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Daniel Ramage
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2020 – today
- 2024
- [i20]Shanshan Wu, Zheng Xu, Yanxiang Zhang, Yuanbo Zhang, Daniel Ramage:
Prompt Public Large Language Models to Synthesize Data for Private On-device Applications. CoRR abs/2404.04360 (2024) - [i19]Hubert Eichner, Daniel Ramage, Kallista A. Bonawitz, Dzmitry Huba, Tiziano Santoro, Brett McLarnon, Timon Van Overveldt, Nova Fallen, Peter Kairouz, Albert Cheu, Katharine Daly, Adrià Gascón, Marco Gruteser, Brendan McMahan:
Confidential Federated Computations. CoRR abs/2404.10764 (2024) - [i18]Eugene Bagdasaryan, Ren Yi, Sahra Ghalebikesabi, Peter Kairouz, Marco Gruteser, Sewoong Oh, Borja Balle, Daniel Ramage:
Air Gap: Protecting Privacy-Conscious Conversational Agents. CoRR abs/2405.05175 (2024) - 2023
- [i17]Yuanbo Zhang, Daniel Ramage, Zheng Xu, Yanxiang Zhang, Shumin Zhai, Peter Kairouz:
Private Federated Learning in Gboard. CoRR abs/2306.14793 (2023) - 2022
- [j3]Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage:
Federated learning and privacy. Commun. ACM 65(4): 90-97 (2022) - [c23]Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage:
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning. SP 2022: 1354-1371 - 2021
- [j2]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis
, Arjun Nitin Bhagoji
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14(1-2): 1-210 (2021) - [j1]Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage:
Federated Learning and Privacy: Building privacy-preserving systems for machine learning and data science on decentralized data. ACM Queue 19(5): 87-114 (2021) - [i16]Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage:
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Federated Learning. CoRR abs/2108.10241 (2021) - 2020
- [c22]Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Agüera y Arcas:
Generative Models for Effective ML on Private, Decentralized Datasets. ICLR 2020 - [c21]Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun:
Context Aware Local Differential Privacy. ICML 2020: 52-62 - [i15]Daniel Ramage, Christopher D. Manning, Daniel A. McFarland
Mapping Three Decades of Intellectual Change in Academia. CoRR abs/2004.01291 (2020)
2010 – 2019
- 2019
- [c20]Kallista A. Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander:
Towards Federated Learning at Scale: System Design. SysML 2019 - [i14]Kallista A. Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman
Towards Federated Learning at Scale: System Design. CoRR abs/1902.01046 (2019) - [i13]Kangkang Wang, Rajiv Mathews, Chloé Kiddon, Hubert Eichner, Françoise Beaufays, Daniel Ramage:
Federated Evaluation of On-device Personalization. CoRR abs/1910.10252 (2019) - [i12]Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun:
Context-Aware Local Differential Privacy. CoRR abs/1911.00038 (2019) - [i11]Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Agüera y Arcas:
Generative Models for Effective ML on Private, Decentralized Datasets. CoRR abs/1911.06679 (2019) - [i10]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett
Advances and Open Problems in Federated Learning. CoRR abs/1912.04977 (2019) - 2018
- [c19]H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang:
Learning Differentially Private Recurrent Language Models. ICLR (Poster) 2018 - [i9]Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, Daniel Ramage:
Federated Learning for Mobile Keyboard Prediction. CoRR abs/1811.03604 (2018) - [i8]Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, Françoise Beaufays:
Applied Federated Learning: Improving Google Keyboard Query Suggestions. CoRR abs/1812.02903 (2018) - 2017
- [c18]Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas:
Communication-Efficient Learning of Deep Networks from Decentralized Data. AISTATS 2017: 1273-1282 - [c17]Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth:
Practical Secure Aggregation for Privacy-Preserving Machine Learning. CCS 2017: 1175-1191 - [i7]H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang:
Learning Differentially Private Language Models Without Losing Accuracy. CoRR abs/1710.06963 (2017) - [i6]Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth:
Practical Secure Aggregation for Privacy Preserving Machine Learning. IACR Cryptol. ePrint Arch. 2017: 281 (2017) - 2016
- [c16]Peter Kairouz, Kallista A. Bonawitz, Daniel Ramage:
Discrete Distribution Estimation under Local Privacy. ICML 2016: 2436-2444 - [i5]H. Brendan McMahan, Eider Moore, Daniel Ramage, Blaise Agüera y Arcas:
Federated Learning of Deep Networks using Model Averaging. CoRR abs/1602.05629 (2016) - [i4]Peter Kairouz, Kallista A. Bonawitz, Daniel Ramage:
Discrete Distribution Estimation under Local Privacy. CoRR abs/1602.07387 (2016) - [i3]Jakub Konecný, H. Brendan McMahan, Daniel Ramage, Peter Richtárik:
Federated Optimization: Distributed Machine Learning for On-Device Intelligence. CoRR abs/1610.02527 (2016) - [i2]Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth:
Practical Secure Aggregation for Federated Learning on User-Held Data. CoRR abs/1611.04482 (2016) - 2015
- [i1]Jakub Konecný, Brendan McMahan, Daniel Ramage:
Federated Optimization: Distributed Optimization Beyond the Datacenter. CoRR abs/1511.03575 (2015) - 2012
- [c15]Jason Chuang, Daniel Ramage, Christopher D. Manning
Interpretation and trust: designing model-driven visualizations for text analysis. CHI 2012: 443-452 - 2011
- [b1]Daniel Ramage:
Studying people, organizations, and the web with statistical text models. Stanford University, USA, 2011 - [c14]Daniel Ramage, Christopher D. Manning
Partially labeled topic models for interpretable text mining. KDD 2011: 457-465 - [c13]Nikhil Johri, Daniel Ramage, Daniel A. McFarland, Daniel Jurafsky:
A Study of Academic Collaborations in Computational Linguistics using a Latent Mixture of Authors Model. LaTeCH@ACL 2011: 124-132 - [c12]Jaime Teevan, Daniel Ramage, Meredith Ringel Morris:
#TwitterSearch: a comparison of microblog search and web search. WSDM 2011: 35-44 - 2010
- [c11]Daniel Ramage, Susan T. Dumais, Daniel J. Liebling:
Characterizing Microblogs with Topic Models. ICWSM 2010
2000 – 2009
- 2009
- [c10]Daniel Ramage, David Hall, Ramesh Nallapati, Christopher D. Manning:
Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. EMNLP 2009: 248-256 - [c9]Daniel Ramage, Anna N. Rafferty, Christopher D. Manning:
Random Walks for Text Semantic Similarity. Graph-based Methods for Natural Language Processing 2009: 23-31 - [c8]Eric Yeh, Daniel Ramage, Christopher D. Manning, Eneko Agirre, Aitor Soroa:
WikiWalk: Random walks on Wikipedia for Semantic Relatedness. Graph-based Methods for Natural Language Processing 2009: 41-49 - [c7]Daniel Ramage, Paul Heymann, Christopher D. Manning
Clustering the tagged web. WSDM 2009: 54-63 - 2008
- [c6]Paul Heymann, Daniel Ramage, Hector Garcia-Molina:
Social tag prediction. SIGIR 2008: 531-538 - 2007
- [c5]Marie-Catherine de Marneffe, Trond Grenager, Bill MacCartney, Daniel M. Cer, Daniel Ramage, Chloé Kiddon, Christopher D. Manning:
Robust Graph Alignment Methods for Textual Inference and Machine Reading. AAAI Spring Symposium: Machine Reading 2007: 36-42 - [c4]Nathanael Chambers, Daniel M. Cer, Trond Grenager, David Hall, Chloé Kiddon, Bill MacCartney, Marie-Catherine de Marneffe, Daniel Ramage, Eric Yeh, Christopher D. Manning:
Learning Alignments and Leveraging Natural Logic. ACL-PASCAL@ACL 2007: 165-170 - [c3]Thad Hughes, Daniel Ramage:
Lexical Semantic Relatedness with Random Graph Walks. EMNLP-CoNLL 2007: 581-589 - [c2]Daniel Ramage, Adam J. Oliner:
RA: ResearchAssistant for the computational sciences. Experimental Computer Science 2007: 19 - 2002
- [c1]danah boyd, Hyun-Yeul Lee, Daniel Ramage, Judith S. Donath:
Developing Legible Visualizations for Online Social Spaces. HICSS 2002: 115
Coauthor Index
aka: Brendan McMahan
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last updated on 2024-10-07 21:20 CEST by the dblp team
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