Ananda Theertha Suresh
Ananda Theertha Suresh is a research scientist at Google. He obtained PhD from University of California, San Diego where he was advised by Prof. Alon Orlitsky. His research interests lie in the intersection of machine learning, information theory, and statistics. More details can be found at theertha.info
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Google
Efficient Language Model Architectures for Differentially Private Federated Learning
Yanxiang Zhang
Privacy Regulation and Protection in Machine Learning Workshop at ICLR 2024 (2024) (to appear)
FEDAQT: ACCURATE QUANTIZED TRAINING WITH FEDERATED LEARNING
Renkun Ni
Yonghui Xiao
Oleg Rybakov
Phoenix Meadowlark
Tom Goldstein
2024
Approximating probabilistic models as weighted finite automata
Vlad Schogol
Computational Linguistics, 47 (2021), pp. 221-254
A Field Guide to Federated Optimization
Jianyu Wang
Gauri Joshi
Maruan Al-Shedivat
Galen Andrew
A. Salman Avestimehr
Katharine Daly
Deepesh Data
Suhas Diggavi
Hubert Eichner
Advait Gadhikar
Antonious M. Girgis
Filip Hanzely
Chaoyang He
Samuel Horvath
Martin Jaggi
Tara Javidi
Satyen Chandrakant Kale
Sai Praneeth Karimireddy
Jakub Konečný
Sanmi Koyejo
Tian Li
Peter Richtarik
Karan Singhal
Virginia Smith
Mahdi Soltanolkotabi
Weikang Song
Sebastian Stich
Ameet Talwalkar
Hongyi Wang
Blake Woodworth
Honglin Yuan
Manzil Zaheer
Mi Zhang
Tong Zhang
Chunxiang (Jake) Zheng
Chen Zhu
arxiv (2021)
FedJAX: Federated learning simulation with JAX
Ke Wu
1st NeurIPS Workshop on New Frontiers in Federated Learning (NFFL 2021) (2021)
