Virginia Smith

According to our database, Virginia Smith authored at least 80 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Maximizing Global Model Appeal in Federated Learning.

Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi

Trans. Mach. Learn. Res., 2024

Decoding Dark Matter: Specialized Sparse Autoencoders for Interpreting Rare Concepts in Foundation Models.

Aashiq Muhamed, Mona Diab, Virginia Smith

CoRR, 2024

Position: LLM Unlearning Benchmarks are Weak Measures of Progress.

Pratiksha Thaker, Shengyuan Hu, Neil Kale, Yash Maurya, Zhiwei Steven Wu, Virginia Smith

CoRR, 2024

Revisiting Cascaded Ensembles for Efficient Inference.

Steven Kolawole, Don Kurian Dennis, Ameet Talwalkar, Virginia Smith

CoRR, 2024

RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.

Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, Aviral Kumar

CoRR, 2024

Jogging the Memory of Unlearned Model Through Targeted Relearning Attack.

Shengyuan Hu, Yiwei Fu, Zhiwei Steven Wu, Virginia Smith

CoRR, 2024

Federated LoRA with Sparse Communication.

Kevin Kuo, Arian Raje, Kousik Rajesh, Virginia Smith

CoRR, 2024

Privacy Amplification for the Gaussian Mechanism via Bounded Support.

Shengyuan Hu, Saeed Mahloujifar, Virginia Smith, Kamalika Chaudhuri, Chuan Guo

CoRR, 2024

Many-Objective Multi-Solution Transport.

Ziyue Li, Tian Li, Virginia Smith, Jeff Bilmes, Tianyi Zhou

CoRR, 2024

Guardrail Baselines for Unlearning in LLMs.

Pratiksha Thaker, Yash Maurya, Virginia Smith

CoRR, 2024

Attacking LLM Watermarks by Exploiting Their Strengths.

Qi Pang, Shengyuan Hu, Wenting Zheng, Virginia Smith

CoRR, 2024

Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes.

Lucio M. Dery, Steven Kolawole, Jean-François Kagey, Virginia Smith, Graham Neubig, Ameet Talwalkar

CoRR, 2024

Fair Federated Learning via Bounded Group Loss.

Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith

Proceedings of the IEEE Conference on Secure and Trustworthy Machine Learning, 2024

Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models.

Amrith Setlur, Saurabh Garg, Virginia Smith, Sergey Levine

Proceedings of the Forty-first International Conference on Machine Learning, 2024

GRASS: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients.

Aashiq Muhamed, Oscar Li, David P. Woodruff, Mona Diab, Virginia Smith

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023

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

Shengyuan Hu, Steven Wu, Virginia Smith

Trans. Mach. Learn. Res., 2023

On Tilted Losses in Machine Learning: Theory and Applications.

Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith

J. Mach. Learn. Res., 2023

Leveraging Public Representations for Private Transfer Learning.

Pratiksha Thaker, Amrith Setlur, Zhiwei Steven Wu, Virginia Smith

CoRR, 2023

Noise-Reuse in Online Evolution Strategies.

Oscar Li, James Harrison, Jascha Sohl-Dickstein, Virginia Smith, Luke Metz

CoRR, 2023

Progressive Knowledge Distillation: Building Ensembles for Efficient Inference.

Don Kurian Dennis, Abhishek Shetty, Anish Sevekari, Kazuhito Koishida, Virginia Smith

CoRR, 2023

Federated Learning as a Network Effects Game.

Shengyuan Hu, Dung Daniel T. Ngo, Shuran Zheng, Virginia Smith, Zhiwei Steven Wu

CoRR, 2023

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

Oscar Li, James Harrison, Jascha Sohl-Dickstein, Virginia Smith, Luke Metz

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift.

Saurabh Garg, Amrith Setlur, Zachary C. Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Progressive Ensemble Distillation: Building Ensembles for Efficient Inference.

Don Kurian Dennis, Abhishek Shetty, Anish Prasad Sevekari, Kazuhito Koishida, Virginia Smith

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On Noisy Evaluation in Federated Hyperparameter Tuning.

Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith

Proceedings of the Sixth Conference on Machine Learning and Systems, 2023

Validating Large Language Models with ReLM.

Michael Kuchnik, Virginia Smith, George Amvrosiadis

Proceedings of the Sixth Conference on Machine Learning and Systems, 2023

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

Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Differentially Private Adaptive Optimization with Delayed Preconditioners.

Tian Li, Manzil Zaheer, Ken Liu, Sashank J. Reddi, Hugh Brendan McMahan, Virginia Smith

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Leveraging Machine Learning to Understand Green Stormwater Infrastructure Performance Risks.

Sizhe Zhang, Achira Amur, Emma Olson, Peleg Kremer, Xun Jiao, Virginia Smith, Bridget Wadzuk

Proceedings of the IEEE Global Humanitarian Technology Conference, 2023

2022

Motley: Benchmarking Heterogeneity and Personalization in Federated Learning.

Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ken Ziyu Liu, Zheng Xu, Virginia Smith

CoRR, 2022

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

Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi

CoRR, 2022

Provably Fair Federated Learning via Bounded Group Loss.

Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith

CoRR, 2022

Adversarial Unlearning: Reducing Confidence Along Adversarial Directions.

Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On Privacy and Personalization in Cross-Silo Federated Learning.

Ken Ziyu Liu, Shengyuan Hu, Steven Wu, Virginia Smith

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Michael Kuchnik, Ana Klimovic, Jiri Simsa, Virginia Smith, George Amvrosiadis

Proceedings of the Fifth Conference on Machine Learning and Systems, 2022

Private Adaptive Optimization with Side information.

Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith

Proceedings of the International Conference on Machine Learning, 2022

Label Leakage and Protection in Two-party Split Learning.

Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, Chong Wang

Proceedings of the Tenth International Conference on Learning Representations, 2022

Diverse Client Selection for Federated Learning via Submodular Maximization.

Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, Jeff A. Bilmes

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

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.

Angela Cotugno, Virginia Smith, Tracy Baker, Raghavan Srinivasan

Remote. Sens., 2021

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

Michael Kuchnik, George Amvrosiadis, Virginia Smith

Proc. VLDB Endow., 2021

A Field Guide to Federated Optimization.

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 Horváth, 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

CoRR, 2021

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

Amrith Setlur, Oscar Li, Virginia Smith

CoRR, 2021

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

Amrith Setlur, Oscar Li, Virginia Smith

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On Large-Cohort Training for Federated Learning.

Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Heterogeneity for the Win: One-Shot Federated Clustering.

Don Kurian Dennis, Tian Li, Virginia Smith

Proceedings of the 38th International Conference on Machine Learning, 2021

Ditto: Fair and Robust Federated Learning Through Personalization.

Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith

Proceedings of the 38th International Conference on Machine Learning, 2021

Tilted Empirical Risk Minimization.

Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Federated Learning: Challenges, Methods, and Future Directions.

Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith

IEEE Signal Process. Mag., 2020

Federated Multi-Task Learning for Competing Constraints.

Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith

CoRR, 2020

Is Support Set Diversity Necessary for Meta-Learning?

Amrith Setlur, Oscar Li, Virginia Smith

CoRR, 2020

Federated Optimization in Heterogeneous Networks.

Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith

Proceedings of the Third Conference on Machine Learning and Systems, 2020

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

Tianlong Yu, Tian Li, Yuqiong Sun, Susanta Nanda, Virginia Smith, Vyas Sekar, Srinivasan Seshan

Proceedings of the Fifth IEEE/ACM International Conference on Internet-of-Things Design and Implementation, 2020

Fair Resource Allocation in Federated Learning.

Tian Li, Maziar Sanjabi, Ahmad Beirami, Virginia Smith

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Enhancing the Privacy of Federated Learning with Sketching.

Zaoxing Liu, Tian Li, Virginia Smith, Vyas Sekar

CoRR, 2019

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

Tian Li, Zaoxing Liu, Vyas Sekar, Virginia Smith

CoRR, 2019

Fair Resource Allocation in Federated Learning.

Tian Li, Maziar Sanjabi, Virginia Smith

CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.

Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. 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 Randall 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

CoRR, 2019

One-Shot Federated Learning.

Neel Guha, Ameet Talwalkar, Virginia Smith

CoRR, 2019

A Kernel Theory of Modern Data Augmentation.

Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, Christopher Ré

Proceedings of the 36th International Conference on Machine Learning, 2019

Efficient Augmentation via Data Subsampling.

Michael Kuchnik, Virginia Smith

Proceedings of the 7th International Conference on Learning Representations, 2019

FedDANE: A Federated Newton-Type Method.

Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith

Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018

On the Convergence of Federated Optimization in Heterogeneous Networks.

Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith

CoRR, 2018

LEAF: A Benchmark for Federated Settings.

Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar

CoRR, 2018

2017

System-Aware Optimization for Machine Learning at Scale.

Virginia Smith

PhD thesis, 2017

Distributed optimization with arbitrary local solvers.

Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác

Optim. Methods Softw., 2017

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

Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi

J. Mach. Learn. Res., 2017

Federated Multi-Task Learning.

Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

A Static Change Impact Analysis Approach based on Metrics and Visualizations to Support the Evolution of Workflow Repositories.

Gustavo Ansaldi Oliva, Marco Aurélio Gerosa, Fabio Kon, Virginia Smith, Dejan S. Milojicic

Int. J. Web Serv. Res., 2016

2015

L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework.

Virginia Smith, Simone Forte, Michael I. Jordan, Martin Jaggi

CoRR, 2015

Going In-Depth: Finding Longform on the Web.

Virginia Smith, Miriam Connor, Isabelle Stanton

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Adding vs. Averaging in Distributed Primal-Dual Optimization.

Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Communication-Efficient Distributed Dual Coordinate Ascent.

Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

A comparative study of high renewables penetration electricity grids.

Jay Taneja, Virginia Smith, David E. Culler, Catherine Rosenberg

Proceedings of the IEEE Fourth International Conference on Smart Grid Communications, 2013

A Change Impact Analysis Approach for Workflow Repository Management.

Gustavo Ansaldi Oliva, Marco Aurélio Gerosa, Dejan S. Milojicic, Virginia Smith

Proceedings of the 2013 IEEE 20th International Conference on Web Services, Santa Clara, CA, USA, June 28, 2013

MLI: An API for Distributed Machine Learning.

Evan Randall Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska

Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

Classification of sidewalks in street view images.

Virginia Smith, Jitendra Malik, David E. Culler

Proceedings of the International Green Computing Conference, 2013

2012

Modeling building thermal response to HVAC zoning.

Virginia Smith, Tamim I. Sookoor, Kamin Whitehouse

SIGBED Rev., 2012

Identifying models of HVAC systems using semiparametric regression.

Anil Aswani, Neal Master, Jay Taneja, Virginia Smith, Andrew Krioukov, David E. Culler, Claire J. Tomlin

Proceedings of the American Control Conference, 2012

Representing USDL for Humans and Tools.

Keith Duddy, Matthias Heinrich, Steffen Heinzl, Martin Knechtel, Carlos Pedrinaci, Benjamin Schmeling, Virginia Smith

Proceedings of the Handbook of Service Description - USDL and Its Methods, 2012