Artiklar med krav på offentlig åtkomst - Virginia SmithLäs mer
Tillgängliga någonstans: 21
Federated optimization in heterogeneous networks
T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith
Conference on Machine Learning and Systems, 2020
Krav: US National Science Foundation, US Department of Defense
Ditto: Fair and robust federated learning through personalization
T Li, S Hu, A Beirami, V Smith
International conference on machine learning, 6357-6368, 2021
Krav: US National Science Foundation
CoCoA: A general framework for communication-efficient distributed optimization
V Smith, S Forte, C Ma, M Takáč, MI Jordan, M Jaggi
Journal of Machine Learning Research 18 (230), 1-49, 2018
Krav: US National Science Foundation, Swiss National Science Foundation, US …
A kernel theory of modern data augmentation
T Dao, A Gu, A Ratner, V Smith, C De Sa, C Ré
International conference on machine learning, 1528-1537, 2019
Krav: US National Science Foundation, US Department of Defense, US National …
Heterogeneity for the win: One-shot federated clustering
DK Dennis, T Li, V Smith
International Conference on Machine Learning, 2611-2620, 2021
Krav: US National Science Foundation
Diverse client selection for federated learning via submodular maximization
R Balakrishnan, T Li, T Zhou, N Himayat, V Smith, J Bilmes
International Conference on Learning Representations, 2022
Krav: US National Science Foundation, US Department of Defense
Learning context-aware policies from multiple smart homes via federated multi-task learning
T Yu, T Li, Y Sun, S Nanda, V Smith, V Sekar, S Seshan
2020 IEEE/ACM Fifth international conference on internet-of-things design …, 2020
Krav: US National Science Foundation, US Department of Defense
Federated hyperparameter tuning: Challenges, baselines, and connections to weight-sharing
M Khodak, R Tu, T Li, L Li, MFF Balcan, V Smith, A Talwalkar
Advances in Neural Information Processing Systems 34, 19184-19197, 2021
Krav: US National Science Foundation, US Department of Defense
On privacy and personalization in cross-silo federated learning
K Liu, S Hu, SZ Wu, V Smith
Advances in neural information processing systems 35, 5925-5940, 2022
Krav: US National Science Foundation
{SketchLib}: Enabling efficient sketch-based monitoring on programmable switches
H Namkung, Z Liu, D Kim, V Sekar, P Steenkiste
19th USENIX Symposium on Networked Systems Design and Implementation (NSDI …, 2022
Krav: US National Science Foundation, US Department of Defense
Plumber: Diagnosing and removing performance bottlenecks in machine learning data pipelines
M Kuchnik, A Klimovic, J Simsa, V Smith, G Amvrosiadis
Proceedings of Machine Learning and Systems 4, 33-51, 2022
Krav: US Department of Defense
Private adaptive optimization with side information
T Li, M Zaheer, S Reddi, V Smith
International Conference on Machine Learning, 13086-13105, 2022
Krav: US National Science Foundation
Provably fair federated learning via bounded group loss
S Hu, ZS Wu, V Smith
arXiv preprint arXiv:2203.10190 7, 2022
Krav: US National Science Foundation
Validating large language models with relm
M Kuchnik, V Smith, G Amvrosiadis
Proceedings of Machine Learning and Systems 5, 457-476, 2023
Krav: US Department of Defense
Two sides of meta-learning evaluation: In vs. out of distribution
A Setlur, O Li, V Smith
Advances in neural information processing systems 34, 3770-3783, 2021
Krav: US National Science Foundation
Federated multi-task learning for competing constraints
T Li, S Hu, A Beirami, V Smith
arXiv preprint arXiv:2012.04221, 2020
Krav: US National Science Foundation
Raregan: Generating samples for rare classes
Z Lin, H Liang, G Fanti, V Sekar
Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7506-7515, 2022
Krav: US National Science Foundation, US Department of Defense
On noisy evaluation in federated hyperparameter tuning
K Kuo, P Thaker, M Khodak, J Nguyen, D Jiang, A Talwalkar, V Smith
Proceedings of Machine Learning and Systems 5, 127-144, 2023
Krav: US National Science Foundation
Progressive ensemble distillation: Building ensembles for efficient inference
D Dennis, A Shetty, AP Sevekari, K Koishida, V Smith
Advances in Neural Information Processing Systems 36, 43525-43543, 2023
Krav: US National Science Foundation
On the benefits of public representations for private transfer learning under distribution shift
P Thaker, A Setlur, S Wu, V Smith
The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2023
Krav: US National Science Foundation
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