Virginia Smith

发表

SysML: The New Frontier of Machine Learning Systems pdf

Gustavo Alonso, Dan Alistarh, Dimitris S. Papailiopoulos, 2019, ArXiv.

Adding vs. Averaging in Distributed Primal-Dual Optimization pdf

Michael I. Jordan, Martin Jaggi, Peter Richtárik, 2015, ICML.

Distributed Optimization for Non-Strongly Convex Regularizers

Virginia Smith, Dr. Matthias Seeger, 2016 .

MLI: An API for Distributed Machine Learning

Tim Kraska, Ameet Talwalkar, Michael I. Jordan, 2013, 2013 IEEE 13th International Conference on Data Mining.

MLbase : A Distributed Machine Learning Wrapper

Michael I. Jordan, John C. Duchi, Evan R. Sparks, 2012 .

CoCoA: A General Framework for Communication-Efficient Distributed Optimization pdf

Michael I. Jordan, Martin Jaggi, Martin Takác, 2016, J. Mach. Learn. Res..

Distributed optimization with arbitrary local solvers

Michael I. Jordan, Martin Jaggi, Peter Richtárik, 2015, Optim. Methods Softw..

Federated Multi-Task Learning

Ameet Talwalkar, Virginia Smith, Maziar Sanjabi, 2017, NIPS.

Ditto: Fair and Robust Federated Learning Through Personalization

Virginia Smith, Ahmad Beirami, Shengyuan Hu, 2020, ICML.

Federated Learning: Challenges, Methods, and Future Directions

Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith, 2019, IEEE Signal Processing Magazine.

MLSys: The New Frontier of Machine Learning Systems

Christopher De Sa, Michael I. Jordan, Phillip B. Gibbons, 2019, 1904.03257.

Federated Optimization in Heterogeneous Networks

Anit Kumar Sahu, Ameet Talwalkar, Manzil Zaheer, 2018, MLSys.

A Kernel Theory of Modern Data Augmentation

Tri Dao, Christopher De Sa, Christopher Ré, 2018, ICML.

Efficient Augmentation via Data Subsampling pdf

Virginia Smith, Michael Kuchnik, Virginia Smith, 2018, ICLR.

Federated Kernelized Multi-Task Learning

Ameet S. Talwalkar, S. Caldas, Virginia Smith, 2018 .

Distributed Optimization for Non-Strongly

Martin Jaggi, Thomas Hofmann, Virginia Smith, 2016 .

Is Support Set Diversity Necessary for Meta-Learning? pdf

Virginia Smith, Amrith Setlur, Amrith Rajagopal Setlur, 2020, ArXiv.

Ditto: Fair and Robust Federated Learning Through Personalization

Virginia Smith, Tian Li, Shengyuan Hu, 2020, ICML.

Adversarial Unlearning: Reducing Confidence Along Adversarial Directions

Amrith Rajagopal Setlur, S. Levine, Benjamin Eysenbach, 2022, NeurIPS.

Diverse Client Selection for Federated Learning:Submodularity and Convergence Analysis

J. Bilmes, Virginia Smith, Tian Li, 2021 .

On Privacy and Personalization in Cross-Silo Federated Learning

Zhiwei Steven Wu, Virginia Smith, Shengyuan Hu, 2022, 2206.07902.

Motley: Benchmarking Heterogeneity and Personalization in Federated Learning

Zachary B. Charles, Virginia Smith, Zheng Xu, 2022, 2206.09262.

Communication Efﬁcient Federated Learning with Secure Aggregation and Differential Privacy

Christopher A. Choquette-Choo, Phillip B. Gibbons, Ameet S. Talwalkar, 2021 .

Motley: Benchmarking Heterogeneity and Personalization in Federated Learning

Zachary B. Charles, Virginia Smith, Zheng Xu, 2022, ArXiv.

Motley: Benchmarking Heterogeneity and Personalization in Federated Learning

Zachary B. Charles, Virginia Smith, Zheng Xu, 2022, ArXiv.

Motley: Benchmarking Heterogeneity and Personalization in Federated Learning

Zachary B. Charles, Virginia Smith, Zheng Xu, 2022, ArXiv.

Fair Federated Learning via Bounded Group Loss

Zhiwei Steven Wu, Virginia Smith, Shengyuan Hu, 2022, 2203.10190.

On Tilted Losses in Machine Learning: Theory and Applications

Virginia Smith, Maziar Sanjabi, A. Beirami, 2021, J. Mach. Learn. Res..

On Privacy and Personalization in Cross-Silo Federated Learning

Zhiwei Steven Wu, Li-Yu Daisy Liu, Virginia Smith, 2022, NeurIPS.

Private Adaptive Optimization with Side Information

Sashank J. Reddi, M. Zaheer, Virginia Smith, 2022, ICML.

P LUMBER : D IAGNOSING AND R EMOVING P ERFORMANCE B OTTLENECKS IN M ACHINE L EARNING D ATA P IPELINES

Virginia Smith, George Amvrosiadis, Ana Klimovic, 2022 .

Fair Federated Learning via Bounded Group Loss

Zhiwei Steven Wu, Virginia Smith, Shengyuan Hu, 2022, 2203.10190.

On Noisy Evaluation in Federated Hyperparameter Tuning

Ameet S. Talwalkar, Daniel Jiang, Virginia Smith, 2022, arXiv.org.

Enhancing the Privacy of Federated Learning with Sketching pdf

Vyas Sekar, Tian Li, Zaoxing Liu, 2019, ArXiv.

Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches

Virginia Smith, V. Sekar, Tian Li, 2019, ArXiv.

Communication-Efficient Distributed Dual Coordinate Ascent

Thomas Hofmann, Michael I. Jordan, Martin Jaggi, 2014, NIPS.

Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing pdf

Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar, 2021, NeurIPS.

A Field Guide to Federated Optimization pdf

Suhas Diggavi, Ameet Talwalkar, Tara Javidi, 2021, ArXiv.

Provably Fair Federated Learning via Bounded Group Loss

Zhiwei Steven Wu, Virginia Smith, Shengyuan Hu, 2022, ArXiv.

One-Shot Federated Learning pdf

Ameet Talwalkar, Virginia Smith, Neel Guha, 2019, ArXiv.

FedDANE: A Federated Newton-Type Method

Anit Kumar Sahu, Ameet Talwalkar, Manzil Zaheer, 2019, 2019 53rd Asilomar Conference on Signals, Systems, and Computers.

On the Convergence of Federated Optimization in Heterogeneous Networks pdf

Anit Kumar Sahu, Ameet Talwalkar, Tian Li, 2018, ArXiv.

Heterogeneity for the Win: One-Shot Federated Clustering

Virginia Smith, Don Kurian Dennis, Tian Li, 2021, ICML.

Federated Learning as a Network Effects Game

Zhiwei Steven Wu, Virginia Smith, Shengyuan Hu, 2023, ArXiv.

To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning

Yae Jee Cho, Virginia Smith, Tian Li, 2022, ArXiv.

Fair Resource Allocation in Federated Learning pdf

Tian Li, Virginia Smith, Maziar Sanjabi, 2019, ICLR.

Maximizing Global Model Appeal in Federated Learning

Yae Jee Cho, Virginia Smith, Tian Li, 2022, 2205.14840.

Lessons from Chasing Few-Shot Learning Benchmarks: Rethinking the Evaluation of Meta-Learning Methods pdf

Virginia Smith, Amrith Setlur, Amrith Rajagopal Setlur, 2021, ArXiv.

Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution

Amrith Rajagopal Setlur, Virginia Smith, Oscar Li, 2021, NeurIPS.

Federated Multi-Task Learning for Competing Constraints pdf

Tian Li, Virginia Smith, Ahmad Beirami, 2020, ArXiv.

LEAF: A Benchmark for Federated Settings pdf

Sebastian Caldas, Ameet Talwalkar, Tian Li, 2018, ArXiv.

Learning Context-Aware Policies from Multiple Smart Homes via Federated Multi-Task Learning

Srinivasan Seshan, Yuqiong Sun, Tianlong Yu, 2020, 2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI).

Private Multi-Task Learning: Formulation and Applications to Federated Learning

Zhiwei Steven Wu, Virginia Smith, Shengyuan Hu, 2021, Trans. Mach. Learn. Res..

Diverse Client Selection for Federated Learning via Submodular Maximization

J. Bilmes, Virginia Smith, Tian Li, 2022, ICLR.

Tilted Empirical Risk Minimization pdf

Virginia Smith, Ahmad Beirami, Tian Li, 2020, ICLR.

Label Leakage and Protection in Two-party Split Learning

Virginia Smith, Hongyi Zhang, Junyuan Xie, 2021, ICLR.

On Large-Cohort Training for Federated Learning pdf

Virginia Smith, Zhouyuan Huo, Zachary B. Charles, 2021, NeurIPS.

Differentially Private Adaptive Optimization with Delayed Preconditioners

Sashank J. Reddi, Li-Yu Daisy Liu, H. B. McMahan, 2022, ICLR.

Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts

Amrith Rajagopal Setlur, S. Levine, Chelsea Finn, 2023, ICLR.

Progressive Compressed Records: Taking a Byte out of Deep Learning Data

Virginia Smith, George Amvrosiadis, Michael Kuchnik, 2019, Proc. VLDB Endow..

Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines

Virginia Smith, George Amvrosiadis, Ana Klimovic, 2021, MLSys.

Model Aggregation via Good-Enough Model Spaces

Virginia Smith, Neel Guha, Virginia Smith, 2018 .

System-Aware Optimization for Machine Learning at Scale

Virginia Smith, Virginia Smith, 2017 .

MLSys: The New Frontier of Machine Learning Systems

Christopher De Sa, Michael I. Jordan, Phillip B. Gibbons, 2019, 1904.03257.

SysML: The New Frontier of Machine Learning Systems

Christopher De Sa, Michael I. Jordan, Phillip B. Gibbons, 2019, ArXiv.

Noise-Reuse in Online Evolution Strategies

Virginia Smith, Luke Metz, Oscar Li, 2023, ArXiv.

Progressive Knowledge Distillation: Building Ensembles for Efficient Inference

Virginia Smith, D. Dennis, Abhishek Shetty, 2023, ArXiv.

Private Multi-Task Learning: Formulation and Applications to Federated Learning

Zhiwei Steven Wu, Virginia Smith, Shengyuan Hu, 2021, Trans. Mach. Learn. Res..

Federated Optimization in Heterogeneous Networks

Anit Kumar Sahu, M. Zaheer, Virginia Smith, 2018, MLSys.

On Noisy Evaluation in Federated Hyperparameter Tuning

Daniel Jiang, Virginia Smith, Pratiksha Thaker, 2022, ArXiv.

Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies

Virginia Smith, Luke Metz, Oscar Li, 2023, NeurIPS.