotabi (University of Southern California), Dawn Song (UC Berkeley), Daniel Soudry (Technion - Israel Institute of Technology), Nati Srebro Bartom (Toyota Technological Institute at Chicago), Nike Sun (MIT), Matus Telgarsky (University of Illinois at Urbana-Champaign), Rene Vidal (Johns Hopkins University), Nisheeth Vishnoi (Yale University), Martin Wainwright (UC Berkeley), Rachel Ward (University of Texas at Austin), Bin Yu (UC Berkeley)
Yossi Arjevani (Weizmann Institute of Science), Yu Bai (Stanford University), Soheil Feizi (University of Maryland, College Park), Surbhi Goel (University of Texas at Austin), Quanquan Gu (University of California, Los Angeles), Pritish Kamath (MIT), Qi Lei (University of Texas at Austin), Behnam Neyshabur (New York University), Quynh Nguyen (Saarland University), Gintare Karolina Roy (University of Cambridge)
Visiting Graduate Students and Postdocs:
Bolton Bailey (University of Illinois Urbana-Champaign), Logan Engstrom (MIT), Ruiqi Gao (University of Southern California), Andrew Ilyas (MIT), Ruhui Jin (University of Texas at Austin), Matt Jordan (University of Texas at Austin), Stefani Karp (Carnegie Mellon University), Frederic Koehler (MIT), Omar Montasser (TTI-Chicago), Adit Radhakrishnan (MIT), Sujit Rao (MIT), Shibani Santurkar (MIT), Dimitris Tsipras (MIT), Kiran Vodrahalli (Columbia University), Colin Wei (Stanford University), Shirley Wu (University of Texas at Austin), Ruicheng Xian (University of Illinois Urbana-Champaign)
List of weekly visitors:
Anima Anandkumar (California Institute of Technology), Yasaman Bahri (Google Brain), Samy Bengio (Google), Paul Christiano (OpenAI), Inderjit Dhillon (Amazon), Vitaly Feldman (Google Brain), T.S. Jayram (IBM Almaden Research), Tomer Koren (Google Research), Ming-Yu Liu (Nvidia), Philip Long (Google Brain), Nimrod Meggido (IBM Almaden Research), Ofer Meshi (Google), Ilya Mironov (Google Brain), Hossein Mobahi (Google), Jakub Pachocki (OpenAI), Rina Panigrahy (Google Brain), Maithra Raghu (Google Brain), Sam Schoenholz (Google Brain), Hanie Sedghi (Google Brain), Szymon Sidor (OpenAI), Yoram Singer (Google Brain), Jascha Sohl-Dickstein (Google Brain), Kunal Talwar (Google), Laura Zaremba (Groq)
Those interested in participating in this program should send an email to the organizers at this dl2019 [at] lists [dot] simons [dot] berkeley [dot] edu (at this address).