Virginia Smith

ORCID

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 93

Top Venues

University of California, Berkeley, USA (PHD)

Education History

University of California, Berkeley, USA

PHD

Publications

Shengyuan Hu, Yiwei Fu, Zhiwei Steven Wu, Virginia Smith
Jogging the Memory of Unlearned Model Through Targeted Relearning Attack. CoRR (2024)
Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi
Maximizing Global Model Appeal in Federated Learning. Trans. Mach. Learn. Res. 2024 (2024)
Steven Kolawole, Don Kurian Dennis, Ameet Talwalkar, Virginia Smith
Revisiting Cascaded Ensembles for Efficient Inference. CoRR (2024)
Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
Fair Federated Learning via Bounded Group Loss. SaTML (2024)
Shengyuan Hu, Saeed Mahloujifar, Virginia Smith, Kamalika Chaudhuri, Chuan Guo
Privacy Amplification for the Gaussian Mechanism via Bounded Support. CoRR (2024)
Ziyue Li, Tian Li, Virginia Smith, Jeff Bilmes, Tianyi Zhou
Many-Objective Multi-Solution Transport. CoRR (2024)
Kevin Kuo, Arian Raje, Kousik Rajesh, Virginia Smith
Federated LoRA with Sparse Communication. CoRR (2024)
Lucio M. Dery, Steven Kolawole, Jean-Francois Kagey, Virginia Smith, Graham Neubig, Ameet Talwalkar
Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes. CoRR (2024)
Aashiq Muhamed, Oscar Li, David Woodruff, Mona Diab, Virginia Smith
Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients. CoRR (2024)
Qi Pang, Shengyuan Hu, Wenting Zheng, Virginia Smith
Attacking LLM Watermarks by Exploiting Their Strengths. CoRR (2024)
Pratiksha Thaker, Yash Maurya, Virginia Smith
Guardrail Baselines for Unlearning in LLMs. CoRR (2024)
Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, Aviral Kumar
RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. CoRR (2024)
Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
On Tilted Losses in Machine Learning: Theory and Applications. J. Mach. Learn. Res. 24 (2023)
Shengyuan Hu, Dung Daniel T. Ngo, Shuran Zheng, Virginia Smith, Zhiwei Steven Wu
Federated Learning as a Network Effects Game. CoRR (2023)
Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine
Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. ICLR (2023)
Michael Kuchnik, Virginia Smith, George Amvrosiadis
Validating Large Language Models with ReLM. MLSys (2023)
Shengyuan Hu, Steven Wu, Virginia Smith
Private Multi-Task Learning: Formulation and Applications to Federated Learning. Trans. Mach. Learn. Res. 2023 (2023)
Oscar Li, James Harrison, Jascha Sohl-Dickstein, Virginia Smith, Luke Metz
Noise-Reuse in Online Evolution Strategies. CoRR (2023)
Tian Li, Manzil Zaheer, Ken Liu, Sashank J. Reddi, Hugh Brendan McMahan, Virginia Smith
Differentially Private Adaptive Optimization with Delayed Preconditioners. ICLR (2023)
Pratiksha Thaker, Amrith Setlur, Zhiwei Steven Wu, Virginia Smith
Leveraging Public Representations for Private Transfer Learning. CoRR (2023)
Oscar Li, James Harrison, Jascha Sohl-Dickstein, Virginia Smith, Luke Metz
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies. NeurIPS (2023)
Sizhe Zhang, Achira Amur, Emma Olson, Peleg Kremer, Xun Jiao, Virginia Smith, Bridget Wadzuk
Leveraging Machine Learning to Understand Green Stormwater Infrastructure Performance Risks. GHTC (2023)
Saurabh Garg, Amrith Setlur, Zachary C. Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. NeurIPS (2023)
Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith
On Noisy Evaluation in Federated Hyperparameter Tuning. MLSys (2023)
Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine
Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. CoRR (2023)
Don Kurian Dennis, Abhishek Shetty, Anish Sevekari, Kazuhito Koishida, Virginia Smith
Progressive Knowledge Distillation: Building Ensembles for Efficient Inference. CoRR (2023)
Saurabh Garg, Amrith Setlur, Zachary Chase Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. CoRR (2023)
Don Kurian Dennis, Abhishek Shetty, Anish Prasad Sevekari, Kazuhito Koishida, Virginia Smith
Progressive Ensemble Distillation: Building Ensembles for Efficient Inference. NeurIPS (2023)
Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
Provably Fair Federated Learning via Bounded Group Loss. CoRR (2022)
Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. NeurIPS (2022)
Michael Kuchnik, Virginia Smith, George Amvrosiadis
Validating Large Language Models with ReLM. CoRR (2022)
Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith
Private Adaptive Optimization with Side Information. CoRR (2022)
Ken Ziyu Liu, Shengyuan Hu, Steven Wu, Virginia Smith
On Privacy and Personalization in Cross-Silo Federated Learning. NeurIPS (2022)
Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, Jeff A. Bilmes
Diverse Client Selection for Federated Learning via Submodular Maximization. ICLR (2022)
Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith
On Noisy Evaluation in Federated Hyperparameter Tuning. CoRR (2022)
Tian Li, Manzil Zaheer, Ken Ziyu Liu, Sashank J. Reddi, H. Brendan McMahan, Virginia Smith
Differentially Private Adaptive Optimization with Delayed Preconditioners. CoRR (2022)
Ziyu Liu, Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
On Privacy and Personalization in Cross-Silo Federated Learning. CoRR (2022)
Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi
To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning. CoRR (2022)
Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, Chong Wang
Label Leakage and Protection in Two-party Split Learning. ICLR (2022)
Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith
Private Adaptive Optimization with Side information. ICML (2022)
Michael Kuchnik, Ana Klimovic, Jiri Simsa, Virginia Smith, George Amvrosiadis
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines. MLSys (2022)
Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ziyu Liu, Zheng Xu, Virginia Smith
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning. CoRR (2022)
Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. CoRR (2022)
Don Kurian Dennis, Tian Li, Virginia Smith
Heterogeneity for the Win: One-Shot Federated Clustering. ICML (2021)
Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
Tilted Empirical Risk Minimization. ICLR (2021)
Amrith Setlur, Oscar Li, Virginia Smith
Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution. NeurIPS (2021)
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu
A Field Guide to Federated Optimization. CoRR (2021)
Amrith Setlur, Oscar Li, Virginia Smith
Lessons from Chasing Few-Shot Learning Benchmarks: Rethinking the Evaluation of Meta-Learning Methods. CoRR (2021)
Michael Kuchnik, Ana Klimovic, Jiri Simsa, George Amvrosiadis, Virginia Smith
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines. CoRR (2021)
Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
Private Multi-Task Learning: Formulation and Applications to Federated Learning. CoRR (2021)
Angela Cotugno, Virginia Smith, Tracy Baker, Raghavan Srinivasan
A Framework for Calculating Peak Discharge and Flood Inundation in Ungauged Urban Watersheds Using Remotely Sensed Precipitation Data: A Case Study in Freetown, Sierra Leone. Remote. Sens. 13 (19) (2021)
Don Kurian Dennis, Tian Li, Virginia Smith
Heterogeneity for the Win: One-Shot Federated Clustering. CoRR (2021)
Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, Chong Wang
Label Leakage and Protection in Two-party Split Learning. CoRR (2021)
Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. NeurIPS (2021)
Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith
Ditto: Fair and Robust Federated Learning Through Personalization. ICML (2021)
Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith
On Large-Cohort Training for Federated Learning. NeurIPS (2021)
Michael Kuchnik, George Amvrosiadis, Virginia Smith
Progressive Compressed Records: Taking a Byte out of Deep Learning Data. Proc. VLDB Endow. 14 (11) (2021)
Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. CoRR (2021)
Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
On Tilted Losses in Machine Learning: Theory and Applications. CoRR (2021)
Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith
On Large-Cohort Training for Federated Learning. CoRR (2021)
Amrith Setlur, Oscar Li, Virginia Smith
Is Support Set Diversity Necessary for Meta-Learning? CoRR (2020)
Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
Tilted Empirical Risk Minimization. CoRR (2020)
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
FedDANE: A Federated Newton-Type Method. CoRR (2020)
Tian Li, Maziar Sanjabi, Ahmad Beirami, Virginia Smith
Fair Resource Allocation in Federated Learning. ICLR (2020)
Tianlong Yu, Tian Li, Yuqiong Sun, Susanta Nanda, Virginia Smith, Vyas Sekar, Srinivasan Seshan
Learning Context-Aware Policies from Multiple Smart Homes via Federated Multi-Task Learning. IoTDI (2020)
Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith
Federated Multi-Task Learning for Competing Constraints. CoRR (2020)
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith
Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Process. Mag. 37 (3) (2020)
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
Federated Optimization in Heterogeneous Networks. MLSys (2020)
Neel Guha, Ameet Talwalkar, Virginia Smith
One-Shot Federated Learning. CoRR (2019)
Michael Kuchnik, Virginia Smith
Efficient Augmentation via Data Subsampling. ICLR (Poster) (2019)
Zaoxing Liu, Tian Li, Virginia Smith, Vyas Sekar
Enhancing the Privacy of Federated Learning with Sketching. CoRR (2019)
Tian Li, Zaoxing Liu, Vyas Sekar, Virginia Smith
Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches. CoRR (2019)
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
FedDANE: A Federated Newton-Type Method. ACSSC (2019)
Michael Kuchnik, George Amvrosiadis, Virginia Smith
Progressive Compressed Records: Taking a Byte out of Deep Learning Data. CoRR (2019)
Tian Li, Maziar Sanjabi, Virginia Smith
Fair Resource Allocation in Federated Learning. CoRR (2019)
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan R. Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
SysML: The New Frontier of Machine Learning Systems. CoRR (2019)
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith
Federated Learning: Challenges, Methods, and Future Directions. CoRR (2019)
Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, Christopher Ré
A Kernel Theory of Modern Data Augmentation. ICML (2019)
Michael Kuchnik, Virginia Smith
Efficient Augmentation via Data Subsampling. CoRR (2018)
Tri Dao, Albert Gu, Alexander J. Ratner, Virginia Smith, Christopher De Sa, Christopher Ré
A Kernel Theory of Modern Data Augmentation. CoRR (2018)
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar
LEAF: A Benchmark for Federated Settings. CoRR (2018)
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith
On the Convergence of Federated Optimization in Heterogeneous Networks. CoRR (2018)
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet Talwalkar
Federated Multi-Task Learning. CoRR (2017)
Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi
CoCoA: A General Framework for Communication-Efficient Distributed Optimization. J. Mach. Learn. Res. 18 (2017)
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet S. Talwalkar
Federated Multi-Task Learning. NIPS (2017)
Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác
Distributed optimization with arbitrary local solvers. Optim. Methods Softw. 32 (4) (2017)
Gustavo Ansaldi Oliva, Marco Aurélio Gerosa, Fabio Kon, Virginia Smith, Dejan S. Milojicic
A Static Change Impact Analysis Approach based on Metrics and Visualizations to Support the Evolution of Workflow Repositories. Int. J. Web Serv. Res. 13 (2) (2016)
Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi
CoCoA: A General Framework for Communication-Efficient Distributed Optimization. CoRR (2016)
Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác
Distributed Optimization with Arbitrary Local Solvers. CoRR (2015)
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác
Adding vs. Averaging in Distributed Primal-Dual Optimization. ICML (2015)
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác
Adding vs. Averaging in Distributed Primal-Dual Optimization. CoRR (2015)
Virginia Smith, Miriam Connor, Isabelle Stanton
Going In-Depth: Finding Longform on the Web. KDD (2015)
Virginia Smith, Simone Forte, Michael I. Jordan, Martin Jaggi
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework. CoRR (2015)
Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan
Communication-Efficient Distributed Dual Coordinate Ascent. CoRR (2014)
Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan
Communication-Efficient Distributed Dual Coordinate Ascent. NIPS (2014)
Virginia Smith, Jitendra Malik, David E. Culler
Classification of sidewalks in street view images. IGCC (2013)
Gustavo Ansaldi Oliva, Marco Aurélio Gerosa, Dejan S. Milojicic, Virginia Smith
A Change Impact Analysis Approach for Workflow Repository Management. ICWS (2013)
Jay Taneja, Virginia Smith, David E. Culler, Catherine Rosenberg
A comparative study of high renewables penetration electricity grids. SmartGridComm (2013)
Evan R. Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska
MLI: An API for Distributed Machine Learning. ICDM (2013)
Evan R. Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska
MLI: An API for Distributed Machine Learning. CoRR (2013)

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Publication Activity (10 Years)

Top Topics

Top Venues

Virginia Smith