Daniel Ramage - RankMe

54 papers A* 10A 4Journal 27Unranked 12

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications. ACL (6) Yanxiang Zhang, Zheng Xu, Shanshan Wu, Yuanbo Zhang, Daniel Ramage
2025	J	`jnl`	Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications. CoRR Yanxiang Zhang, Zheng Xu, Shanshan Wu, Yuanbo Zhang, Daniel Ramage
2025	J	`jnl`	Toward provably private analytics and insights into GenAI use. CoRR Albert Cheu, Artem Lagzdin, Brett McLarnon, Daniel Ramage, Katharine Daly, Marco Gruteser, Peter Kairouz, Rakshita Tandon, Stanislav Chiknavaryan, Timon Van Overveldt, Zoe Gong
2025	J	`jnl`	Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptography. CoRR Ilia Shumailov, Daniel Ramage, Sarah Meiklejohn, Peter Kairouz, Florian Hartmann, Borja Balle, Eugene Bagdasarian
2024	J	`jnl`	Air Gap: Protecting Privacy-Conscious Conversational Agents. CoRR Eugene Bagdasaryan, Ren Yi, Sahra Ghalebikesabi, Peter Kairouz, Marco Gruteser, Sewoong Oh, Borja Balle, Daniel Ramage
2024	A*	`conf`	AirGapAgent: Protecting Privacy-Conscious Conversational Agents. CCS Eugene Bagdasarian, Ren Yi, Sahra Ghalebikesabi, Peter Kairouz, Marco Gruteser, Sewoong Oh, Borja Balle, Daniel Ramage
2024	J	`jnl`	Confidential Federated Computations. CoRR 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
2024	—	`conf`	Federated Learning in Practice: Reflections and Projections. TPS-ISA Katharine Daly, Hubert Eichner, Peter Kairouz, H. Brendan McMahan, Daniel Ramage, Zheng Xu
2024	J	`jnl`	Federated Learning in Practice: Reflections and Projections. CoRR Katharine Daly, Hubert Eichner, Peter Kairouz, H. Brendan McMahan, Daniel Ramage, Zheng Xu
2024	J	`jnl`	Prompt Public Large Language Models to Synthesize Data for Private On-device Applications. CoRR Shanshan Wu, Zheng Xu, Yanxiang Zhang, Yuanbo Zhang, Daniel Ramage
2023	J	`jnl`	Private Federated Learning in Gboard. CoRR Yuanbo Zhang, Daniel Ramage, Zheng Xu, Yanxiang Zhang, Shumin Zhai, Peter Kairouz
2022	A*	`conf`	Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning. SP Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage
2022	J	`jnl`	Federated learning and privacy. Commun. ACM Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage
2021	J	`jnl`	Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 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
2021	J	`jnl`	Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Federated Learning. CoRR Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage
2021	J	`jnl`	Federated Learning and Privacy: Building privacy-preserving systems for machine learning and data science on decentralized data. ACM Queue Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage
2020	A*	`conf`	Context Aware Local Differential Privacy. ICML Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun
2020	A*	`conf`	Generative Models for Effective ML on Private, Decentralized Datasets. ICLR Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Agüera y Arcas
2020	J	`jnl`	Mapping Three Decades of Intellectual Change in Academia. CoRR Daniel Ramage, Christopher D. Manning, Daniel A. McFarland
2019	J	`jnl`	Advances and Open Problems in Federated Learning. CoRR 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
2019	J	`jnl`	Context-Aware Local Differential Privacy. CoRR Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun
2019	J	`jnl`	Federated Evaluation of On-device Personalization. CoRR Kangkang Wang, Rajiv Mathews, Chloé Kiddon, Hubert Eichner, Françoise Beaufays, Daniel Ramage
2019	J	`jnl`	Generative Models for Effective ML on Private, Decentralized Datasets. CoRR Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Agüera y Arcas
2019	—	`conf`	Towards Federated Learning at Scale: System Design. SysML 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
2019	J	`jnl`	Towards Federated Learning at Scale: System Design. CoRR 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
2018	J	`jnl`	Applied Federated Learning: Improving Google Keyboard Query Suggestions. CoRR Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, Françoise Beaufays
2018	J	`jnl`	Federated Learning for Mobile Keyboard Prediction. CoRR Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, Daniel Ramage
2018	—	`conf`	Learning Differentially Private Recurrent Language Models. ICLR (Poster) H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang
2017	A	`conf`	Communication-Efficient Learning of Deep Networks from Decentralized Data. AISTATS Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas
2017	J	`jnl`	Learning Differentially Private Language Models Without Losing Accuracy. CoRR H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang
2017	J	`jnl`	Practical Secure Aggregation for Privacy Preserving Machine Learning. IACR Cryptol. ePrint Arch. Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth
2017	A*	`conf`	Practical Secure Aggregation for Privacy-Preserving Machine Learning. CCS Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth
2016	A*	`conf`	Discrete Distribution Estimation under Local Privacy. ICML Peter Kairouz, Kallista A. Bonawitz, Daniel Ramage
2016	J	`jnl`	Discrete Distribution Estimation under Local Privacy. CoRR Peter Kairouz, Kallista A. Bonawitz, Daniel Ramage
2016	J	`jnl`	Federated Learning of Deep Networks using Model Averaging. CoRR H. Brendan McMahan, Eider Moore, Daniel Ramage, Blaise Agüera y Arcas
2016	J	`jnl`	Federated Optimization: Distributed Machine Learning for On-Device Intelligence. CoRR Jakub Konecný, H. Brendan McMahan, Daniel Ramage, Peter Richtárik
2016	J	`jnl`	Practical Secure Aggregation for Federated Learning on User-Held Data. CoRR Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth
2015	J	`jnl`	Federated Optimization: Distributed Optimization Beyond the Datacenter. CoRR Jakub Konecný, Brendan McMahan, Daniel Ramage
2012	A*	`conf`	Interpretation and trust: designing model-driven visualizations for text analysis. CHI Jason Chuang, Daniel Ramage, Christopher D. Manning, Jeffrey Heer
2011	A	`conf`	#TwitterSearch: a comparison of microblog search and web search. WSDM Jaime Teevan, Daniel Ramage, Meredith Ringel Morris
2011	—	`conf`	A Study of Academic Collaborations in Computational Linguistics using a Latent Mixture of Authors Model. LaTeCH@ACL Nikhil Johri, Daniel Ramage, Daniel A. McFarland, Daniel Jurafsky
2011	A*	`conf`	Partially labeled topic models for interpretable text mining. KDD Daniel Ramage, Christopher D. Manning, Susan T. Dumais
2011	—	`—`	Studying people, organizations, and the web with statistical text models. Daniel Ramage
2010	A	`conf`	Characterizing Microblogs with Topic Models. ICWSM Daniel Ramage, Susan T. Dumais, Daniel J. Liebling
2009	A	`conf`	Clustering the tagged web. WSDM Daniel Ramage, Paul Heymann, Christopher D. Manning, Hector Garcia-Molina
2009	A*	`conf`	Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. EMNLP Daniel Ramage, David Hall, Ramesh Nallapati, Christopher D. Manning
2009	—	`conf`	Random Walks for Text Semantic Similarity. Graph-based Methods for Natural Language Processing Daniel Ramage, Anna N. Rafferty, Christopher D. Manning
2009	—	`conf`	WikiWalk: Random walks on Wikipedia for Semantic Relatedness. Graph-based Methods for Natural Language Processing Eric Yeh, Daniel Ramage, Christopher D. Manning, Eneko Agirre, Aitor Soroa
2008	A*	`conf`	Social tag prediction. SIGIR Paul Heymann, Daniel Ramage, Hector Garcia-Molina
2007	—	`conf`	Learning Alignments and Leveraging Natural Logic. ACL-PASCAL@ACL Nathanael Chambers, Daniel M. Cer, Trond Grenager, David Hall, Chloé Kiddon, Bill MacCartney, Marie-Catherine de Marneffe, Daniel Ramage, Eric Yeh, Christopher D. Manning
2007	—	`conf`	Lexical Semantic Relatedness with Random Graph Walks. EMNLP-CoNLL Thad Hughes, Daniel Ramage
2007	—	`conf`	RA: ResearchAssistant for the computational sciences. Experimental Computer Science Daniel Ramage, Adam J. Oliner
2007	—	`conf`	Robust Graph Alignment Methods for Textual Inference and Machine Reading. AAAI Spring Symposium: Machine Reading Marie-Catherine de Marneffe, Trond Grenager, Bill MacCartney, Daniel M. Cer, Daniel Ramage, Chloé Kiddon, Christopher D. Manning
2002	—	`conf`	Developing Legible Visualizations for Online Social Spaces. HICSS danah boyd, Hyun-Yeul Lee, Daniel Ramage, Judith S. Donath