
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)
Registration
Member
By 02/08/2019
$90
After 02/08/2019
$130
Nonmember Academia, Faculty, etc.
By 02/08/2019
$180
After 02/08/2019
$260
Nonmember Corporate, Other
By 02/08/2019
$250
After 02/08/2019
$350
Nonmember Not for Profit
By 02/08/2019
$180
After 02/08/2019
$260
Nonmember Student, Undergrad, Grad, Fellow
By 02/08/2019
$100
After 02/08/2019
$145
Member Student, Post-Doc, Fellow
By 02/08/2019
$50
After 02/08/2019
$70
Earlybird Registration:
0
days
left
Bronze Sponsor
Academy Friends
Prize Sponsors
Promotional Partners
Friday
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
Speaker
Aaron Roth, PhD
University of Pennsylvania
10:50 AM
Audience Q&A
Spotlight Talks: Session 1
11:05 AM
Learning with Reflective Likelihoods
Speaker
Adji B. Dieng
Columbia University
11:10 AM
Uniform Convergence of Gradients for Non-Convex Learning and Optimization
Speaker
Ayush Sekhari
Cornell University
11:15 AM
Learning to Bid without Knowing Your Value
Speaker
Chara Podimata
Harvard University
11:20 AM
Efficient Dictionary Learning with Gradient Descent
Speaker
Dar Gilboa
Columbia University
11:25 AM
Distributed Learning with Sublinear Communication
Speaker
Dylan Foster
MIT
11:30 AM
Networking Break and Poster Viewing
Keynote Address 2
12:20 PM
Towards Coaching from Demonstration
Speaker
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
Speaker
Jalaj Bhandari
Columbia University
2:35 PM
Attribute-Efficient Learning of Monomials over Highly-Correlated Variables
Speaker
Kiran Vodrahalli
Columbia University
2:40 PM
High Probability Bounds for Sketching Tensor Products
Speaker
Michela Meister
Google
2:45 PM
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks
Speaker
Nadav Cohen
Princeton University
2:50 PM
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
Speaker
Wei Hu
Princeton University
Keynote Address 3
2:55 PM
Integrating Domain-Knowledge into Deep Learning
Speaker
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
Speaker
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