Virginia Smith

Virginia Smith

I'm an assistant professor in the Machine Learning Department at Carnegie Mellon University, and a courtesy faculty member in the Electrical and Computer Engineering Department. My research interests are in machine learning and optimization, and their interplay with computer systems. Specific topics include: large-scale machine learning, distributed optimization, federated and on-device learning, multi-task learning, transfer learning, and data augmentation.

Prior to CMU, I was a postdoc with Chris Ré at Stanford University. I received my PhD at UC Berkeley, where I worked with Michael I. Jordan and David Culler as a member of the AMPLab.


Federated Learning: Challenges, Methods, and Future Directions
T. Li, A. K. Sahu, A. Talwalkar, V. Smith
Model Aggregation via Good-Enough Model Spaces
N. Guha, V. Smith
Fair Resource Allocation in Federated Learning
T. Li, M. Sanjabi, V. Smith
Federated Optimization for Heterogeneous Networks
T. Li*, A. K. Sahu*, M. Sanjabi, M. Zaheer, A. Talwalkar, V. Smith
LEAF: A Benchmark for Federated Settings
S. Caldas, P. Wu, T. Li, J. Konecny, B. McMahan, V. Smith, A. Talwalkar
Refereed Conference or Journal

A Kernel Theory of Modern Data Augmentation
T. Dao, A. Gu, A. Ratner, V. Smith, C. De Sa, C. Re
International Conference on Machine Learning (ICML '19)
Efficient Augmentation via Data Subsampling
M. Kuchnik, V. Smith
International Conference on Learning Representations (ICLR '19)
CoCoA: A General Framework for Communication-Efficient Distributed Optimization
V. Smith, S. Forte, C. Ma, M. Takac, M. I. Jordan, M. Jaggi
Journal of Machine Learning Research (JMLR), 2018
Federated Multi-Task Learning
V. Smith, C. Chiang*, M. Sanjabi*, A. Talwalkar
Neural Information Processing Systems (NIPS '17)
Distributed Optimization with Arbitrary Local Solvers
C. Ma, J. Konecny, M. Jaggi, V. Smith, M. I. Jordan, P. Richtarik, M. Takac
Optimization Methods and Software, 2017
Going In-Depth: Finding Longform on the Web
V. Smith, M. Connor, I. Stanton
Conference on Knowledge Discovery and Data Mining (KDD '15)
Adding vs. Averaging in Distributed Primal-Dual Optimization
C. Ma*, V. Smith*, M. Jaggi, M. I. Jordan, P. Richtarik, M. Takac
International Conference on Machine Learning (ICML '15)
Communication-Efficient Distributed Dual Coordinate Ascent
M. Jaggi*, V. Smith*, M. Takac, J. Terhorst, S. Krishnan, T. Hofmann, M. I. Jordan
Neural Information Processing Systems (NIPS '14)
MLI: An API for user-friendly distribued machine learning
E. Sparks, A. Talwalkar, V. Smith, X. Pan, J. Gonzalez, T. Kraska, M. I. Jordan, and M. J. Franklin
IEEE International Conference on Data Mining (ICDM '13)
A Comparative Study of High Renewables Penetration Electricity Grids
J. Taneja, V. Smith, D. Culler, and C. Rosenberg
IEEE International Conference on Smart Grid Communications (SmartGridComm '13)
Identifying Models of HVAC Systems Using Semiparametric Regression
A. Aswani, N. Master, J. Taneja, V. Smith, A. Krioukov, D. Culler, and C. Tomlin
Proceedings of the American Control Conference (ACC '12)
Modeling Building Thermal Response to HVAC Zoning
V. Smith, T. Sookoor, and K. Whitehouse
ACM SIGBED Review, 2012
Workshop / Other

SysML: The New Frontier of Machine Learning Systems
One-Shot Federated Learning
N. Guha, A. Talwalkar, V. Smith
Machine Learning on Devices Workshop at Neural Information Processing Systems (NeurIPS '18)
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework
V. Smith, S. Forte, M. I. Jordan, M. Jaggi
ML Systems Workshop at International Conference on Machine Learning (ICML '16)
Classification of Sidewalks in Street View Images
V. Smith, J. Malik, and D. Culler
WiP Workshop at International Green Computing Conference (IGCC '13)
MLbase: A Distributed Machine Learning Wrapper
A. Talwalkar, T. Kraska, R. Griffith, J. Duchi, J. Gonzalez, D. Britz, X. Pan, V. Smith, E. Sparks, A. Wibisono, M. J. Franklin, and M. I. Jordan
Big Learning Workshop at Neural Information Processing Systems (NIPS '12)