DIMACS Workshop on Distributed Optimization, Information Processing, and Learning

August 21 - 23, 2017
Rutgers Academic Building, West Wing, Room 1170,
15 Seminary Place, Rutgers University, New Brunswick, NJ

Organizing Committee:
Waheed U. Bajwa, (General Chair), Rutgers University, waheed.bajwa at rutgers.edu
Alekh Agarwal, (Technical Co-Chair), Microsoft Research, New York, alekha at microsoft.com
Alejandro Ribeiro, (Technical Co-Chair), University of Pennsylvania, aribeiro at seas.upenn.edu
Presented under the auspices of the DIMACS Special Focus on Information Sharing and Dynamic Data Analysis.

Workshop Program:

This is a list of confirmed speakers:

Preliminary Program Structure

Monday, August 21, 2017 

 8:30 -  9:30  Catered breakfast and Registration
 9:30 -  9:45  Welcome messages

 9:45 - 11:00  Talks 

11:00 - 11:15  Coffee break

11:15 - 12:30  Talks 

12:30 -  2:00  Catered lunch

 2:00  - 3:30  Talks 

 3:30  - 3:45  Coffee break

 3:45 -  5:45  Talks 

 5:45          Informal social activities

Tuesday, August 22, 2017 

 8:30 -  9:30  Catered breakfast and Registration

 9:30 - 11:00  Talks 

11:00 - 11:15  Coffee break

11:15 - 12:30  Talks 

12:30 -  2:00  Catered lunch

 2:00 -  4:00  Talks 

 4:00 -  4:15  Coffee break

 4:15 -  5:45  Poster session 

               Should I Distribute my Machine Learning Training Job?
               Michael Alan Chang, University of California, Berkeley

               Distributed Dictionary Learning over Dynamic Directed Network Topologies
               Amir Daneshmand, Purdue University

               A Decentralized Primal-Dual Quasi-Newton Method with Exact Linear Convergence
               Mark Eisen, University of Pennsylvania

               Private Learning on Networks
               Shripad Gade, University of Illinois at Urbana-Champaign

               Power and Spectrum Optimization for Wireless Autonomous Systems
               Konstantinos Gatsis, University of Pennsylvania

               Distributed Stochastic Coordinate Descent With Non-uniform Sampling
               Mohsen Ghassemi, Rutgers University

               Distributed Zeroth-Order Nonconvex Optimization
               Davood Hajinezhad, Iowa State University

               REPR: Regression-Style Learning by Column Generation
               Ai Kagawa, Rutgers University

               Using LDPC Codes for Computing Large Linear Transforms Distributedly
               Fatemeh Kazemikordasiabi, Rutgers University

               Decentralized Efficient Nonparametric Stochastic Optimization
               Alec Koppel, University of Pennsylvania

               Superlinearly Convergent Asynchronous Distributed Network Newton Method
               Fatemeh Mansoori, Northwestern University

               IQN: An Incremental Quasi-Newton Method with Local Superlinear Convergence Rate
               Aryan Mokhtari, University of Pennsylvania

               Accelerated Distributed Nesterov Gradient Descent
               Guannan Qu, Harvard University

               Oja's Rule for Distributed Principal Component Analysis (PCA)
               Haroon Raja, Rutgers University

               Distributed optimization over directed graphs
               Ran Xin, Tufts University

               Byzantine resilient distributed learning via coordinate descent
               Zhixiong Yang, Rutgers University

 6:30 -  8:30  Workshop banquet in New Brunswick

Wednesdday, August 22, 2017

 8:30 -  9:00  Catered Breakfast and Registration

 9:30 - 11:00  Talks 

11:00 - 11:15  Coffee break

11:15 - 12:45  Talks 

12:45 -  2:00  Catered lunch and concluding remarks

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Document last modified on July 13, 2017.