Daniel Ramage

Publication Activity (10 Years)

Years Active: 2002-2024
Publications (10 Years): 30

Top Topics

Differentially Private

Conversational Agent

Top Venues

Publications

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 (2024)
Eugene Bagdasaryan, Ren Yi, Sahra Ghalebikesabi, Peter Kairouz, Marco Gruteser, Sewoong Oh, Borja Balle, Daniel Ramage
Air Gap: Protecting Privacy-Conscious Conversational Agents. CoRR (2024)
Shanshan Wu, Zheng Xu, Yanxiang Zhang, Yuanbo Zhang, Daniel Ramage
Prompt Public Large Language Models to Synthesize Data for Private On-device Applications. CoRR (2024)
Yuanbo Zhang, Daniel Ramage, Zheng Xu, Yanxiang Zhang, Shumin Zhai, Peter Kairouz
Private Federated Learning in Gboard. CoRR (2023)
Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning. IEEE Symposium on Security and Privacy (2022)
Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage
Federated learning and privacy. Commun. ACM 65 (4) (2022)
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) (2021)
Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Federated Learning. CoRR (2021)
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
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14 (1-2) (2021)
Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun
Context Aware Local Differential Privacy. ICML (2020)
Daniel Ramage, Christopher D. Manning, Daniel A. McFarland
Mapping Three Decades of Intellectual Change in Academia. CoRR (2020)
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)
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 (2019)
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. MLSys (2019)
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
Advances and Open Problems in Federated Learning. CoRR (2019)
Kallista A. Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander
Towards Federated Learning at Scale: System Design. CoRR (2019)
Kangkang Wang, Rajiv Mathews, Chloé Kiddon, Hubert Eichner, Françoise Beaufays, Daniel Ramage
Federated Evaluation of On-device Personalization. CoRR (2019)
Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun
Context-Aware Local Differential Privacy. CoRR (2019)
Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, Daniel Ramage
Federated Learning for Mobile Keyboard Prediction. CoRR (2018)
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang
Learning Differentially Private Recurrent Language Models. ICLR (Poster) (2018)
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 (2018)
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)
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 (2017)
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas
Communication-Efficient Learning of Deep Networks from Decentralized Data. AISTATS (2017)
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang
Learning Differentially Private Language Models Without Losing Accuracy. CoRR (2017)
Peter Kairouz, Kallista A. Bonawitz, Daniel Ramage
Discrete Distribution Estimation under Local Privacy. CoRR (2016)
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 (2016)
Jakub Konecný, H. Brendan McMahan, Daniel Ramage, Peter Richtárik
Federated Optimization: Distributed Machine Learning for On-Device Intelligence. CoRR (2016)
H. Brendan McMahan, Eider Moore, Daniel Ramage, Blaise Agüera y Arcas
Federated Learning of Deep Networks using Model Averaging. CoRR (2016)
Peter Kairouz, Kallista A. Bonawitz, Daniel Ramage
Discrete Distribution Estimation under Local Privacy. ICML (2016)
Jakub Konecný, Brendan McMahan, Daniel Ramage
Federated Optimization: Distributed Optimization Beyond the Datacenter. CoRR (2015)
Jason Chuang, Daniel Ramage, Christopher D. Manning, Jeffrey Heer
Interpretation and trust: designing model-driven visualizations for text analysis. CHI (2012)
Daniel Ramage, Christopher D. Manning, Susan T. Dumais
Partially labeled topic models for interpretable text mining. KDD (2011)
Jaime Teevan, Daniel Ramage, Meredith Ringel Morris
#TwitterSearch: a comparison of microblog search and web search. WSDM (2011)
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)
Daniel Ramage, Susan T. Dumais, Daniel J. Liebling
Characterizing Microblogs with Topic Models. ICWSM (2010)
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)
Daniel Ramage, Paul Heymann, Christopher D. Manning, Hector Garcia-Molina
Clustering the tagged web. WSDM (2009)
Daniel Ramage, David Hall, Ramesh Nallapati, Christopher D. Manning
Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. EMNLP (2009)
Daniel Ramage, Anna N. Rafferty, Christopher D. Manning
Random Walks for Text Semantic Similarity. Graph-based Methods for Natural Language Processing (2009)
Paul Heymann, Daniel Ramage, Hector Garcia-Molina
Social tag prediction. SIGIR (2008)
Thad Hughes, Daniel Ramage
Lexical Semantic Relatedness with Random Graph Walks. EMNLP-CoNLL (2007)
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)
Daniel Ramage, Adam J. Oliner
RA: ResearchAssistant for the computational sciences. Experimental Computer Science (2007)
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)
danah boyd, Hyun-Yeul Lee, Daniel Ramage, Judith S. Donath
Developing Legible Visualizations for Online Social Spaces. HICSS (2002)