Past Events

Alma Mater

Monday, May 15, 2017 - 5:00pm
Columbia University in the City of New York
New York, NY 10027
United States

Students graduating from Columbia Engineering are recognized for their achievements: After remarks and award presentations have concluded, graduates process across the stage as their names are read, are photographed, and receive their class pins.

Thursday, May 11, 2017 - 6:00pm to 7:00pm
Schapiro Hall (CEPSR) Davis Auditorium
Columbia University
New York, NY 10027
United States

Improving Health-Care: Challenges and Opportunities for Reinforcement Learning

Joelle Pineau, Associate Professor of Computer Science McGill University

Thursday, May 11, 2017 - 4:00pm to 5:30pm
Schapiro Hall (CEPSR) 750 (Costa Engineering Commons)
Columbia University
New York, NY 10027
United States

Columbia Data Science Institute Industry Innovation Seminars

(Talk 2 of 2)


Peter Marx, Vice President, Advanced Projects, GE Digital, Adjunct Professor, USC

Thursday, May 11, 2017 - 4:00pm to 5:30pm
Schapiro Hall (CEPSR) 750 (Costa Engineering Commons)
Columbia University
New York, NY 10027
United States

Columbia Data Science Institute Industry Innovation Seminars

(Talk 1 of 2)


Peter Tu,Senior Principal Scientist, GE Global Research

Tuesday, May 9, 2017 - 6:00pm to 7:00pm
International Affairs Building
420 W. 118th St.
New York, NY 10027
United States
The Transformation of the National Statistical System in the Era of Digital Data ? Without a Roadmap
 
Friday, May 5, 2017 - 8:30am to 7:00pm
Columbia University Italian Academy
1161 Amsterdam Avenue
New York, NY 10027
United States

Global Digital Futures Policy Forum 2017 at Columbia University

Thursday, May 4, 2017 - 4:30pm to 5:30pm
Columbia University in the City of New York
New York, NY 10027
United States

Recent Advances in Post-Selection Statistical Inference

Robert Tibshirani, Professor of Biomedical Data Science, and Statistics Stanford University

Thursday, May 4, 2017 (All day)
Columbia University
New York, NY 10027
United States

Colloquium Series: Dr. Robert Schapire
Principal Researcher, Microsoft Research (NYC)

We study the general problem of how to learn through experience to make intelligent decisions.  In this setting, called the contextual bandits problem, the learner must repeatedly decide which action to take in response to an observed context, and is then permitted to observe the received reward, but only for the chosen action.  The goal is to learn through experience to behave nearly as well as the best policy (or decision rule) in some possibly very large and rich space of candidate policies.  Previous approaches to this problem were all highly inefficient and often extremely complicated.  In this work, we present a fast and simple algorithm that learns to behave as well as the best policy at a rate that is (almost) statistically optimal.  Our approach assumes access to a kind of “oracle” (or subroutine) for classification learning problems which can be used to select policies; in practice, most off-the-shelf classification algorithms could be used for this purpose.  Our algorithm makes very modest use of the oracle, which it calls far less than once per round, on average, a huge improvement over previous methods.  These properties suggest this may be the most practical contextual bandits algorithm among all existing approaches that are provably effective for general policy classes.

This is joint work with Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford and Lihong Li. 

Wednesday, May 3, 2017 - 2:00pm to 4:00pm
United States

The Data Science Institute's Centers for Data, Media & Society (formerly New Media) + Health Analytics will be holding a poster session, Wednesday, May 3rd from 2PM to 4PM in 407 Mudd featuring poster presentations and demonstrations related t

Thursday, April 27, 2017 - 6:00pm to 7:00pm
Columbia Business School
Uris 332
New York, NY 10027
United States

A panel of 4 hedge funds that are using Big Data and Machine Learning to invest, moderated by BAML PB Consulting.

Pages

Back to Top