Developing Legible Visualizations for Online Social Spaces. - danah boyd; Hyun-Yeul Lee; Daniel Ramage; Judith S. Donath (HICSS, 2002)

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 (AAAI Spring Symposium: Machine Reading, 2007)

Learning Alignments and Leveraging Natural Logic. - Nathanael Chambers; Daniel M. Cer; Trond Grenager; David Hall 006; Chloé Kiddon; Bill MacCartney; Marie-Catherine de Marneffe; Daniel Ramage; Eric Yeh; Christopher D. Manning (ACL-PASCAL@ACL, 2007)

Lexical Semantic Relatedness with Random Graph Walks. - Thad Hughes; Daniel Ramage (EMNLP-CoNLL, 2007)

RA: ResearchAssistant for the computational sciences. - Daniel Ramage; Adam J. Oliner (Experimental Computer Science, 2007)

Social tag prediction. - Paul Heymann; Daniel Ramage; Hector Garcia-Molina (SIGIR, 2008)

Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. - Daniel Ramage; David Hall 006; Ramesh Nallapati; Christopher D. Manning (EMNLP, 2009)

Random Walks for Text Semantic Similarity. - Daniel Ramage; Anna N. Rafferty; Christopher D. Manning (Graph-based Methods for Natural Language Processing, 2009)

WikiWalk: Random walks on Wikipedia for Semantic Relatedness. - Eric Yeh; Daniel Ramage; Christopher D. Manning; Eneko Agirre; Aitor Soroa (Graph-based Methods for Natural Language Processing, 2009)

Clustering the tagged web. - Daniel Ramage; Paul Heymann; Christopher D. Manning; Hector Garcia-Molina (WSDM, 2009)

Characterizing Microblogs with Topic Models. - Daniel Ramage; Susan T. Dumais; Daniel J. Liebling (ICWSM, 2010)

Partially labeled topic models for interpretable text mining. - Daniel Ramage; Christopher D. Manning; Susan T. Dumais (KDD, 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 (LaTeCH@ACL, 2011)

#TwitterSearch: a comparison of microblog search and web search. - Jaime Teevan; Daniel Ramage; Meredith Ringel Morris (WSDM, 2011)

Studying people, organizations, and the web with statistical text models. - Daniel Ramage (2011)

Interpretation and trust: designing model-driven visualizations for text analysis. - Jason Chuang; Daniel Ramage; Christopher D. Manning; Jeffrey Heer (CHI, 2012)

Federated Optimization: Distributed Optimization Beyond the Datacenter. - Jakub Konecný; Brendan McMahan; Daniel Ramage (CoRR, 2015)

Discrete Distribution Estimation under Local Privacy. - Peter Kairouz; Kallista A. Bonawitz; Daniel Ramage (ICML, 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)

Federated Optimization: Distributed Machine Learning for On-Device Intelligence. - Jakub Konecný; H. Brendan McMahan; Daniel Ramage; Peter Richtárik (CoRR, 2016)

Federated Learning of Deep Networks using Model Averaging. - H. Brendan McMahan; Eider Moore; Daniel Ramage; Blaise Agüera y Arcas (CoRR, 2016)

Communication-Efficient Learning of Deep Networks from Decentralized Data. - Brendan McMahan; Eider Moore; Daniel Ramage; Seth Hampson; Blaise Agüera y Arcas (AISTATS, 2017)

Learning Differentially Private Language Models Without Losing Accuracy. - H. Brendan McMahan; Daniel Ramage; Kunal Talwar; Li Zhang 001 (CoRR, 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 Recurrent Language Models. - H. Brendan McMahan; Daniel Ramage; Kunal Talwar; Li Zhang 001 (ICLR (Poster), 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)

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)

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; et al. (SysML, 2019)

Federated Evaluation of On-device Personalization. - Kangkang Wang; Rajiv Mathews; Chloé Kiddon; Hubert Eichner; Françoise Beaufays; Daniel Ramage (CoRR, 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; et al. (CoRR, 2019)

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 (ICLR, 2020)

Context Aware Local Differential Privacy. - Jayadev Acharya; Kallista A. Bonawitz; Peter Kairouz; Daniel Ramage; Ziteng Sun (ICML, 2020)

Mapping Three Decades of Intellectual Change in Academia. - Daniel Ramage; Christopher D. Manning; Daniel A. McFarland (CoRR, 2020)

Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Federated Learning. - Virat Shejwalkar; Amir Houmansadr; Peter Kairouz; Daniel Ramage (CoRR, 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; et al. (Found. Trends Mach. Learn., 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)

Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning. - Virat Shejwalkar; Amir Houmansadr; Peter Kairouz; Daniel Ramage (SP, 2022)

Federated learning and privacy. - Kallista A. Bonawitz; Peter Kairouz; Brendan McMahan; Daniel Ramage (Commun. ACM, 2022)

Private Federated Learning in Gboard. - Yuanbo Zhang; Daniel Ramage; Zheng Xu 002; Yanxiang Zhang; Shumin Zhai; Peter Kairouz (CoRR, 2023)

AirGapAgent: Protecting Privacy-Conscious Conversational Agents. - Eugene Bagdasarian; Ren Yi; Sahra Ghalebikesabi; Peter Kairouz; Marco Gruteser; Sewoong Oh; Borja Balle; Daniel Ramage (CCS, 2024)

Federated Learning in Practice: Reflections and Projections. - Katharine Daly; Hubert Eichner; Peter Kairouz; H. Brendan McMahan; Daniel Ramage; Zheng Xu 002 (TPS-ISA, 2024)

Prompt Public Large Language Models to Synthesize Data for Private On-device Applications. - Shanshan Wu; Zheng Xu 002; Yanxiang Zhang; Yuanbo Zhang; 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; et al. (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)

Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptography. - Ilia Shumailov; Daniel Ramage; Sarah Meiklejohn; Peter Kairouz; Florian Hartmann; Borja Balle; Eugene Bagdasarian (CoRR, 2025)

Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications. - Yanxiang Zhang; Zheng Xu; Shanshan Wu; Yuanbo Zhang; Daniel Ramage (CoRR, 2025)