ML Anthology

Smith, Virginia

54 publications

TMLR 2025 Agreement-Based Cascading for Efficient Inference Steven Kolawole, Don Dennis, Ameet Talwalkar, Virginia Smith
ICLRW 2025 Exact Unlearning of Finetuning Data via Model Merging at Scale Kevin Kuo, Amrith Setlur, Kartik Srinivas, Aditi Raghunathan, Virginia Smith
ICLR 2025 Many-Objective Multi-Solution Transport Ziyue Li, Tian Li, Virginia Smith, Jeff Bilmes, Tianyi Zhou
TMLR 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, Kevin Kuo, Virginia Smith, Yusuke Yamasaki, Takumi Fukami, Kenta Niwa, Iifan Tyou, Hiro Ishii, Rio Yokota, Ragul N, Rintu Kutum, Josep Llados, Ernest Valveny, Antti Honkela, Mario Fritz, Dimosthenis Karatzas
NeurIPS 2025 PARALLELPROMPT: Extracting Parallelism from Large Language Model Queries Steven Kolawole, Keshav Santhanam, Virginia Smith, Pratiksha Thaker
ICLR 2025 Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning Shengyuan Hu, Yiwei Fu, Steven Wu, Virginia Smith
ICLRW 2024 Attacking LLM Watermarks by Exploiting Their Strengths Qi Pang, Shengyuan Hu, Wenting Zheng, Virginia Smith
ICLRW 2024 Cache Me if You Can: The Case for Retrieval Augmentation in Federated Learning Aashiq Muhamed, Pratiksha Thaker, Mona T. Diab, Virginia Smith
NeurIPSW 2024 Extracting Parallelism from Large Language Model Queries Steven Kolawole, Keshav Santhanam, Virginia Smith, Pratiksha Thaker
ICLRW 2024 Fed up with Complexity: Simplifying Many-Task Federated Learning with NTKFedAvg Aashiq Muhamed, Meher Mankikar, Virginia Smith
ICMLW 2024 GRASS: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients Aashiq Muhamed, Oscar Li, David Woodruff, Mona T. Diab, Virginia Smith
ICLRW 2024 I'm Not Familiar with the Name Harry Potter: Prompting Baselines for Unlearning in LLMs Pratiksha Thaker, Yash Maurya, Virginia Smith
NeurIPSW 2024 Jogging the Memory of Unlearned LLMs Through Targeted Relearning Attacks Shengyuan Hu, Yiwei Fu, Steven Wu, Virginia Smith
ICMLW 2024 Jogging the Memory of Unlearned Models Through Targeted Relearning Attacks Shengyuan Hu, Yiwei Fu, Steven Wu, Virginia Smith
ICMLW 2024 Learning to Reason by Failing: Offline RL on Sub-Optimal Rollouts Scales Synthetic Data by 8x Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, Aviral Kumar
TMLR 2024 Maximizing Global Model Appeal in Federated Learning Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi
NeurIPS 2024 No Free Lunch in LLM Watermarking: Trade-Offs in Watermarking Design Choices Qi Pang, Shengyuan Hu, Wenting Zheng, Virginia Smith
NeurIPS 2024 On the Benefits of Public Representations for Private Transfer Learning Under Distribution Shift Pratiksha Thaker, Amrith Setlur, Zhiwei Steven Wu, Virginia Smith
ICML 2024 Prompting Is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models Amrith Setlur, Saurabh Garg, Virginia Smith, Sergey Levine
ICLRW 2024 Prompting for Robustness: Extracting Robust Classifiers from Foundation Models Amrith Setlur, Saurabh Garg, Virginia Smith, Sergey Levine
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
ICMLW 2024 Revisiting Cascaded Ensembles for Efficient Inference Steven Kolawole, Don Dennis, Ameet Talwalkar, Virginia Smith
ICLRW 2024 Sparsity for Communication-Efficient LoRA Kevin Kuo, Arian Raje, Kousik Rajesh, Virginia Smith
ICLR 2023 Bitrate-Constrained DRO: Beyond Worst Case Robustness to Unknown Group Shifts Amrith Setlur, Don Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine
NeurIPS 2023 Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift Saurabh Garg, Amrith Setlur, Zachary Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan
ICLR 2023 Differentially Private Adaptive Optimization with Delayed Preconditioners Tian Li, Manzil Zaheer, Ken Liu, Sashank J. Reddi, Hugh Brendan McMahan, Virginia Smith
JMLR 2023 On Tilted Losses in Machine Learning: Theory and Applications Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
TMLR 2023 Private Multi-Task Learning: Formulation and Applications to Federated Learning Shengyuan Hu, Steven Wu, Virginia Smith
NeurIPS 2023 Progressive Ensemble Distillation: Building Ensembles for Efficient Inference Don Dennis, Abhishek Shetty, Anish Prasad Sevekari, Kazuhito Koishida, Virginia Smith
ICMLW 2023 Progressive Knowledge Distillation: Balancing Inference Latency and Accuracy at Runtime Don Dennis, Abhishek Shetty, Anish Sevekari, Kazuhito Koishida, Virginia Smith
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 2022 Adversarial Unlearning: Reducing Confidence Along Adversarial Directions Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine
NeurIPSW 2022 Bitrate-Constrained DRO: Beyond Worst Case Robustness to Unknown Group Shifts Amrith Setlur, Don Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine
NeurIPSW 2022 Differentially Private Adaptive Optimization with Delayed Preconditioners Tian Li, Manzil Zaheer, Ken Liu, Sashank J. Reddi, Hugh Brendan McMahan, Virginia Smith
ICLR 2022 Diverse Client Selection for Federated Learning via Submodular Maximization Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, Jeff Bilmes
ICLR 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
ICLRW 2022 Maximizing Entropy on Adversarial Examples Can Improve Generalization Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine
NeurIPSW 2022 Motley: Benchmarking Heterogeneity and Personalization in Federated Learning Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ken Liu, Zheng Xu, Virginia Smith
NeurIPS 2022 On Privacy and Personalization in Cross-Silo Federated Learning Ken Liu, Shengyuan Hu, Steven Z. Wu, Virginia Smith
ICML 2022 Private Adaptive Optimization with Side Information Tian Li, Manzil Zaheer, Sashank Reddi, Virginia Smith
NeurIPSW 2022 To Federate or Not to Federate: Incentivizing Client Participation in Federated Learning Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi
ICML 2021 Ditto: Fair and Robust Federated Learning Through Personalization Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith
NeurIPS 2021 Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina F Balcan, Virginia Smith, Ameet Talwalkar
ICML 2021 Heterogeneity for the Win: One-Shot Federated Clustering Don Kurian Dennis, Tian Li, Virginia Smith
NeurIPS 2021 On Large-Cohort Training for Federated Learning Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith
ICLR 2021 Tilted Empirical Risk Minimization Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
NeurIPS 2021 Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution Amrith Setlur, Oscar Li, Virginia Smith
ICLR 2020 Fair Resource Allocation in Federated Learning Tian Li, Maziar Sanjabi, Ahmad Beirami, Virginia Smith
ICML 2019 A Kernel Theory of Modern Data Augmentation Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, Christopher Re
ICLR 2019 Efficient Augmentation via Data Subsampling Michael Kuchnik, Virginia Smith
ICMLW 2019 Federated Optimization for Heterogeneous Networks Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
NeurIPS 2017 Federated Multi-Task Learning Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet S Talwalkar
ICML 2015 Adding vs. Averaging in Distributed Primal-Dual Optimization Chenxin Ma, Virginia Smith, Martin Jaggi, Michael Jordan, Peter Richtarik, Martin Takac
NeurIPS 2014 Communication-Efficient Distributed Dual Coordinate Ascent Martin Jaggi, Virginia Smith, Martin Takac, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I Jordan