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2020 – today
- 2024
- [j10]Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi:
Maximizing Global Model Appeal in Federated Learning. Trans. Mach. Learn. Res. 2024 (2024) - [c38]Aashiq Muhamed, Oscar Li, David P. Woodruff, Mona Diab, Virginia Smith:
GRASS: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients. EMNLP 2024: 14978-15003 - [c37]Amrith Setlur, Saurabh Garg, Virginia Smith, Sergey Levine:
Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models. ICML 2024 - [c36]Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith:
Fair Federated Learning via Bounded Group Loss. SaTML 2024: 140-160 - [i57]Lucio M. Dery, Steven Kolawole, Jean-François Kagey, Virginia Smith, Graham Neubig, Ameet Talwalkar:
Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes. CoRR abs/2402.05406 (2024) - [i56]Qi Pang, Shengyuan Hu, Wenting Zheng, Virginia Smith:
Attacking LLM Watermarks by Exploiting Their Strengths. CoRR abs/2402.16187 (2024) - [i55]Pratiksha Thaker, Yash Maurya, Virginia Smith:
Guardrail Baselines for Unlearning in LLMs. CoRR abs/2403.03329 (2024) - [i54]Ziyue Li, Tian Li, Virginia Smith, Jeff Bilmes, Tianyi Zhou:
Many-Objective Multi-Solution Transport. CoRR abs/2403.04099 (2024) - [i53]Shengyuan Hu, Saeed Mahloujifar, Virginia Smith, Kamalika Chaudhuri, Chuan Guo:
Privacy Amplification for the Gaussian Mechanism via Bounded Support. CoRR abs/2403.05598 (2024) - [i52]Kevin Kuo, Arian Raje, Kousik Rajesh, Virginia Smith:
Federated LoRA with Sparse Communication. CoRR abs/2406.05233 (2024) - [i51]Shengyuan Hu, Yiwei Fu, Zhiwei Steven Wu, Virginia Smith:
Jogging the Memory of Unlearned Model Through Targeted Relearning Attack. CoRR abs/2406.13356 (2024) - [i50]Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, Aviral Kumar:
RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. CoRR abs/2406.14532 (2024) - [i49]Aashiq Muhamed, Oscar Li, David P. Woodruff, Mona Diab, Virginia Smith:
Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients. CoRR abs/2406.17660 (2024) - [i48]Steven Kolawole, Don Kurian Dennis, Ameet Talwalkar, Virginia Smith:
Revisiting Cascaded Ensembles for Efficient Inference. CoRR abs/2407.02348 (2024) - [i47]Pratiksha Thaker, Shengyuan Hu, Neil Kale, Yash Maurya, Zhiwei Steven Wu, Virginia Smith:
Position: LLM Unlearning Benchmarks are Weak Measures of Progress. CoRR abs/2410.02879 (2024) - 2023
- [j9]Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith:
On Tilted Losses in Machine Learning: Theory and Applications. J. Mach. Learn. Res. 24: 142:1-142:79 (2023) - [j8]Shengyuan Hu, Steven Wu, Virginia Smith:
Private Multi-Task Learning: Formulation and Applications to Federated Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c35]Sizhe Zhang, Achira Amur, Emma Olson, Peleg Kremer, Xun Jiao, Virginia Smith, Bridget Wadzuk:
Leveraging Machine Learning to Understand Green Stormwater Infrastructure Performance Risks. GHTC 2023: 302-303 - [c34]Tian Li, Manzil Zaheer, Ken Liu, Sashank J. Reddi, Hugh Brendan McMahan, Virginia Smith:
Differentially Private Adaptive Optimization with Delayed Preconditioners. ICLR 2023 - [c33]Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine:
Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. ICLR 2023 - [c32]Michael Kuchnik, Virginia Smith, George Amvrosiadis:
Validating Large Language Models with ReLM. MLSys 2023 - [c31]Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith:
On Noisy Evaluation in Federated Hyperparameter Tuning. MLSys 2023 - [c30]Don Kurian Dennis, Abhishek Shetty, Anish Prasad Sevekari, Kazuhito Koishida, Virginia Smith:
Progressive Ensemble Distillation: Building Ensembles for Efficient Inference. NeurIPS 2023 - [c29]Saurabh Garg, Amrith Setlur, Zachary C. Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan:
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. NeurIPS 2023 - [c28]Oscar Li, James Harrison, Jascha Sohl-Dickstein, Virginia Smith, Luke Metz:
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies. NeurIPS 2023 - [i46]Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach
, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine:
Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. CoRR abs/2302.02931 (2023) - [i45]Shengyuan Hu, Dung Daniel T. Ngo, Shuran Zheng, Virginia Smith, Zhiwei Steven Wu:
Federated Learning as a Network Effects Game. CoRR abs/2302.08533 (2023) - [i44]Don Kurian Dennis, Abhishek Shetty, Anish Sevekari, Kazuhito Koishida, Virginia Smith:
Progressive Knowledge Distillation: Building Ensembles for Efficient Inference. CoRR abs/2302.10093 (2023) - [i43]Oscar Li, James Harrison, Jascha Sohl-Dickstein, Virginia Smith, Luke Metz:
Noise-Reuse in Online Evolution Strategies. CoRR abs/2304.12180 (2023) - [i42]Saurabh Garg, Amrith Setlur, Zachary Chase Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan:
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. CoRR abs/2312.03318 (2023) - [i41]Pratiksha Thaker, Amrith Setlur, Zhiwei Steven Wu, Virginia Smith:
Leveraging Public Representations for Private Transfer Learning. CoRR abs/2312.15551 (2023) - 2022
- [c27]Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, Jeff A. Bilmes:
Diverse Client Selection for Federated Learning via Submodular Maximization. ICLR 2022 - [c26]Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, Chong Wang:
Label Leakage and Protection in Two-party Split Learning. ICLR 2022 - [c25]Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith:
Private Adaptive Optimization with Side information. ICML 2022: 13086-13105 - [c24]Michael Kuchnik, Ana Klimovic
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines. MLSys 2022 - [c23]Ken Ziyu Liu, Shengyuan Hu, Steven Wu, Virginia Smith:
On Privacy and Personalization in Cross-Silo Federated Learning. NeurIPS 2022 - [c22]Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine:
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. NeurIPS 2022 - [i40]Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith:
Private Adaptive Optimization with Side Information. CoRR abs/2202.05963 (2022) - [i39]Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith:
Provably Fair Federated Learning via Bounded Group Loss. CoRR abs/2203.10190 (2022) - [i38]Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi:
To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning. CoRR abs/2205.14840 (2022) - [i37]Amrith Setlur, Benjamin Eysenbach
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. CoRR abs/2206.01367 (2022) - [i36]Ken Ziyu Liu, Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith:
On Privacy and Personalization in Cross-Silo Federated Learning. CoRR abs/2206.07902 (2022) - [i35]Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ken Ziyu Liu, Zheng Xu, Virginia Smith:
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning. CoRR abs/2206.09262 (2022) - [i34]Michael Kuchnik, Virginia Smith, George Amvrosiadis:
Validating Large Language Models with ReLM. CoRR abs/2211.15458 (2022) - [i33]Tian Li, Manzil Zaheer, Ken Ziyu Liu, Sashank J. Reddi, H. Brendan McMahan, Virginia Smith:
Differentially Private Adaptive Optimization with Delayed Preconditioners. CoRR abs/2212.00309 (2022) - [i32]Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith:
On Noisy Evaluation in Federated Hyperparameter Tuning. CoRR abs/2212.08930 (2022) - 2021
- [j7]Michael Kuchnik, George Amvrosiadis, Virginia Smith:
Progressive Compressed Records: Taking a Byte out of Deep Learning Data. Proc. VLDB Endow. 14(11): 2627-2641 (2021) - [j6]Angela Cotugno, Virginia Smith
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. 13(19): 3806 (2021) - [c21]Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith:
Tilted Empirical Risk Minimization. ICLR 2021 - [c20]Don Kurian Dennis, Tian Li, Virginia Smith:
Heterogeneity for the Win: One-Shot Federated Clustering. ICML 2021: 2611-2620 - [c19]Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith:
Ditto: Fair and Robust Federated Learning Through Personalization. ICML 2021: 6357-6368 - [c18]Amrith Setlur, Oscar Li, Virginia Smith:
Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution. NeurIPS 2021: 3770-3783 - [c17]Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar:
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. NeurIPS 2021: 19184-19197 - [c16]Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith:
On Large-Cohort Training for Federated Learning. NeurIPS 2021: 20461-20475 - [i31]Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, Chong Wang:
Label Leakage and Protection in Two-party Split Learning. CoRR abs/2102.08504 (2021) - [i30]Amrith Setlur, Oscar Li, Virginia Smith:
Lessons from Chasing Few-Shot Learning Benchmarks: Rethinking the Evaluation of Meta-Learning Methods. CoRR abs/2102.11503 (2021) - [i29]Don Kurian Dennis, Tian Li, Virginia Smith:
Heterogeneity for the Win: One-Shot Federated Clustering. CoRR abs/2103.00697 (2021) - [i28]Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar:
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. CoRR abs/2106.04502 (2021) - [i27]Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith:
On Large-Cohort Training for Federated Learning. CoRR abs/2106.07820 (2021) - [i26]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
A Field Guide to Federated Optimization. CoRR abs/2107.06917 (2021) - [i25]Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith:
Private Multi-Task Learning: Formulation and Applications to Federated Learning. CoRR abs/2108.12978 (2021) - [i24]Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith:
On Tilted Losses in Machine Learning: Theory and Applications. CoRR abs/2109.06141 (2021) - [i23]Michael Kuchnik, Ana Klimovic, Jiri Simsa, George Amvrosiadis, Virginia Smith:
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines. CoRR abs/2111.04131 (2021) - 2020
- [j5]Tian Li, Anit Kumar Sahu
Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Process. Mag. 37(3): 50-60 (2020) - [c15]Tian Li, Maziar Sanjabi, Ahmad Beirami, Virginia Smith:
Fair Resource Allocation in Federated Learning. ICLR 2020 - [c14]Tianlong Yu, Tian Li, Yuqiong Sun, Susanta Nanda, Virginia Smith, Vyas Sekar, Srinivasan Seshan
Learning Context-Aware Policies from Multiple Smart Homes via Federated Multi-Task Learning. IoTDI 2020: 104-115 - [c13]Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith:
Federated Optimization in Heterogeneous Networks. MLSys 2020 - [i22]Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith:
FedDANE: A Federated Newton-Type Method. CoRR abs/2001.01920 (2020) - [i21]Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith:
Tilted Empirical Risk Minimization. CoRR abs/2007.01162 (2020) - [i20]Amrith Setlur, Oscar Li, Virginia Smith:
Is Support Set Diversity Necessary for Meta-Learning? CoRR abs/2011.14048 (2020) - [i19]Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith:
Federated Multi-Task Learning for Competing Constraints. CoRR abs/2012.04221 (2020)
2010 – 2019
- 2019
- [c12]Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith:
FedDANE: A Federated Newton-Type Method. ACSSC 2019: 1227-1231 - [c11]Michael Kuchnik, Virginia Smith:
Efficient Augmentation via Data Subsampling. ICLR (Poster) 2019 - [c10]Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, Christopher Ré:
A Kernel Theory of Modern Data Augmentation. ICML 2019: 1528-1537 - [e1]Ameet Talwalkar, Virginia Smith, Matei Zaharia:
Proceedings of the Second Conference on Machine Learning and Systems, SysML 2019, Stanford, CA, USA, March 31 - April 2, 2019. mlsys.org 2019 [contents] - [i18]Neel Guha, Ameet Talwalkar, Virginia Smith:
One-Shot Federated Learning. CoRR abs/1902.11175 (2019) - [i17]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
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i16]Tian Li, Maziar Sanjabi, Virginia Smith:
Fair Resource Allocation in Federated Learning. CoRR abs/1905.10497 (2019) - [i15]Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith:
Federated Learning: Challenges, Methods, and Future Directions. CoRR abs/1908.07873 (2019) - [i14]Michael Kuchnik, George Amvrosiadis, Virginia Smith:
Progressive Compressed Records: Taking a Byte out of Deep Learning Data. CoRR abs/1911.00472 (2019) - [i13]Tian Li, Zaoxing Liu, Vyas Sekar, Virginia Smith:
Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches. CoRR abs/1911.00972 (2019) - [i12]Zaoxing Liu, Tian Li, Virginia Smith, Vyas Sekar:
Enhancing the Privacy of Federated Learning with Sketching. CoRR abs/1911.01812 (2019) - 2018
- [i11]Tri Dao, Albert Gu, Alexander J. Ratner, Virginia Smith, Christopher De Sa, Christopher Ré:
A Kernel Theory of Modern Data Augmentation. CoRR abs/1803.06084 (2018) - [i10]Michael Kuchnik, Virginia Smith:
Efficient Augmentation via Data Subsampling. CoRR abs/1810.05222 (2018) - [i9]Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar:
LEAF: A Benchmark for Federated Settings. CoRR abs/1812.01097 (2018) - [i8]Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith:
On the Convergence of Federated Optimization in Heterogeneous Networks. CoRR abs/1812.06127 (2018) - 2017
- [b1]Virginia Smith:
System-Aware Optimization for Machine Learning at Scale. University of California, Berkeley, USA, 2017 - [j4]Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi:
CoCoA: A General Framework for Communication-Efficient Distributed Optimization. J. Mach. Learn. Res. 18: 230:1-230:49 (2017) - [j3]Chenxin Ma, Jakub Konecný, Martin Jaggi
Distributed optimization with arbitrary local solvers. Optim. Methods Softw. 32(4): 813-848 (2017) - [c9]Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet Talwalkar:
Federated Multi-Task Learning. NIPS 2017: 4424-4434 - [i7]Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet Talwalkar:
Federated Multi-Task Learning. CoRR abs/1705.10467 (2017) - 2016
- [j2]Gustavo Ansaldi Oliva, Marco Aurélio Gerosa, Fabio Kon
A Static Change Impact Analysis Approach based on Metrics and Visualizations to Support the Evolution of Workflow Repositories. Int. J. Web Serv. Res. 13(2): 74-101 (2016) - [i6]Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi:
CoCoA: A General Framework for Communication-Efficient Distributed Optimization. CoRR abs/1611.02189 (2016) - 2015
- [c8]Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác:
Adding vs. Averaging in Distributed Primal-Dual Optimization. ICML 2015: 1973-1982 - [c7]Virginia Smith, Miriam Connor, Isabelle Stanton:
Going In-Depth: Finding Longform on the Web. KDD 2015: 2109-2118 - [i5]Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác:
Adding vs. Averaging in Distributed Primal-Dual Optimization. CoRR abs/1502.03508 (2015) - [i4]Virginia Smith, Simone Forte, Michael I. Jordan, Martin Jaggi:
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework. CoRR abs/1512.04011 (2015) - [i3]Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác:
Distributed Optimization with Arbitrary Local Solvers. CoRR abs/1512.04039 (2015) - 2014
- [c6]Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan:
Communication-Efficient Distributed Dual Coordinate Ascent. NIPS 2014: 3068-3076 - [i2]Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan:
Communication-Efficient Distributed Dual Coordinate Ascent. CoRR abs/1409.1458 (2014) - 2013
- [c5]Virginia Smith, Jitendra Malik, David E. Culler:
Classification of sidewalks in street view images. IGCC 2013: 1-6 - [c4]Evan Randall Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez
MLI: An API for Distributed Machine Learning. ICDM 2013: 1187-1192 - [c3]Gustavo Ansaldi Oliva, Marco Aurélio Gerosa, Dejan S. Milojicic
A Change Impact Analysis Approach for Workflow Repository Management. ICWS 2013: 308-315 - [c2]Jay Taneja, Virginia Smith, David E. Culler, Catherine Rosenberg:
A comparative study of high renewables penetration electricity grids. SmartGridComm 2013: 49-54 - [i1]Evan Randall Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska:
MLI: An API for Distributed Machine Learning. CoRR abs/1310.5426 (2013) - 2012
- [j1]Virginia Smith, Tamim I. Sookoor, Kamin Whitehouse:
Modeling building thermal response to HVAC zoning. SIGBED Rev. 9(3): 39-45 (2012) - [c1]Anil Aswani
Identifying models of HVAC systems using semiparametric regression. ACC 2012: 3675-3680 - [p1]Keith Duddy, Matthias Heinrich, Steffen Heinzl, Martin Knechtel, Carlos Pedrinaci, Benjamin Schmeling, Virginia Smith:
Representing USDL for Humans and Tools. Handbook of Service Description 2012: 357-383
Coauthor Index
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