Representing USDL for Humans and Tools. - Keith Duddy; Matthias Heinrich 001; Steffen Heinzl; Martin Knechtel; Carlos Pedrinaci; Benjamin Schmeling; Virginia Smith (Handbook of Service Description, 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 (ACC, 2012)

Modeling building thermal response to HVAC zoning. - Virginia Smith; Tamim I. Sookoor; Kamin Whitehouse (SIGBED Rev., 2012)

Classification of sidewalks in street view images. - Virginia Smith; Jitendra Malik; David E. Culler (IGCC, 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 (ICDM, 2013)

A Change Impact Analysis Approach for Workflow Repository Management. - Gustavo Ansaldi Oliva; Marco Aurélio Gerosa; Dejan S. Milojicic; Virginia Smith (ICWS, 2013)

A comparative study of high renewables penetration electricity grids. - Jay Taneja; Virginia Smith; David E. Culler; Catherine Rosenberg (SmartGridComm, 2013)

Communication-Efficient Distributed Dual Coordinate Ascent. - Martin Jaggi; Virginia Smith; Martin Takác 001; Jonathan Terhorst; Sanjay Krishnan; Thomas Hofmann 001; Michael I. Jordan (NIPS, 2014)

Adding vs. Averaging in Distributed Primal-Dual Optimization. - Chenxin Ma; Virginia Smith; Martin Jaggi; Michael I. Jordan; Peter Richtárik; Martin Takác 001 (ICML, 2015)

Going In-Depth: Finding Longform on the Web. - Virginia Smith; Miriam Connor; Isabelle Stanton (KDD, 2015)

L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework. - Virginia Smith; Simone Forte; Michael I. Jordan; Martin Jaggi (CoRR, 2015)

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)

Federated Multi-Task Learning. - Virginia Smith; Chao-Kai Chiang; Maziar Sanjabi; Ameet Talwalkar (NIPS, 2017)

CoCoA: A General Framework for Communication-Efficient Distributed Optimization. - Virginia Smith; Simone Forte; Chenxin Ma; Martin Takác 001; Michael I. Jordan; Martin Jaggi (J. Mach. Learn. Res., 2017)

Distributed optimization with arbitrary local solvers. - Chenxin Ma; Jakub Konecný; Martin Jaggi; Virginia Smith; Michael I. Jordan; Peter Richtárik; Martin Takác 001 (Optim. Methods Softw., 2017)

System-Aware Optimization for Machine Learning at Scale. - Virginia Smith (2017)

LEAF: A Benchmark for Federated Settings. - Sebastian Caldas; Peter Wu; Tian Li 005; Jakub Konecný; H. Brendan McMahan; Virginia Smith; Ameet Talwalkar (CoRR, 2018)

On the Convergence of Federated Optimization in Heterogeneous Networks. - Anit Kumar Sahu; Tian Li 005; Maziar Sanjabi; Manzil Zaheer; Ameet Talwalkar; Virginia Smith (CoRR, 2018)

FedDANE: A Federated Newton-Type Method. - Tian Li 005; Anit Kumar Sahu; Manzil Zaheer; Maziar Sanjabi; Ameet Talwalkar; Virginia Smith (ACSSC, 2019)

Efficient Augmentation via Data Subsampling. - Michael Kuchnik; Virginia Smith (ICLR (Poster), 2019)

A Kernel Theory of Modern Data Augmentation. - Tri Dao; Albert Gu; Alexander Ratner; Virginia Smith; Chris De Sa; Christopher Ré (ICML, 2019)

One-Shot Federated Learning. - Neel Guha; Ameet Talwalkar; 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; et al. (CoRR, 2019)

Fair Resource Allocation in Federated Learning. - Tian Li 005; Maziar Sanjabi; Virginia Smith (CoRR, 2019)

Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches. - Tian Li 005; Zaoxing Liu; Vyas Sekar; Virginia Smith (CoRR, 2019)

Enhancing the Privacy of Federated Learning with Sketching. - Zaoxing Liu; Tian Li 005; Virginia Smith; Vyas Sekar (CoRR, 2019)

Fair Resource Allocation in Federated Learning. - Tian Li 005; Maziar Sanjabi; Ahmad Beirami; Virginia Smith (ICLR, 2020)

Learning Context-Aware Policies from Multiple Smart Homes via Federated Multi-Task Learning. - Tianlong Yu; Tian Li 005; Yuqiong Sun; Susanta Nanda; Virginia Smith; Vyas Sekar; Srinivasan Seshan (IoTDI, 2020)

Federated Optimization in Heterogeneous Networks. - Tian Li 005; Anit Kumar Sahu; Manzil Zaheer; Maziar Sanjabi; Ameet Talwalkar; Virginia Smith (MLSys, 2020)

Is Support Set Diversity Necessary for Meta-Learning? - Amrith Setlur; Oscar Li; Virginia Smith (CoRR, 2020)

Federated Multi-Task Learning for Competing Constraints. - Tian Li 005; Shengyuan Hu 001; Ahmad Beirami; Virginia Smith (CoRR, 2020)

Federated Learning: Challenges, Methods, and Future Directions. - Tian Li 005; Anit Kumar Sahu; Ameet Talwalkar; Virginia Smith (IEEE Signal Process. Mag., 2020)

Tilted Empirical Risk Minimization. - Tian Li 005; Ahmad Beirami; Maziar Sanjabi; Virginia Smith (ICLR, 2021)

Ditto: Fair and Robust Federated Learning Through Personalization. - Tian Li 005; Shengyuan Hu 001; Ahmad Beirami; Virginia Smith (ICML, 2021)

Heterogeneity for the Win: One-Shot Federated Clustering. - Don Kurian Dennis; Tian Li 005; Virginia Smith (ICML, 2021)

On Large-Cohort Training for Federated Learning. - Zachary Charles; Zachary Garrett; Zhouyuan Huo; Sergei Shmulyian; Virginia Smith (NeurIPS, 2021)

Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. - Mikhail Khodak; Renbo Tu; Tian Li 005; Liam Li; Maria-Florina Balcan; Virginia Smith; Ameet Talwalkar (NeurIPS, 2021)

Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution. - Amrith Setlur; Oscar Li; Virginia Smith (NeurIPS, 2021)

Lessons from Chasing Few-Shot Learning Benchmarks: Rethinking the Evaluation of Meta-Learning Methods. - Amrith Setlur; Oscar Li; Virginia Smith (CoRR, 2021)

A Field Guide to Federated Optimization. - Jianyu Wang; Zachary Charles; Zheng Xu 002; Gauri Joshi; H. Brendan McMahan; Blaise Agüera y Arcas; Maruan Al-Shedivat; Galen Andrew; Salman Avestimehr; Katharine Daly; Deepesh Data; Suhas N. Diggavi; et al. (CoRR, 2021)

Progressive Compressed Records: Taking a Byte out of Deep Learning Data. - Michael Kuchnik; George Amvrosiadis; Virginia Smith (Proc. VLDB Endow., 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)

Diverse Client Selection for Federated Learning via Submodular Maximization. - Ravikumar Balakrishnan; Tian Li 005; Tianyi Zhou 001; Nageen Himayat; Virginia Smith; Jeff A. Bilmes (ICLR, 2022)

Label Leakage and Protection in Two-party Split Learning. - Oscar Li; Jiankai Sun; Xin Yang 017; Weihao Gao; Hongyi Zhang; Junyuan Xie; Virginia Smith; Chong Wang 002 (ICLR, 2022)

Private Adaptive Optimization with Side information. - Tian Li 005; Manzil Zaheer; Sashank J. Reddi; Virginia Smith (ICML, 2022)

Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines. - Michael Kuchnik; Ana Klimovic; Jiri Simsa; Virginia Smith; George Amvrosiadis (MLSys, 2022)

On Privacy and Personalization in Cross-Silo Federated Learning. - Ken Ziyu Liu; Shengyuan Hu 001; Steven Wu 001; Virginia Smith (NeurIPS, 2022)

Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. - Amrith Setlur; Benjamin Eysenbach; Virginia Smith; Sergey Levine (NeurIPS, 2022)

Provably Fair Federated Learning via Bounded Group Loss. - Shengyuan Hu 001; Zhiwei Steven Wu; Virginia Smith (CoRR, 2022)

To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning. - Yae Jee Cho; Divyansh Jhunjhunwala; Tian Li 005; Virginia Smith; Gauri Joshi (CoRR, 2022)

Motley: Benchmarking Heterogeneity and Personalization in Federated Learning. - Shanshan Wu; Tian Li 005; Zachary Charles; Yu Xiao; Ken Ziyu Liu; Zheng Xu 002; Virginia Smith (CoRR, 2022)

Leveraging Machine Learning to Understand Green Stormwater Infrastructure Performance Risks. - Sizhe Zhang; Achira Amur; Emma Olson; Peleg Kremer; Xun Jiao; Virginia Smith; Bridget Wadzuk (GHTC, 2023)

Differentially Private Adaptive Optimization with Delayed Preconditioners. - Tian Li 005; Manzil Zaheer; Ken Liu; Sashank J. Reddi; Hugh Brendan McMahan; Virginia Smith (ICLR, 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 (ICLR, 2023)

Validating Large Language Models with ReLM. - Michael Kuchnik; Virginia Smith; George Amvrosiadis (MLSys, 2023)

On Noisy Evaluation in Federated Hyperparameter Tuning. - Kevin Kuo; Pratiksha Thaker; Mikhail Khodak; John Nguyen; Daniel Jiang; Ameet Talwalkar; Virginia Smith (MLSys, 2023)

Progressive Ensemble Distillation: Building Ensembles for Efficient Inference. - Don Kurian Dennis; Abhishek Shetty; Anish Prasad Sevekari; Kazuhito Koishida; Virginia Smith (NeurIPS, 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 (NeurIPS, 2023)

Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies. - Oscar Li; James Harrison; Jascha Sohl-Dickstein; Virginia Smith; Luke Metz (NeurIPS, 2023)

Federated Learning as a Network Effects Game. - Shengyuan Hu 001; Dung Daniel T. Ngo; Shuran Zheng; Virginia Smith; Zhiwei Steven Wu (CoRR, 2023)

Progressive Knowledge Distillation: Building Ensembles for Efficient Inference. - Don Kurian Dennis; Abhishek Shetty; Anish Sevekari; Kazuhito Koishida; Virginia Smith (CoRR, 2023)

Noise-Reuse in Online Evolution Strategies. - Oscar Li; James Harrison; Jascha Sohl-Dickstein; Virginia Smith; Luke Metz (CoRR, 2023)

Leveraging Public Representations for Private Transfer Learning. - Pratiksha Thaker; Amrith Setlur; Zhiwei Steven Wu; Virginia Smith (CoRR, 2023)

On Tilted Losses in Machine Learning: Theory and Applications. - Tian Li 005; Ahmad Beirami; Maziar Sanjabi; Virginia Smith (J. Mach. Learn. Res., 2023)

Private Multi-Task Learning: Formulation and Applications to Federated Learning. - Shengyuan Hu 001; Steven Wu 001; Virginia Smith (Trans. Mach. Learn. Res., 2023)

GRASS: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients. - Aashiq Muhamed; Oscar Li; David P. Woodruff; Mona T. Diab; Virginia Smith (EMNLP, 2024)

Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models. - Amrith Setlur; Saurabh Garg; Virginia Smith; Sergey Levine (ICML, 2024)

No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices. - Qi Pang; Shengyuan Hu 001; Wenting Zheng; Virginia Smith (NeurIPS, 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 (NeurIPS, 2024)

On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift. - Pratiksha Thaker; Amrith Setlur; Steven Z. Wu; Virginia Smith (NeurIPS, 2024)

Fair Federated Learning via Bounded Group Loss. - Shengyuan Hu 001; Zhiwei Steven Wu; Virginia Smith (SaTML, 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)

Attacking LLM Watermarks by Exploiting Their Strengths. - Qi Pang; Shengyuan Hu 001; Wenting Zheng; Virginia Smith (CoRR, 2024)

Guardrail Baselines for Unlearning in LLMs. - Pratiksha Thaker; Yash Maurya; Virginia Smith (CoRR, 2024)

Privacy Amplification for the Gaussian Mechanism via Bounded Support. - Shengyuan Hu 001; Saeed Mahloujifar; Virginia Smith; Kamalika Chaudhuri; Chuan Guo 001 (CoRR, 2024)

Federated LoRA with Sparse Communication. - Kevin Kuo; Arian Raje; Kousik Rajesh; Virginia Smith (CoRR, 2024)

Jogging the Memory of Unlearned Model Through Targeted Relearning Attack. - Shengyuan Hu 001; Yiwei Fu; Zhiwei Steven Wu; Virginia Smith (CoRR, 2024)

Revisiting Cascaded Ensembles for Efficient Inference. - Steven Kolawole; Don Kurian Dennis; Ameet Talwalkar; Virginia Smith (CoRR, 2024)

Maximizing Global Model Appeal in Federated Learning. - Yae Jee Cho; Divyansh Jhunjhunwala; Tian Li 005; Virginia Smith; Gauri Joshi (Trans. Mach. Learn. Res., 2024)

Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning. - Shengyuan Hu 001; Yiwei Fu; Steven Z. Wu; Virginia Smith (ICLR, 2025)

Many-Objective Multi-Solution Transport. - Ziyue Li; Tian Li 005; Virginia Smith; Jeff Bilmes; Tianyi Zhou 001 (ICLR, 2025)

Decoding Dark Matter: Specialized Sparse Autoencoders for Interpreting Rare Concepts in Foundation Models. - Aashiq Muhamed; Mona T. Diab; Virginia Smith (NAACL (Findings), 2025)

CoRAG: Collaborative Retrieval-Augmented Generation. - Aashiq Muhamed; Mona T. Diab; Virginia Smith (NAACL (Short Papers), 2025)

Position: LLM Unlearning Benchmarks are Weak Measures of Progress. - Pratiksha Thaker; Shengyuan Hu 001; Neil Kale; Yash Maurya; Zhiwei Steven Wu; Virginia Smith (SaTML, 2025)

Spatiotemporal flood depth and velocity dynamics using a convolutional neural network within a sequential Deep-Learning framework. - Mohamed M. Fathi; Zihan Liu; Anjali M. Fernandes; Michael T. Hren; Dennis O. Terry; C. Nataraj; Virginia Smith (Environ. Model. Softw., 2025)

Agreement-Based Cascading for Efficient Inference. - Steven Kolawole; Don Kurian Dennis; Ameet Talwalkar; Virginia Smith (Trans. Mach. Learn. Res., 2025)

NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA. - 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; et al. (Trans. Mach. Learn. Res., 2025)

Exact Unlearning of Finetuning Data via Model Merging at Scale. - Kevin Kuo; Amrith Setlur; Kartik Srinivas; Aditi Raghunathan; Virginia Smith (CoRR, 2025)

SAEs Can Improve Unlearning: Dynamic Sparse Autoencoder Guardrails for Precision Unlearning in LLMs. - Aashiq Muhamed; Jacopo Bonato; Mona T. Diab; Virginia Smith (CoRR, 2025)

Position: Mechanistic Interpretability Should Prioritize Feature Consistency in SAEs. - Xiangchen Song; Aashiq Muhamed; Yujia Zheng 001; Lingjing Kong; Zeyu Tang 002; Mona T. Diab; Virginia Smith; Kun Zhang 001 (CoRR, 2025)

Membership Inference Attacks for Unseen Classes. - Pratiksha Thaker; Neil Kale; Zhiwei Steven Wu; Virginia Smith (CoRR, 2025)

e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs. - Amrith Setlur; Matthew Y. R. Yang; Charlie Snell; Jeremy Greer; Ian Wu; Virginia Smith; Max Simchowitz; Aviral Kumar (CoRR, 2025)

BLUR: A Benchmark for LLM Unlearning Robust to Forget-Retain Overlap. - Shengyuan Hu 001; Neil Kale; Pratiksha Thaker; Yiwei Fu; Steven Wu 001; Virginia Smith (CoRR, 2025)

PARALLELPROMPT: Extracting Parallelism from Large Language Model Queries. - Steven Kolawole; Keshav Santhanam; Virginia Smith; Pratiksha Thaker (CoRR, 2025)