Virginia Smith

140 papers A* 29A 1C 1Misc 1Journal 88Unranked 17

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Automated Concept Discovery for LLM-as-a-Judge Preference Analysis. CoRR James Wedgwood, Chhavi Yadav, Virginia Smith
2026	J	`jnl`	DSPA: Dynamic SAE Steering for Data-Efficient Preference Alignment. CoRR James Wedgwood, Aashiq Muhamed, Mona T. Diab, Virginia Smith
2026	J	`jnl`	IsoCompute Playbook: Optimally Scaling Sampling Compute for LLM RL. CoRR Zhoujun Cheng, Yutao Xie, Yuxiao Qu, Amrith Setlur, Shibo Hao, Varad Pimpalkhute, Tongtong Liang, Feng Yao, Zhengzhong Liu, Eric P. Xing, Virginia Smith, Ruslan Salakhutdinov, Zhiting Hu, Taylor W. Killian, Aviral Kumar
2026	J	`jnl`	Noisette: Certifying Differential Privacy Mechanisms Efficiently. IACR Cryptol. ePrint Arch. Qi Pang, Radhika Garg, Ziling Liu, Hanshen Xiao, Virginia Smith, Wenting Zheng, Xiao Wang
2026	J	`jnl`	POPE: Learning to Reason on Hard Problems via Privileged On-Policy Exploration. CoRR Yuxiao Qu, Amrith Setlur, Virginia Smith, Ruslan Salakhutdinov, Aviral Kumar
2026	—	`conf`	RefusalBench: Generative Evaluation of Selective Refusal in Grounded Language Models. EACL (Volume 1: Long Papers) Aashiq Muhamed, Leonardo F. R. Ribeiro, Markus Dreyer, Virginia Smith, Mona T. Diab
2025	J	`jnl`	Agreement-Based Cascading for Efficient Inference. Trans. Mach. Learn. Res. Steven Kolawole, Don Kurian Dennis, Ameet Talwalkar, Virginia Smith
2025	J	`jnl`	BLUR: A Benchmark for LLM Unlearning Robust to Forget-Retain Overlap. CoRR Shengyuan Hu, Neil Kale, Pratiksha Thaker, Yiwei Fu, Steven Wu, Virginia Smith
2025	A*	`conf`	COpter: Efficient Large-Scale Resource-Allocation via Continual Optimization. SOSP Suhas Jayaram Subramanya, Don Kurian Dennis, Virginia Smith, Gregory R. Ganger
2025	—	`conf`	CoRAG: Collaborative Retrieval-Augmented Generation. NAACL (Short Papers) Aashiq Muhamed, Mona T. Diab, Virginia Smith
2025	J	`jnl`	CoRAG: Collaborative Retrieval-Augmented Generation. CoRR Aashiq Muhamed, Mona T. Diab, Virginia Smith
2025	—	`conf`	Decoding Dark Matter: Specialized Sparse Autoencoders for Interpreting Rare Concepts in Foundation Models. NAACL (Findings) Aashiq Muhamed, Mona T. Diab, Virginia Smith
2025	J	`jnl`	Exact Unlearning of Finetuning Data via Model Merging at Scale. CoRR Kevin Kuo, Amrith Setlur, Kartik Srinivas, Aditi Raghunathan, Virginia Smith
2025	A*	`conf`	Many-Objective Multi-Solution Transport. ICLR Ziyue Li, Tian Li, Virginia Smith, Jeff A. Bilmes, Tianyi Zhou
2025	J	`jnl`	Membership Inference Attacks for Unseen Classes. CoRR Pratiksha Thaker, Neil Kale, Zhiwei Steven Wu, Virginia Smith
2025	J	`jnl`	NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA. Trans. Mach. Learn. Res. Marlon Tobaben, Mohamed Ali Souibgui, Rubèn Tito, Khanh Nguyen, Raouf Kerkouche, Kangsoo Jung, Joonas Jälkö, Lei Kang, Andrey Barsky, Vincent Poulain D'Andecy, Aurélie Joseph, Aashiq Muhamed, Kevin Kuo, Virginia Smith, Yusuke Yamasaki, Takumi Fukami, Kenta Niwa, Iifan Tyou, Hiro Ishii, Rio Yokota, Ragul N, Rintu Kutum, Josep Lladós, Ernest Valveny, Antti Honkela, Mario Fritz, Dimosthenis Karatzas
2025	J	`jnl`	PARALLELPROMPT: Extracting Parallelism from Large Language Model Queries. CoRR Steven Kolawole, Keshav Santhanam, Virginia Smith, Pratiksha Thaker
2025	—	`conf`	Position: LLM Unlearning Benchmarks are Weak Measures of Progress. SaTML Pratiksha Thaker, Shengyuan Hu, Neil Kale, Yash Maurya, Zhiwei Steven Wu, Virginia Smith
2025	J	`jnl`	Position: Mechanistic Interpretability Should Prioritize Feature Consistency in SAEs. CoRR Xiangchen Song, Aashiq Muhamed, Yujia Zheng, Lingjing Kong, Zeyu Tang, Mona T. Diab, Virginia Smith, Kun Zhang
2025	J	`jnl`	RefusalBench: Generative Evaluation of Selective Refusal in Grounded Language Models. CoRR Aashiq Muhamed, Leonardo F. R. Ribeiro, Markus Dreyer, Virginia Smith, Mona T. Diab
2025	J	`jnl`	Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice. CoRR Kevin Kuo, Chhavi Yadav, Virginia Smith
2025	J	`jnl`	SAEs CoRR Aashiq Muhamed, Jacopo Bonato, Mona T. Diab, Virginia Smith
2025	—	`conf`	Semantic Agreement Enables Efficient Open-Ended LLM Cascades. EMNLP (Industry Track) Duncan Soiffer, Steven Kolawole, Virginia Smith
2025	J	`jnl`	Semantic Agreement Enables Efficient Open-Ended LLM Cascades. CoRR Duncan Soiffer, Steven Kolawole, Virginia Smith
2025	J	`jnl`	Spatiotemporal flood depth and velocity dynamics using a convolutional neural network within a sequential Deep-Learning framework. Environ. Model. Softw. Mohamed M. Fathi, Zihan Liu, Anjali M. Fernandes, Michael T. Hren, Dennis O. Terry, C. Nataraj, Virginia Smith
2025	A*	`conf`	Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning. ICLR Shengyuan Hu, Yiwei Fu, Steven Z. Wu, Virginia Smith
2025	J	`jnl`	e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs. CoRR Amrith Setlur, Matthew Y. R. Yang, Charlie Snell, Jeremy Greer, Ian Wu, Virginia Smith, Max Simchowitz, Aviral Kumar
2024	J	`jnl`	Attacking LLM Watermarks by Exploiting Their Strengths. CoRR Qi Pang, Shengyuan Hu, Wenting Zheng, Virginia Smith
2024	J	`jnl`	Decoding Dark Matter: Specialized Sparse Autoencoders for Interpreting Rare Concepts in Foundation Models. CoRR Aashiq Muhamed, Mona T. Diab, Virginia Smith
2024	J	`jnl`	Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes. CoRR Lucio M. Dery, Steven Kolawole, Jean-François Kagey, Virginia Smith, Graham Neubig, Ameet Talwalkar
2024	—	`conf`	Fair Federated Learning via Bounded Group Loss. SaTML Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
2024	J	`jnl`	Federated LoRA with Sparse Communication. CoRR Kevin Kuo, Arian Raje, Kousik Rajesh, Virginia Smith
2024	A*	`conf`	GRASS: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients. EMNLP Aashiq Muhamed, Oscar Li, David P. Woodruff, Mona T. Diab, Virginia Smith
2024	J	`jnl`	Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients. CoRR Aashiq Muhamed, Oscar Li, David P. Woodruff, Mona T. Diab, Virginia Smith
2024	J	`jnl`	Guardrail Baselines for Unlearning in LLMs. CoRR Pratiksha Thaker, Yash Maurya, Virginia Smith
2024	J	`jnl`	Jogging the Memory of Unlearned Model Through Targeted Relearning Attack. CoRR Shengyuan Hu, Yiwei Fu, Zhiwei Steven Wu, Virginia Smith
2024	J	`jnl`	Many-Objective Multi-Solution Transport. CoRR Ziyue Li, Tian Li, Virginia Smith, Jeff A. Bilmes, Tianyi Zhou
2024	J	`jnl`	Maximizing Global Model Appeal in Federated Learning. Trans. Mach. Learn. Res. Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi
2024	J	`jnl`	NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA. CoRR Marlon Tobaben, Mohamed Ali Souibgui, Rubèn Tito, Khanh Nguyen, Raouf Kerkouche, Kangsoo Jung, Joonas Jälkö, Lei Kang, Andrey Barsky, Vincent Poulain D'Andecy, Aurélie Joseph, Aashiq Muhamed, Kevin Kuo, Virginia Smith, Yusuke Yamasaki, Takumi Fukami, Kenta Niwa, Iifan Tyou, Hiro Ishii, Rio Yokota, Ragul N, Rintu Kutum, Josep Lladós, Ernest Valveny, Antti Honkela, Mario Fritz, Dimosthenis Karatzas
2024	A*	`conf`	No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices. NeurIPS Qi Pang, Shengyuan Hu, Wenting Zheng, Virginia Smith
2024	A*	`conf`	On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift. NeurIPS Pratiksha Thaker, Amrith Setlur, Steven Z. Wu, Virginia Smith
2024	J	`jnl`	Position: LLM Unlearning Benchmarks are Weak Measures of Progress. CoRR Pratiksha Thaker, Shengyuan Hu, Neil Kale, Yash Maurya, Zhiwei Steven Wu, Virginia Smith
2024	J	`jnl`	Privacy Amplification for the Gaussian Mechanism via Bounded Support. CoRR Shengyuan Hu, Saeed Mahloujifar, Virginia Smith, Kamalika Chaudhuri, Chuan Guo
2024	A*	`conf`	Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models. ICML Amrith Setlur, Saurabh Garg, Virginia Smith, Sergey Levine
2024	A*	`conf`	RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. NeurIPS Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, Aviral Kumar
2024	J	`jnl`	RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. CoRR Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, Aviral Kumar
2024	J	`jnl`	Revisiting Cascaded Ensembles for Efficient Inference. CoRR Steven Kolawole, Don Kurian Dennis, Ameet Talwalkar, Virginia Smith
2023	A*	`conf`	Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. ICLR Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine
2023	J	`jnl`	Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. CoRR Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine
2023	A*	`conf`	Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. NeurIPS Saurabh Garg, Amrith Setlur, Zachary C. Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan
2023	J	`jnl`	Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. CoRR Saurabh Garg, Amrith Setlur, Zachary Chase Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan
2023	A*	`conf`	Differentially Private Adaptive Optimization with Delayed Preconditioners. ICLR Tian Li, Manzil Zaheer, Ken Liu, Sashank J. Reddi, Hugh Brendan McMahan, Virginia Smith
2023	J	`jnl`	Federated Learning as a Network Effects Game. CoRR Shengyuan Hu, Dung Daniel T. Ngo, Shuran Zheng, Virginia Smith, Zhiwei Steven Wu
2023	—	`conf`	Leveraging Machine Learning to Understand Green Stormwater Infrastructure Performance Risks. GHTC Sizhe Zhang, Achira Amur, Emma Olson, Peleg Kremer, Xun Jiao, Virginia Smith, Bridget Wadzuk
2023	J	`jnl`	Leveraging Public Representations for Private Transfer Learning. CoRR Pratiksha Thaker, Amrith Setlur, Zhiwei Steven Wu, Virginia Smith
2023	J	`jnl`	Noise-Reuse in Online Evolution Strategies. CoRR Oscar Li, James Harrison, Jascha Sohl-Dickstein, Virginia Smith, Luke Metz
2023	—	`conf`	On Noisy Evaluation in Federated Hyperparameter Tuning. MLSys Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith
2023	J	`jnl`	On Tilted Losses in Machine Learning: Theory and Applications. J. Mach. Learn. Res. Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
2023	J	`jnl`	Private Multi-Task Learning: Formulation and Applications to Federated Learning. Trans. Mach. Learn. Res. Shengyuan Hu, Steven Wu, Virginia Smith
2023	A*	`conf`	Progressive Ensemble Distillation: Building Ensembles for Efficient Inference. NeurIPS Don Kurian Dennis, Abhishek Shetty, Anish Prasad Sevekari, Kazuhito Koishida, Virginia Smith
2023	J	`jnl`	Progressive Knowledge Distillation: Building Ensembles for Efficient Inference. CoRR Don Kurian Dennis, Abhishek Shetty, Anish Sevekari, Kazuhito Koishida, Virginia Smith
2023	—	`conf`	Validating Large Language Models with ReLM. MLSys Michael Kuchnik, Virginia Smith, George Amvrosiadis
2023	A*	`conf`	Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies. NeurIPS Oscar Li, James Harrison, Jascha Sohl-Dickstein, Virginia Smith, Luke Metz
2022	A*	`conf`	Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. NeurIPS Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine
2022	J	`jnl`	Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. CoRR Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine
2022	J	`jnl`	Differentially Private Adaptive Optimization with Delayed Preconditioners. CoRR Tian Li, Manzil Zaheer, Ken Ziyu Liu, Sashank J. Reddi, H. Brendan McMahan, Virginia Smith
2022	A*	`conf`	Diverse Client Selection for Federated Learning via Submodular Maximization. ICLR Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, Jeff A. Bilmes
2022	A*	`conf`	Label Leakage and Protection in Two-party Split Learning. ICLR Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, Chong Wang
2022	J	`jnl`	Motley: Benchmarking Heterogeneity and Personalization in Federated Learning. CoRR Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ken Ziyu Liu, Zheng Xu, Virginia Smith
2022	J	`jnl`	On Noisy Evaluation in Federated Hyperparameter Tuning. CoRR Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith
2022	A*	`conf`	On Privacy and Personalization in Cross-Silo Federated Learning. NeurIPS Ken Ziyu Liu, Shengyuan Hu, Steven Wu, Virginia Smith
2022	J	`jnl`	On Privacy and Personalization in Cross-Silo Federated Learning. CoRR Ken Ziyu Liu, Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
2022	—	`conf`	Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines. MLSys Michael Kuchnik, Ana Klimovic, Jiri Simsa, Virginia Smith, George Amvrosiadis
2022	J	`jnl`	Private Adaptive Optimization with Side Information. CoRR Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith
2022	A*	`conf`	Private Adaptive Optimization with Side information. ICML Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith
2022	J	`jnl`	Provably Fair Federated Learning via Bounded Group Loss. CoRR Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
2022	J	`jnl`	To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning. CoRR Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi
2022	J	`jnl`	Validating Large Language Models with ReLM. CoRR Michael Kuchnik, Virginia Smith, George Amvrosiadis
2021	J	`jnl`	A Field Guide to Federated Optimization. CoRR 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
2021	J	`jnl`	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. Angela Cotugno, Virginia Smith, Tracy Baker, Raghavan Srinivasan
2021	A*	`conf`	Ditto: Fair and Robust Federated Learning Through Personalization. ICML Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith
2021	A*	`conf`	Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. NeurIPS Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar
2021	J	`jnl`	Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. CoRR Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar
2021	A*	`conf`	Heterogeneity for the Win: One-Shot Federated Clustering. ICML Don Kurian Dennis, Tian Li, Virginia Smith
2021	J	`jnl`	Heterogeneity for the Win: One-Shot Federated Clustering. CoRR Don Kurian Dennis, Tian Li, Virginia Smith
2021	J	`jnl`	Label Leakage and Protection in Two-party Split Learning. CoRR Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, Chong Wang
2021	J	`jnl`	Lessons from Chasing Few-Shot Learning Benchmarks: Rethinking the Evaluation of Meta-Learning Methods. CoRR Amrith Setlur, Oscar Li, Virginia Smith
2021	A*	`conf`	On Large-Cohort Training for Federated Learning. NeurIPS Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith
2021	J	`jnl`	On Large-Cohort Training for Federated Learning. CoRR Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith
2021	J	`jnl`	On Tilted Losses in Machine Learning: Theory and Applications. CoRR Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
2021	J	`jnl`	Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines. CoRR Michael Kuchnik, Ana Klimovic, Jiri Simsa, George Amvrosiadis, Virginia Smith
2021	J	`jnl`	Private Multi-Task Learning: Formulation and Applications to Federated Learning. CoRR Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
2021	J	`jnl`	Progressive Compressed Records: Taking a Byte out of Deep Learning Data. Proc. VLDB Endow. Michael Kuchnik, George Amvrosiadis, Virginia Smith
2021	A*	`conf`	Tilted Empirical Risk Minimization. ICLR Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
2021	A*	`conf`	Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution. NeurIPS Amrith Setlur, Oscar Li, Virginia Smith
2020	A*	`conf`	Fair Resource Allocation in Federated Learning. ICLR Tian Li, Maziar Sanjabi, Ahmad Beirami, Virginia Smith
2020	J	`jnl`	FedDANE: A Federated Newton-Type Method. CoRR Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
2020	J	`jnl`	Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Process. Mag. Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith
2020	J	`jnl`	Federated Multi-Task Learning for Competing Constraints. CoRR Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith
2020	—	`conf`	Federated Optimization in Heterogeneous Networks. MLSys Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
2020	J	`jnl`	Is Support Set Diversity Necessary for Meta-Learning? CoRR Amrith Setlur, Oscar Li, Virginia Smith
2020	—	`conf`	Learning Context-Aware Policies from Multiple Smart Homes via Federated Multi-Task Learning. IoTDI Tianlong Yu, Tian Li, Yuqiong Sun, Susanta Nanda, Virginia Smith, Vyas Sekar, Srinivasan Seshan
2020	J	`jnl`	Tilted Empirical Risk Minimization. CoRR Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
2019	A*	`conf`	A Kernel Theory of Modern Data Augmentation. ICML Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, Christopher Ré
2019	—	`conf`	Efficient Augmentation via Data Subsampling. ICLR (Poster) Michael Kuchnik, Virginia Smith
2019	J	`jnl`	Enhancing the Privacy of Federated Learning with Sketching. CoRR Zaoxing Liu, Tian Li, Virginia Smith, Vyas Sekar
2019	J	`jnl`	Fair Resource Allocation in Federated Learning. CoRR Tian Li, Maziar Sanjabi, Virginia Smith
2019	Misc	`conf`	FedDANE: A Federated Newton-Type Method. ACSSC Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
2019	J	`jnl`	Federated Learning: Challenges, Methods, and Future Directions. CoRR Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith
2019	J	`jnl`	One-Shot Federated Learning. CoRR Neel Guha, Ameet Talwalkar, Virginia Smith
2019	J	`jnl`	Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches. CoRR Tian Li, Zaoxing Liu, Vyas Sekar, Virginia Smith
2019	—	`ed.`	Proceedings of the Second Conference on Machine Learning and Systems, SysML 2019, Stanford, CA, USA, March 31 - April 2, 2019 SysML Ameet Talwalkar, Virginia Smith, Matei Zaharia
2019	J	`jnl`	Progressive Compressed Records: Taking a Byte out of Deep Learning Data. CoRR Michael Kuchnik, George Amvrosiadis, Virginia Smith
2019	J	`jnl`	SysML: The New Frontier of Machine Learning Systems. CoRR 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 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
2018	J	`jnl`	A Kernel Theory of Modern Data Augmentation. CoRR Tri Dao, Albert Gu, Alexander J. Ratner, Virginia Smith, Christopher De Sa, Christopher Ré
2018	J	`jnl`	Efficient Augmentation via Data Subsampling. CoRR Michael Kuchnik, Virginia Smith
2018	J	`jnl`	LEAF: A Benchmark for Federated Settings. CoRR Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar
2018	J	`jnl`	On the Convergence of Federated Optimization in Heterogeneous Networks. CoRR Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith
2017	J	`jnl`	CoCoA: A General Framework for Communication-Efficient Distributed Optimization. J. Mach. Learn. Res. Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi
2017	J	`jnl`	Distributed optimization with arbitrary local solvers. Optim. Methods Softw. Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác
2017	—	`conf`	Federated Multi-Task Learning. NIPS Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet Talwalkar
2017	J	`jnl`	Federated Multi-Task Learning. CoRR Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet Talwalkar
2017	—	`—`	System-Aware Optimization for Machine Learning at Scale. Virginia Smith
2016	J	`jnl`	A Static Change Impact Analysis Approach based on Metrics and Visualizations to Support the Evolution of Workflow Repositories. Int. J. Web Serv. Res. Gustavo Ansaldi Oliva, Marco Aurélio Gerosa, Fabio Kon, Virginia Smith, Dejan S. Milojicic
2016	J	`jnl`	CoCoA: A General Framework for Communication-Efficient Distributed Optimization. CoRR Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi
2015	A*	`conf`	Adding vs. Averaging in Distributed Primal-Dual Optimization. ICML Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác
2015	J	`jnl`	Adding vs. Averaging in Distributed Primal-Dual Optimization. CoRR Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác
2015	J	`jnl`	Distributed Optimization with Arbitrary Local Solvers. CoRR Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác
2015	A*	`conf`	Going In-Depth: Finding Longform on the Web. KDD Virginia Smith, Miriam Connor, Isabelle Stanton
2015	J	`jnl`	L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework. CoRR Virginia Smith, Simone Forte, Michael I. Jordan, Martin Jaggi
2014	—	`conf`	Communication-Efficient Distributed Dual Coordinate Ascent. NIPS Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan
2014	J	`jnl`	Communication-Efficient Distributed Dual Coordinate Ascent. CoRR Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan
2013	A	`conf`	A Change Impact Analysis Approach for Workflow Repository Management. ICWS Gustavo Ansaldi Oliva, Marco Aurélio Gerosa, Dejan S. Milojicic, Virginia Smith
2013	—	`conf`	A comparative study of high renewables penetration electricity grids. SmartGridComm Jay Taneja, Virginia Smith, David E. Culler, Catherine Rosenberg
2013	—	`conf`	Classification of sidewalks in street view images. IGCC Virginia Smith, Jitendra Malik, David E. Culler
2013	A*	`conf`	MLI: An API for Distributed Machine Learning. ICDM Evan Randall Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska
2013	J	`jnl`	MLI: An API for Distributed Machine Learning. CoRR Evan Randall Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska
2012	C	`conf`	Identifying models of HVAC systems using semiparametric regression. ACC Anil Aswani, Neal Master, Jay Taneja, Virginia Smith, Andrew Krioukov, David E. Culler, Claire J. Tomlin
2012	J	`jnl`	Modeling building thermal response to HVAC zoning. SIGBED Rev. Virginia Smith, Tamim I. Sookoor, Kamin Whitehouse
2012	—	`ch.`	Representing USDL for Humans and Tools. Handbook of Service Description Keith Duddy, Matthias Heinrich, Steffen Heinzl, Martin Knechtel, Carlos Pedrinaci, Benjamin Schmeling, Virginia Smith