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:
Monday, August 21, 2017
8:30 - 9:15 Check-in for pre-registered attendees, late registration, and catered breakfast
Location: Outside of Room 1170
9:15 - 9:30 **Welcome messages from General Chair and Technical Co-Chairs
Waheed U. Bajwa, Rutgers University, Alekh Agarwal, Microsoft, and Alejandro Ribeiro, University of Pennsylvania
Please note this is an earlier start than previously posted.
9:30 - 10:50 Oral Session M1 (Chair: A. Ribeiro)
A Proximal Primal-Dual Algorithm for Decomposing Non-convex Nonsmooth Problems
Mingyi Hong, Iowa State University (40 min.)
Slides Video
Asynchronous Algorithms for Conic Programs, including Optimal, Infeasible, and Unbounded Ones
Wotao Yin, UCLA (40 min.)
Slides Video
10:50 - 11:10 Coffee break
11:10 - 11:50 Oral Session M2 (Chair: A. Ribeiro)
How to Analyze Nonconvex Optimization Algorithms in High Dimensions?
Exact Asymptotics via Exchangeability and Scaling Limits
Yue M. Lu, Harvard University (40 min.)
Video
11:50 - 1:00 Catered lunch
Location: Lobby of the Honors College, 5 Seminary Place, New Brunswick, NJ
1:00 - 1:15 DIMACS Welcome
Fred Roberts, DIMACS, Rutgers University
Video
1:15 - 2:35 Oral Session M3 (Chair: W. Yin)
High-order Methods In Empirical Risk Minimization
Alejandro Ribeiro, University of Pennsylvania (40 min.)
Slides Video
Distributed Approaches to Mirror Descent for Stochastic Learning over
Rate-limited Networks
Matthew Nokleby, Wayne State University (40 min.)
Slides Video
2:35 - 3:20 Solar Eclipse (do not look directly at the sun without eclipse glasses) / Coffee break
Making a pinhole: https://www.wired.com/story/view-the-eclipse-with-this-simple-homemade-gadget/
3:20 - 5:20 Oral Session M4 (Chair: G. Scutari)
Distributed Optimization Algorithms for Networked Systems
Michael Zavlanos, Duke University (40 min.)
Slides Video
Distributed Optimization Over Directed Graphs
Usman Khan, Tufts University (40 min.)
Slides Video
When Cyclic Coordinate Descent Beats Randomized Coordinate Descent
Mert Gurbuzbalaban, Rutgers University (40 min.)
Slides Video
5:45 pm Informal social gathering
Brother Jimmy's Barbeque
5 Easton Avenue
New Brunswick, NJ
Tuesday, August 22, 2017
8:30 - 9:30 Check-in for pre-registered attendees, late registration, and catered breakfast
Location: Outside of Room 1170
9:30 - 10:50 Oral Session T1 (Chair: A. Agarwal)
Federated Learning: Privacy-Preserving Collaborative Machine Learning without Centralized Training Data
Keith Bonawitz, Google (40 min.)
Privacy and Fault-Tolerance for Distributed Optimization
Nitin Vaidya, University of Illinois, Urbana-Champaign (40 min.)
Slides Video
10:50 - 11:20 Coffee break
11:20 - 12:00 Oral Session T2 (Chair: U. Khan)
Fast Distributed Algorithms for Optimization in Time-Varying Graphs
Angelia Nedich, Arizona State University (40 min.)
Slides Video
12:00 - 1:30 Catered lunch
Location: Lobby of the Honors College, 5 Seminary Place, New Brunswick, NJ
1:30 - 3:30 Oral Session T3 (Chair: A. Nedic)
Convergence Rates in Decentralized Optimization
Alex Olshevsky, Boston University (40 min.)
Slides Video
Distributed Resource Allocation with Limited Communication
Na Li, Harvard University (40 min.)
Slides Video
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient
Martin Takac, Lehigh University (40 min.)
Slides Video
3:30 - 4:00 Coffee break
4:00 - 5:45 Poster Session P1 (Chair: W. Bajwa)
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 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
Panico's
103 Church St.
New Brunswick, NJ
Wednesday, August 23, 2017
8:30 - 9:30 Check-in for pre-registered attendees, late registration, and catered breakfast
Location: Outside of Room 1170
9:30 - 10:50 Oral Session W1 (Chair: W. Bajwa)
Consensus and Distributed Inference Rates Using Network Divergence
Anand D. Sarwate, Rutgers University (40 min.)
Slides Video
Distributed Large-scale Optimization via Batch Gradient Tracking
Gesualdo Scutari, Purdue University (40 min.)
10:50 - 11:10 Coffee break
11:10 - 12:30 Oral Session W2 (Chair: A. Sarwate)
Balancing Computation and Communication in Distributed Optimization
Ermin Wei, Northwestern University (40 min.)
Slides Video
Distributed Online Learning in the Wild
Alekh Agarwal, Microsoft (40 min.)
12:30 Concluding remarks and boxed lunch
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Document last modified on August 31, 2017.