Click here to learn about Academy events, publications and initiatives around COVID-19.

We are experiencing intermittent technical difficulties. At this time, you may not be able to log in, register for an event, or make a donation via the website. We appreciate your patience, and apologize for any inconvenience this may cause.

Our site is under planned maintenance. At this time, you will not be able to log in, register for an event, or make a donation via the website. We appreciate your patience, and apologize for any inconvenience this may cause.

Support The World's Smartest Network

Help the New York Academy of Sciences bring late-breaking scientific information about the COVID-19 pandemic to global audiences. Please make a tax-deductible gift today.

This site uses cookies.
Learn more.


This website uses cookies. Some of the cookies we use are essential for parts of the website to operate while others offer you a better browsing experience. You give us your permission to use cookies, by continuing to use our website after you have received the cookie notification. To find out more about cookies on this website and how to change your cookie settings, see our Privacy policy and Terms of Use.

We encourage you to learn more about cookies on our site in our Privacy policy and Terms of Use.

13th Annual Machine Learning Symposium

13th Annual Machine Learning Symposium

Friday, March 1, 2019, 9:00 AM - 6:00 PM EST

The New York Academy of Sciences, 7 World Trade Center, 250 Greenwich St Fl 40, New York

Presented By

The New York Academy of Sciences


Machine Learning, a subfield of computer science, involves the development of mathematical algorithms that discover knowledge from specific data sets, and then "learn" from the data in an iterative fashion that allows predictions to be made. Today, Machine Learning has a wide range of applications, including natural language processing, search engine optimization, medical diagnosis and treatment, financial fraud detection, and stock market analysis.

This symposium, the thirteenth in an ongoing series presented by the Machine Learning Discussion Group at the New York Academy of Sciences, will feature Keynote Presentations from leading scientists in both applied and theoretical Machine Learning and Spotlight Talks, a series of short, early career investigator presentations across a variety of topics at the frontier of Machine Learning.

From the event

Aaron Roth's Keynote "The Ethical Algorithm" (livestream)

Russ Salakhutdinov's Keynote "Integrating Domain-Knowledge into Deep Learning" (livestream)

Sham Kakade's Keynote "Curiosity, Intrinsic Motivation, and Provably Efficient Maximum Entropy Exploration" (slideshow PDF)


By 02/08/2019
After 02/08/2019
Nonmember Academia, Faculty, etc.
By 02/08/2019
After 02/08/2019
Nonmember Corporate, Other
By 02/08/2019
After 02/08/2019
Nonmember Not for Profit
By 02/08/2019
After 02/08/2019
Nonmember Student, Undergrad, Grad, Fellow
By 02/08/2019
After 02/08/2019
Member Student, Post-Doc, Fellow
By 02/08/2019
After 02/08/2019
Earlybird Registration:

Scientific Organizing Committee

Alexander Rakhlin, PhD,University of Pennsylvania
Alexander Rakhlin, PhD,
University of Pennsylvania
Corinna Cortes, PhD,Google Research
Corinna Cortes, PhD,
Google Research
Elad Hazan, PhD,Princeton University
Elad Hazan, PhD,
Princeton University
John Langford, PhD,Microsoft Research
John Langford, PhD,
Microsoft Research
Naoki Abe, PhD,IBM Research
Naoki Abe, PhD,
IBM Research
Patrick Haffner, PhD,Interactions Corporation
Patrick Haffner, PhD,
Interactions Corporation
Jennifer L. Costley, PhD,The New York Academy of Sciences
Jennifer L. Costley, PhD,
The New York Academy of Sciences
Mehryar Mohri, PhD,Courant Institute of Mathematical Sciences, New York University
Mehryar Mohri, PhD,
Courant Institute of Mathematical Sciences, New York University
Robert Schapire, PhD,Microsoft Research
Robert Schapire, PhD,
Microsoft Research
Tony Jebara, PhD,
Tony Jebara, PhD,

Columbia University and Netflix


March 01, 2019

9:00 AM

Registration, Continental Breakfast, and Poster Set-up

10:00 AM

Welcome Remarks

Keynote Address 1

10:10 AM

The Ethical Algorithm


Aaron Roth, PhD
University of Pennsylvania
10:50 AM

Audience Q&A

Spotlight Talks: Session 1

11:05 AM

Learning with Reflective Likelihoods


Adji B. Dieng
Columbia University
11:10 AM

Uniform Convergence of Gradients for Non-Convex Learning and Optimization


Ayush Sekhari
Cornell University
11:15 AM

Learning to Bid without Knowing Your Value


Chara Podimata
Harvard University
11:20 AM

Efficient Dictionary Learning with Gradient Descent


Dar Gilboa
Columbia University
11:25 AM

Distributed Learning with Sublinear Communication


Dylan Foster
11:30 AM

Networking Break and Poster Viewing

Keynote Address 2

12:20 PM

Towards Coaching from Demonstration


Emma Brunskill, PhD
Stanford University
1:00 PM

Audience Q&A

1:15 PM

Networking Lunch and Poster Viewing

Spotlight Talks: Session 2

2:30 PM

A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation


Jalaj Bhandari
Columbia University
2:35 PM

Attribute-Efficient Learning of Monomials over Highly-Correlated Variables


Kiran Vodrahalli
Columbia University
2:40 PM

High Probability Bounds for Sketching Tensor Products


Michela Meister
2:45 PM

A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks


Nadav Cohen
Princeton University
2:50 PM

Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks


Wei Hu
Princeton University

Keynote Address 3

2:55 PM

Integrating Domain-Knowledge into Deep Learning


Ruslan Salakhutdinov, PhD
Carnegie Mellon University and Apple
3:35 PM

Audience Q&A

3:50 PM

Networking Break

Keynote Address 4

4:05 PM

Curiosity, Intrinsic Motivation, and Provably Efficient Maximum Entropy Exploration


Sham Kakade. PhD
University of Washington
4:45 PM

Audience Q&A

Closing Remarks and Awards

5:00 PM

Best Poster Presentation

5:05 PM

Spotlight Talk Award Presentation

5:10 PM

Networking Reception

6:00 PM

Symposium Adjourns