The special year was led by Sanjeev Arora, who holds a dual appointment as Charles Fitzmorris Professor of Computer Science at Princeton University and Visiting Professor at the IAS, 2017-2020.

Special Year

The program hosted 18 members (some of them postdocs) in each term. They represented various strands of research under the broad umbrella of Optimization, Statistics and Machine Learning. A key goal of the program was to explore how these traditional disciplines are changing as they respond to modern challenges, especially deep learning.

Members we selected in winter 2018 and organization of various workshops and some key colloquia started in spring’19.

Typical week:

Monday: lunch together in the dining hall.

Tues/Wed in Fall; Tues/Thurs in Spring: Group seminars with lunch served. Tues usually featured an outside speaker, or was twice a month even held outside IAS (see joint seminars below).

Mon in Fall; Tues in Spring: Afternoon informal studygroup/ “open blackboard” session. This rotated among a few topics and settled in spring (thanks to good organizing by Samory Kpotufe of Columbia) on a study of how to develop theory for domain adaptation.

Of course, SY members also would meet daily over cookies at the famous afternoon tea in Fuld Hall.

The seminar series was organized fantastically by postdoc Chris Maddison (now faculty at U. Toronto) until March when he left and then postdoc Ke Li took over and also did a fantastic job running things in the online world.

The talks are nicely catalogued here with links to videos: https://www.ias.edu/event-series/special-year-seminar-math?page=1

Monthly Joint Seminar Series:

Two joint seminar series were held joint with talks roughly once per month. Usually Tuesdays, in lieu of the usual seminar talk.

(a) Joint IAS/PNI Seminar (PNI =Princeton Neuroscience Institute) on talks at the interface of Neuroscience and Machine Learning. There were four talks in fall and three in spring before the Covid shutdown. (Unfortunately, no separate webpage for these talks; they are on the list of special year talks above.)

(b) Joint seminar with Princeton Center for Theoretical Science (PCTS), on “Deep learning for physics.”

Sanjeev Arora led off in September with a survey talk on deep learning and its mysteries. Then each month there were two speakers giving one hour talks each. The list of talks is on this calendar https://pcts.princeton.edu/calendar

Other activities

A crowd-sourced e-book on theory of deep learning. Since many of the best experts were in town for various durations in Fall, we decided to do a seminar course at the university on the topic and collect lecture notes as an e-book. The draft is here

A subset of this group also did a 1-week workshop in Barbados in February 2020 hosted by McGill University and the famous Montreal group in deep learning.This was a true meeting of ideas from theory and practice (Bengio and LeCun were two leading lights from practice) and the practitioners were very intrigued by some of the latest theories being developed. It did make them interested in looking further, and LeCun has already used one of the new theory ideas in a new paper (personal communication).

Scientific Advisory Committee: Michael Jordan (UC Berkeley), Yann LeCun (NYU and Facebook), Yoram Singer (Princeton University and Google Brain), Bin Yu (UC Berkeley)

The following researchers confirmed participation:

Fall: Raman Arora, Laura Balzano, Guang Cheng, Yu Cheng, Sanjoy Dasgupta, Simon Du, Rong Ge, Anna Gilbert, Suriya Gunasekar, Chi Jin, Jason Lee, Christopher Maddison, Boaz Nadler, Sushant Sachdeva, Robert Schapire, Zhao Song, Rachel Ward, Jonathan Weed

Spring: Raman Arora, Laura Balzano, Joan Bruna, Costis Daskalakis, Simon Du, Bianca Dumitrascu, Gintare Karolina Dziugaite, Roger Grosse, Adam Klivans, Samory Kpotufe, Ke Li, Christopher Maddison, Daniel Roy, Zhao Song, Mengdi Wang, Jonathan Weed, Bin Yu,

Researchers from industry who were unable to commit for a semester were welcome for shorter visits.

Theoretical Machine Learning Group at Princeton University