> From: "'The New York Academy of Sciences'" <n...@nyas.org>
> Date: October 16, 2012 12:34:05 PM EDT
> To: jeb...@cs.columbia.edu
> Subject: Final Agenda - Machine Learning Symposium this Friday, October 19 at the Academy

> 7th Annual Machine Learning Symposium

> Friday, October 19, 2012
> 9:30 AM - 5:00 PM
> Location: The New York Academy of Sciences

> This symposium features keynote speakers in applied and theoretical machine learning as well as "spotlight" talks selected from poster abstract submissions.

> AGENDA: Friday October 19, 2012

> 9:30 AM - Breakfast & Poster Set-up

> 10:00 AM - Opening Remarks
> Jamie Kass, PhD, The New York Academy of Sciences

> Tribute to David L. Waltz
> Tony Jebara, PhD, Columbia University

> 10:10 AM - Keynote Talk - Problem of Empirical Inference in Machine Learning and Philosophy of Science
> Vladimir Vapnik, PhD, Columbia University and NEC Labs

> 11:05 AM - Spotlight Talks

> Majorization for Conditional Random Fields and Latent Likelihoods
> Anna Choromanska, Columbia University

> Realtime Online Spatiotemporal Topics for Navigation Summaries
> Yogesh Girdhar, McGill University

> Scaling Up Mixed-Membership Stochastic Blockmodels to Massive Networks
> Prem Gopalan, Princeton University

> Place Models for Sparse Location Prediction
> Berk Kapicioglu, Princeton University

> Efficient Time Series Classification with Multivariate Similarity Kernels
> Pavel P. Kuksa, NEC Labs

> 11:30 AM - Networking and Poster Session

> 12:20 PM - Keynote Talk - Large-scale model selection problems and computational oracle inequalities
> Peter L. Bartlett, PhD, University of California, Berkeley

> 1:10 PM - Networking Lunch

> 2:30 PM - Spotlight Talks

> Simulation, Learning and Optimization Techniques in Watson's Jeopardy! Game Strategies
> Jonathan Lenchner, IBM T.J. Watson Research Center

> Compact Hyperplane Hashing with Bilinear Functions
> Wei Liu, Columbia University

> Collaborative Denoising of Multi-Subject fMRI Data
> Alexander Lorbert, Princeton University

> MAP Inference in Chains using Column Generation
> Alexandre Tachard Passos, University of Massachusetts

> Sparse Reinforcement Learning via Efficient First-order Optimization Methods
> Zhiwei (Tony) Qin, Columbia University

> 3:00 PM - Keynote Talk — Learning matrix decomposition structures
> William T. Freeman, PhD, Massachusetts Institute of Technology

> 3:45 PM - Spotlight Talks

> Capturing Lexical Variation in Topic Models with Inverse Regression
> Maxim Rabinovich, Princeton University

> Improving Training Speed of Deep Belief Networks for Large Speech Tasks
> Tara N. Sainath, IBM T.J. Watson Research Center

> Adaptive Learning Rates for Stochastic Gradients
> Tom Schaul, Courant Institute, NYU

> Tradeoffs in Improved Screening of Lasso Problems
> Yun Wang, Princeton University

> Online Learning with Pairwise Loss Functions
> Yuyang Wang, Tufts University

> 4:10 PM - Networking and Poster Session

> 4:50 PM - Student Award Winner Announcement & Closing Remarks

> 5:00 PM - End of Machine Learning Symposium

> 5:15 PM - Machine Learning Careers in NYC Startups
> Presented in collaboration with the Academy's Science Alliance program and hackNY
> - Mike Dewar, Bitly
> - Ky Harlin, Buzzfeed
> - Josh Schwartz, Chartbeat
> - David Rosenberg, Sense Networks
> - Jeroen Janssens, Visual Revenue

> 7:00 PM - End of Program

> REGISTRATION:

> Member     $25
> Student/Postdoc Member     $10
> Nonmember (Academia)     $60
> Nonmember (Corporate)     $80
> Nonmember (Non-profit)     $60
> Nonmember (Student / Postdoc / Resident / Fellow)     $40

> Click to register

> LOCATION:

> The New York Academy of Sciences
> 7 World Trade Center
> 250 Greenwich St, 40th Floor
> New York, NY 10007

> Map & Directions

> PRESENTED BY:
> The Machine Learning Discussion Group at the New York Academy of Sciences

> SPONSORS:

> Bronze Sponsor:
> IBM Research

> Academy Friends:
> AT&T Labs-Research
> Microsoft Research

> ACADEMY EBRIEFINGS:

> The Implications of a Data-driven Built Environment
> Data about energy consumption in buildings can revolutionize energy use and, if analyzed effectively, has the potential to transform buildings' market value. This eBriefing examines 'big data' in the real estate industry and focuses on new systems for energy management.

> The Intersection of Data and Design: Mark Hansen
> With the combination of sensors, math and creativity, data collection and visualization can engage students in new ways of seeing and reporting upon their world. Join Mark Hansen in this eBriefing as he describes his work at the intersection of data and design.

> The Market's Hidden Cards: Algorithmic Trading Strategies
> Algorithmic trading has transformed the finance industry, affecting the way shares are sold in "dark pools" and how portfolio managers and traders interact

> For more information,
> please contact our
> Customer Service Team:
> customerserv...@nyas.org

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