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13th Annual Machine Learning Symposium

13th Annual Machine Learning Symposium

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

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.

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
Deadline:
0
days
left

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



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

Symposium Adjourns

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