Workshop on Machine Learning for Network Data

January 29, 2019

Registration has closed.


The goal of this workshop is to discuss our incipient understanding of how we can take advantage of the underlying irregular structure of a graph signal to produce transformations that take advantage of the signal's internal symmetries. In particular, we will cover advances on graph signal processing, graph neural networks, and graph scattering transforms:

The State of the Art in Machine Learning. Machine learning is at a crucial juncture where the success achieved in some problems has opened up the possibility of developing a new industry. To make this possibility a reality, what are the challenges we need to overcome in the next decade? The processing of signals supported in irregular domains is one of these fundamental challenges but in this workshop we want to discuss what other advances are needed to push machine learning forward.

Graph Signal Processing. Graph signal processing is an emerging field whose goal is to extend classical signal processing tools to signals supported on graphs. Central to this effort are generalizations of Fourier transforms, wavelets, and convolutions to irregular domains. Graph Signal Processing has had remarkable success in classical processing tasks such as filtering, systems identification, and sampling. How does the field need to evolve to address the higher cognitive questions that are typical of machine learning?

Graph Neural Networks. Graph neural networks are layered architectures akin to Convolutional Neural Networks that use graph convolutions in lieu of standard convolutions. There are several architectures that have been proposed in the literature that build on different possible ways in which convolutions can be generalized. Naturally, graph neural networks lag behind convolutional neural networks in the richness of their architectures and their application domains. How can we expand the reach of graph neural networks into new application domains? How can we expand the range of architectures to process signals supported on graphs?

Graph Scattering Transforms. Why are convolutional neural networks useful information processing architectures? Scattering transforms provide an answer to this question in the form of the notion of Lipschitz stability with respect to deformations. Graph scattering transforms have been introduced as an attempt to answer the analogous questions for graph neural networks. While preliminary stability results have been established, our understanding of stability properties of graph neural networks remains limited. How can we further our understanding of stability properties of graph neural networks? What other properties of graph neural networks are important to understand their behavior?


9:00am - 9:10am: Arrival

9:10am - 10:00am: Yann LeCun, New York University, Facebook

10:00am - 10:50am: Brian Sadler, Army Research Laboratory

10:50am - 12:10pm: Session 1- Fundamental properties

Joan Bruna, New York University

Risi Kondor, University of Chicago

Matthew Hirn, Michigan State University

Gilad Lerman, University of Minnesota

12:10pm - 1:00pm: Lunch and Breakout Discussions

1:00pm - 2:20pm: Session 2- Architectures for information processing on graphs and manifolds

Alejandro Ribeiro, University of Pennsylvania

Le Song, Georgia Tech

Kostas Daniilidis, University of Pennsylvania

Anima Anandkumar, Caltech

2:20pm - 3:10pm: Coffee and Breakout Discussions

3:10pm - 5:10pm: Session 3- Network data

Rajesh Ranganath, New York University

Jure Leskovec, Stanford

Aylin Yener, Penn State University

He He, Amazon Web Services

Arthur Szlam, Facebook Research

Vikram Krishnamurthy, Cornell Tech

5:10pm - 6:00pm: Breakout Discussions

6:00pm - 7:30pm: Dinner and Poster Session


Joan Bruna - New York University

Alejandro Ribeiro - University of Pennsylvania


NYU Center for Data Science (CDS)

60 5th Avenue

New York NY 10011

Please proceed to the 7th floor and follow directions to the CDS Open Space.


Washington Square Hotel (closest hotel to the workshop location)

103 Waverly Pl, New York, NY 10011

Reservations Manager: Beverley Harris, 212-777-9515

Park South Hotel

124 E 28th St, New York, NY 10016

Courtyard by Marriott New York Manhattan/Chelsea

135 W 30th St, New York, NY 10001

Club Quarters Hotel, Times Square - Midtown

40 W 45th St, New York, NY 10036

SpringHill Suites by Marriott New York Midtown Manhattan/Fifth Avenue

25 W 37th St, New York, NY 10018

Residence Inn New York Manhattan/Central Park

1717 Broadway, New York, NY 10019


Rick Blum, Lehigh

Addison Bohannon, Army Research Laboratory

Ceyhun Eksin, Texas A&M

Yonina Eldar, Technion-Israel Institute of Technology

Nageen Himayat, Intel

Purush Iyer, Army Research Office

Soummya Kar, Carnegie Mellon University

Alec Koppel, Army Research Laboratory

Jelena Kovacevic, Carnegie Mellon University

Mauro Magionni, Johns Hopkins University

Gonzalo Mateos, Rochester

Navid Naderializadeh, Intel

Kumar Vijay Mishra, Army Research Laboratory

Mike Rabbat, Facebook Research

Gesualdo Scutari, Purdue

Santiago Segarra, Rice

Eric Vanden-Eijnden, New York University

Dongmian Zou, University of Minnesota