Boosting as a Metaphor for Algorithm Design. - Kevin Leyton-Brown; Eugene Nudelman; Galen Andrew; Jim McFadden; Yoav Shoham (CP, 2003)

A Portfolio Approach to Algorithm Selection. - Kevin Leyton-Brown; Eugene Nudelman; Galen Andrew; Jim McFadden; Yoav Shoham (IJCAI, 2003)

Verb Sense and Subcategorization: Using Joint Inference to Improve Performance on Complementary Task. - Galen Andrew; Trond Grenager; Christopher D. Manning (EMNLP, 2004)

A Conditional Random Field Word Segmenter for Sighan Bakeoff 2005. - Huihsin Tseng; Pi-Chuan Chang; Galen Andrew; Daniel Jurafsky; Christopher D. Manning (SIGHAN@IJCNLP 2005, 2005)

A Hybrid Markov/Semi-Markov Conditional Random Field for Sequence Segmentation. - Galen Andrew (EMNLP, 2006)

Tregex and Tsurgeon: tools for querying and manipulating tree data structures. - Roger Levy; Galen Andrew (LREC, 2006)

A Comparative Study of Parameter Estimation Methods for Statistical Natural Language Processing. - Jianfeng Gao 001; Galen Andrew; Mark Johnson; Kristina Toutanova (ACL, 2007)

Scalable training of L1-regularized log-linear models. - Galen Andrew; Jianfeng Gao 001 (ICML, 2007)

Sequential Deep Belief Networks. - Galen Andrew; Jeff A. Bilmes (ICASSP, 2012)

Memory-efficient inference in dynamic graphical models using multiple cores. - Galen Andrew; Jeff A. Bilmes (AISTATS, 2012)

Deep Canonical Correlation Analysis. - Galen Andrew; Raman Arora; Jeff A. Bilmes; Karen Livescu (ICML (3), 2013)

Interactive Control of Diverse Complex Characters with Neural Networks. - Igor Mordatch; Kendall Lowrey; Galen Andrew; Zoran Popovic; Emanuel Todorov (NIPS, 2015)

New Techniques in Deep Representation Learning. - Galen Andrew (2016)

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)

A General Approach to Adding Differential Privacy to Iterative Training Procedures. - H. Brendan McMahan; Galen Andrew (CoRR, 2018)

Differentially Private Learning with Adaptive Clipping. - Om Thakkar 001; Galen Andrew; H. Brendan McMahan (CoRR, 2019)

Training Production Language Models without Memorizing User Data. - Swaroop Ramaswamy; Om Thakkar 001; Rajiv Mathews; Galen Andrew; H. Brendan McMahan; Françoise Beaufays (CoRR, 2020)

Differentially Private Learning with Adaptive Clipping. - Galen Andrew; Om Thakkar 001; Brendan McMahan; Swaroop Ramaswamy (NeurIPS, 2021)

A Field Guide to Federated Optimization. - Jianyu Wang; Zachary Charles; Zheng Xu 002; Gauri Joshi; H. Brendan McMahan; Blaise Agüera y Arcas; Maruan Al-Shedivat; Galen Andrew; Salman Avestimehr; Katharine Daly; Deepesh Data; Suhas N. Diggavi; et al. (CoRR, 2021)

Federated Learning of Gboard Language Models with Differential Privacy. - Zheng Xu 002; Yanxiang Zhang; Galen Andrew; Christopher A. Choquette-Choo; Peter Kairouz; H. Brendan McMahan; Jesse Rosenstock; Yuanbo Zhang (ACL (industry), 2023)

Unleashing the Power of Randomization in Auditing Differentially Private ML. - Krishna Pillutla; Galen Andrew; Peter Kairouz; H. Brendan McMahan; Alina Oprea; Sewoong Oh (NeurIPS, 2023)

One-shot Empirical Privacy Estimation for Federated Learning. - Galen Andrew; Peter Kairouz; Sewoong Oh; Alina Oprea; Hugh Brendan McMahan; Vinith Menon Suriyakumar (ICLR, 2024)