Daniel Ramage

According to our database, Daniel Ramage authored at least 44 papers between 2002 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Federated Learning in Practice: Reflections and Projections.

Katharine Daly, Hubert Eichner, Peter Kairouz, H. Brendan McMahan, Daniel Ramage, Zheng Xu

CoRR, 2024

Air Gap: Protecting Privacy-Conscious Conversational Agents.

Eugene Bagdasaryan, Ren Yi, Sahra Ghalebikesabi, Peter Kairouz, Marco Gruteser, Sewoong Oh, Borja Balle, Daniel Ramage

CoRR, 2024

Confidential Federated Computations.

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

CoRR, 2024

Prompt Public Large Language Models to Synthesize Data for Private On-device Applications.

Shanshan Wu, Zheng Xu, Yanxiang Zhang, Yuanbo Zhang, Daniel Ramage

CoRR, 2024

AirGapAgent: Protecting Privacy-Conscious Conversational Agents.

Eugene Bagdasarian, Ren Yi, Sahra Ghalebikesabi, Peter Kairouz, Marco Gruteser, Sewoong Oh, Borja Balle, Daniel Ramage

Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, 2024

2023

Private Federated Learning in Gboard.

Yuanbo Zhang, Daniel Ramage, Zheng Xu, Yanxiang Zhang, Shumin Zhai, Peter Kairouz

CoRR, 2023

2022

Federated learning and privacy.

Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage

Commun. ACM, 2022

Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning.

Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage

Proceedings of the 43rd IEEE Symposium on Security and Privacy, 2022

2021

Federated Learning and Privacy: Building privacy-preserving systems for machine learning and data science on decentralized data.

Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage

ACM Queue, 2021

Advances and Open Problems in Federated Learning.

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, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao

Found. Trends Mach. Learn., 2021

Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Federated Learning.

Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage

CoRR, 2021

2020

Mapping Three Decades of Intellectual Change in Academia.

Daniel Ramage, Christopher D. Manning, Daniel A. McFarland

CoRR, 2020

Context Aware Local Differential Privacy.

Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun

Proceedings of the 37th International Conference on Machine Learning, 2020

Generative Models for Effective ML on Private, Decentralized Datasets.

Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Agüera y Arcas

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Advances and Open Problems in Federated Learning.

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, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao

CoRR, 2019

Federated Evaluation of On-device Personalization.

Kangkang Wang, Rajiv Mathews, Chloé Kiddon, Hubert Eichner, Françoise Beaufays, Daniel Ramage

CoRR, 2019

Towards Federated Learning at Scale: System Design.

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

Proceedings of the Second Conference on Machine Learning and Systems, SysML 2019, 2019

2018

Applied Federated Learning: Improving Google Keyboard Query Suggestions.

Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, Françoise Beaufays

CoRR, 2018

Federated Learning for Mobile Keyboard Prediction.

Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, Daniel Ramage

CoRR, 2018

Learning Differentially Private Recurrent Language Models.

H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Practical Secure Aggregation for Privacy Preserving Machine Learning.

Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth

IACR Cryptol. ePrint Arch., 2017

Learning Differentially Private Language Models Without Losing Accuracy.

H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang

CoRR, 2017

Communication-Efficient Learning of Deep Networks from Decentralized Data.

Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Federated Learning of Deep Networks using Model Averaging.

H. Brendan McMahan, Eider Moore, Daniel Ramage, Blaise Agüera y Arcas

CoRR, 2016

Federated Optimization: Distributed Machine Learning for On-Device Intelligence.

Jakub Konecný, H. Brendan McMahan, Daniel Ramage, Peter Richtárik

CoRR, 2016

Practical Secure Aggregation for Federated Learning on User-Held Data.

Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth

CoRR, 2016

Discrete Distribution Estimation under Local Privacy.

Peter Kairouz, Kallista A. Bonawitz, Daniel Ramage

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Federated Optimization: Distributed Optimization Beyond the Datacenter.

Jakub Konecný, Brendan McMahan, Daniel Ramage

CoRR, 2015

2012

Interpretation and trust: designing model-driven visualizations for text analysis.

Jason Chuang, Daniel Ramage, Christopher D. Manning, Jeffrey Heer

Proceedings of the CHI Conference on Human Factors in Computing Systems, 2012

2011

Studying people, organizations, and the web with statistical text models.

Daniel Ramage

PhD thesis, 2011

#TwitterSearch: a comparison of microblog search and web search.

Jaime Teevan, Daniel Ramage, Meredith Ringel Morris

Proceedings of the Forth International Conference on Web Search and Web Data Mining, 2011

A Study of Academic Collaborations in Computational Linguistics using a Latent Mixture of Authors Model.

Nikhil Johri, Daniel Ramage, Daniel A. McFarland, Daniel Jurafsky

Proceedings of the 5th ACL Workshop on Language Technology for Cultural Heritage, 2011

Partially labeled topic models for interpretable text mining.

Daniel Ramage, Christopher D. Manning, Susan T. Dumais

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

2010

Characterizing Microblogs with Topic Models.

Daniel Ramage, Susan T. Dumais, Daniel J. Liebling

Proceedings of the Fourth International Conference on Weblogs and Social Media, 2010

2009

Clustering the tagged web.

Daniel Ramage, Paul Heymann, Christopher D. Manning, Hector Garcia-Molina

Proceedings of the Second International Conference on Web Search and Web Data Mining, 2009

WikiWalk: Random walks on Wikipedia for Semantic Relatedness.

Eric Yeh, Daniel Ramage, Christopher D. Manning, Eneko Agirre, Aitor Soroa

Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing, 2009

Random Walks for Text Semantic Similarity.

Daniel Ramage, Anna N. Rafferty, Christopher D. Manning

Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing, 2009

Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora.

Daniel Ramage, David Hall, Ramesh Nallapati, Christopher D. Manning

Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 2009

2008

Social tag prediction.

Paul Heymann, Daniel Ramage, Hector Garcia-Molina

Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2008

2007

RA: ResearchAssistant for the computational sciences.

Daniel Ramage, Adam J. Oliner

Proceedings of the Workshop on Experimental Computer Science, 2007

Lexical Semantic Relatedness with Random Graph Walks.

Thad Hughes, Daniel Ramage

Proceedings of the EMNLP-CoNLL 2007, 2007

Learning Alignments and Leveraging Natural Logic.

Nathanael Chambers, Daniel M. Cer, Trond Grenager, David Hall, Chloé Kiddon, Bill MacCartney, Marie-Catherine de Marneffe, Daniel Ramage, Eric Yeh, Christopher D. Manning

Proceedings of the ACL-PASCAL@ACL 2007 Workshop on Textual Entailment and Paraphrasing, 2007

Robust Graph Alignment Methods for Textual Inference and Machine Reading.

Marie-Catherine de Marneffe, Trond Grenager, Bill MacCartney, Daniel M. Cer, Daniel Ramage, Chloé Kiddon, Christopher D. Manning

Proceedings of the Machine Reading, 2007

2002

Developing Legible Visualizations for Online Social Spaces.

danah boyd, Hyun-Yeul Lee, Daniel Ramage, Judith S. Donath

Proceedings of the 35th Hawaii International Conference on System Sciences (HICSS-35 2002), 2002