Ananda Theertha Suresh

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 129

Top Venues

CoRR

ICML

ISIT

NeurIPS

Publications

Jae Hun Ro, Srinadh Bhojanapalli, Zheng Xu, Yanxiang Zhang, Ananda Theertha Suresh
Efficient Language Model Architectures for Differentially Private Federated Learning. CoRR (2024)
Joy Qiping Yang, Salman Salamatian, Ziteng Sun, Ananda Theertha Suresh, Ahmad Beirami
Asymptotics of Language Model Alignment. CoRR (2024)
Prathamesh Mayekar, Shubham K. Jha, Ananda Theertha Suresh, Himanshu Tyagi
Wyner-Ziv Estimators for Distributed Mean Estimation With Side Information and Optimization. IEEE Trans. Inf. Theory 70 (4) (2024)
Renkun Ni, Yonghui Xiao, Phoenix Meadowlark, Oleg Rybakov, Tom Goldstein, Ananda Theertha Suresh, Ignacio López-Moreno, Mingqing Chen, Rajiv Mathews
FedAQT: Accurate Quantized Training with Federated Learning. ICASSP (2024)
Ziteng Sun, Jae Hun Ro, Ahmad Beirami, Ananda Theertha Suresh
Optimal Block-Level Draft Verification for Accelerating Speculative Decoding. CoRR (2024)
Ahmad Beirami, Alekh Agarwal, Jonathan Berant, Alexander D'Amour, Jacob Eisenstein, Chirag Nagpal, Ananda Theertha Suresh
Theoretical guarantees on the best-of-n alignment policy. CoRR (2024)
Taehyeon Kim, Ananda Theertha Suresh, Kishore Papineni, Michael Riley, Sanjiv Kumar, Adrian Benton
Towards Fast Inference: Exploring and Improving Blockwise Parallel Drafts. CoRR (2024)
Ziteng Sun, Peter Kairouz, Haicheng Sun, Adrià Gascón, Ananda Theertha Suresh
Private federated discovery of out-of-vocabulary words for Gboard. CoRR (2024)
Ziteng Sun, Ananda Theertha Suresh, Aditya Krishna Menon
The importance of feature preprocessing for differentially private linear optimization. ICLR (2024)
Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh
Subset-Based Instance Optimality in Private Estimation. CoRR (2023)
Ziteng Sun, Ananda Theertha Suresh, Aditya Krishna Menon
The importance of feature preprocessing for differentially private linear optimization. CoRR (2023)
Adrià Gascón, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh
Federated Heavy Hitter Recovery under Linear Sketching. CoRR (2023)
Xuechen Zhang, Mingchen Li, Xiangyu Chang, Jiasi Chen, Amit K. Roy-Chowdhury, Ananda Theertha Suresh, Samet Oymak
FedYolo: Augmenting Federated Learning with Pretrained Transformers. CoRR (2023)
Adrià Gascón, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh
Federated Heavy Hitter Recovery under Linear Sketching. ICML (2023)
Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh
Principled Approaches for Private Adaptation from a Public Source. AISTATS (2023)
Alex Kulesza, Ananda Theertha Suresh, Yuyan Wang
Mean estimation in the add-remove model of differential privacy. CoRR (2023)
Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh
Subset-Based Instance Optimality in Private Estimation. ICML (2023)
Yuhan Liu, Ananda Theertha Suresh, Wennan Zhu, Peter Kairouz, Marco Gruteser
Algorithms for bounding contribution for histogram estimation under user-level privacy. ICML (2023)
Lucas Monteiro Paes, Ananda Theertha Suresh, Alex Beutel, Flávio P. Calmon, Ahmad Beirami
Multi-Group Fairness Evaluation via Conditional Value-at-Risk Testing. CoRR (2023)
Clément L. Canonne, Ziteng Sun, Ananda Theertha Suresh
Concentration Bounds for Discrete Distribution Estimation in KL Divergence. ISIT (2023)
Ziteng Sun, Ananda Theertha Suresh, Jae Hun Ro, Ahmad Beirami, Himanshu Jain, Felix X. Yu
SpecTr: Fast Speculative Decoding via Optimal Transport. NeurIPS (2023)
Clément L. Canonne, Ziteng Sun, Ananda Theertha Suresh
Concentration Bounds for Discrete Distribution Estimation in KL Divergence. CoRR (2023)
Ziteng Sun, Ananda Theertha Suresh, Jae Hun Ro, Ahmad Beirami, Himanshu Jain, Felix X. Yu
SpecTr: Fast Speculative Decoding via Optimal Transport. CoRR (2023)
Pranjal Awasthi, Abhimanyu Das, Rajat Sen, Ananda Theertha Suresh
On the benefits of maximum likelihood estimation for Regression and Forecasting. ICLR (2022)
Jae Hun Ro, Theresa Breiner, Lara McConnaughey, Mingqing Chen, Ananda Theertha Suresh, Shankar Kumar, Rajiv Mathews
Scaling Language Model Size in Cross-Device Federated Learning. CoRR (2022)
Wei-Ning Chen, Christopher A. Choquette-Choo, Peter Kairouz, Ananda Theertha Suresh
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning. CoRR (2022)
Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh
Differentially Private Learning with Margin Guarantees. NeurIPS (2022)
Ananda Theertha Suresh, Ziteng Sun, Jae Ro, Felix X. Yu
Correlated Quantization for Distributed Mean Estimation and Optimization. ICML (2022)
Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh
Differentially Private Learning with Margin Guarantees. CoRR (2022)
Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh
Private Domain Adaptation from a Public Source. CoRR (2022)
Yuhan Liu, Ananda Theertha Suresh, Wennan Zhu, Peter Kairouz, Marco Gruteser
Histogram Estimation under User-level Privacy with Heterogeneous Data. CoRR (2022)
Wei-Ning Chen, Christopher A. Choquette-Choo, Peter Kairouz, Ananda Theertha Suresh
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning. ICML (2022)
Jayadev Acharya, Ayush Jain, Gautam Kamath, Ananda Theertha Suresh, Huanyu Zhang
Robust Estimation for Random Graphs. COLT (2022)
Ananda Theertha Suresh, Ziteng Sun, Jae Hun Ro, Felix X. Yu
Correlated quantization for distributed mean estimation and optimization. CoRR (2022)
Jae Ro, Mingqing Chen, Rajiv Mathews, Mehryar Mohri, Ananda Theertha Suresh
Communication-Efficient Agnostic Federated Averaging. Interspeech (2021)
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu
A Field Guide to Federated Optimization. CoRR (2021)
Prathamesh Mayekar, Ananda Theertha Suresh, Himanshu Tyagi
Wyner-Ziv Estimators: Efficient Distributed Mean Estimation with Side-Information. AISTATS (2021)
Pranjal Awasthi, Abhimanyu Das, Rajat Sen, Ananda Theertha Suresh
On the benefits of maximum likelihood estimation for Regression and Forecasting. CoRR (2021)
Ananda Theertha Suresh
Robust hypothesis testing and distribution estimation in Hellinger distance. AISTATS (2021)
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Ananda Theertha Suresh, Peter Kairouz
On the Renyi Differential Privacy of the Shuffle Model. CoRR (2021)
Ananda Theertha Suresh, Brian Roark, Michael Riley, Vlad Schogol
Approximating Probabilistic Models as Weighted Finite Automata. Comput. Linguistics 47 (2) (2021)
Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh, Ningshan Zhang
A Discriminative Technique for Multiple-Source Adaptation. ICML (2021)
Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh
Relative Deviation Margin Bounds. ICML (2021)
Jae Hun Ro, Ananda Theertha Suresh, Ke Wu
FedJAX: Federated learning simulation with JAX. CoRR (2021)
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh
Shuffled Model of Federated Learning: Privacy, Accuracy and Communication Trade-Offs. IEEE J. Sel. Areas Inf. Theory 2 (1) (2021)
Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh
Remember What You Want to Forget: Algorithms for Machine Unlearning. CoRR (2021)
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh
Shuffled Model of Differential Privacy in Federated Learning. AISTATS (2021)
Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh
Remember What You Want to Forget: Algorithms for Machine Unlearning. NeurIPS (2021)
Wittawat Jitkrittum, Michal Lukasik, Ananda Theertha Suresh, Felix X. Yu, Gang Wang
HD-cos Networks: Efficient Neural Architectures for Secure Multi-Party Computation. CoRR (2021)
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Ananda Theertha Suresh, Peter Kairouz
On the Rényi Differential Privacy of the Shuffle Model. CCS (2021)
Yishay Mansour, Mehryar Mohri, Jae Ro, Ananda Theertha Suresh, Ke Wu
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data. AISTATS (2021)
Corinna Cortes, Mehryar Mohri, Dmitry Storcheus, Ananda Theertha Suresh
Boosting with Multiple Sources. NeurIPS (2021)
Jayadev Acharya, Ayush Jain, Gautam Kamath, Ananda Theertha Suresh, Huanyu Zhang
Robust Estimation for Random Graphs. CoRR (2021)
Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
Breaking the centralized barrier for cross-device federated learning. NeurIPS (2021)
Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh
Learning with User-Level Privacy. NeurIPS (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)
Jae Ro, Mingqing Chen, Rajiv Mathews, Mehryar Mohri, Ananda Theertha Suresh
Communication-Efficient Agnostic Federated Averaging. CoRR (2021)
Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh
Learning with User-Level Privacy. CoRR (2021)
Jenny Hamer, Mehryar Mohri, Ananda Theertha Suresh
FedBoost: A Communication-Efficient Algorithm for Federated Learning. ICML (2020)
Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning. CoRR (2020)
Prathamesh Mayekar, Ananda Theertha Suresh, Himanshu Tyagi
Wyner-Ziv Estimators: Efficient Distributed Mean Estimation with Side Information. CoRR (2020)
Ananda Theertha Suresh
Robust hypothesis testing and distribution estimation in Hellinger distance. CoRR (2020)
Yuhan Liu, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Michael Riley
Learning discrete distributions: user vs item-level privacy. NeurIPS (2020)
Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh
Relative Deviation Margin Bounds. CoRR (2020)
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. ICML (2020)
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs. CoRR (2020)
Yishay Mansour, Mehryar Mohri, Jae Ro, Ananda Theertha Suresh
Three Approaches for Personalization with Applications to Federated Learning. CoRR (2020)
Jayadev Acharya, Ananda Theertha Suresh
Optimal multiclass overfitting by sequence reconstruction from Hamming queries. ALT (2020)
Yuhan Liu, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Michael Riley
Learning discrete distributions: user vs item-level privacy. CoRR (2020)
Yishay Mansour, Mehryar Mohri, Ananda Theertha Suresh, Ke Wu
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data. CoRR (2020)
Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh, Ningshan Zhang
Multiple-Source Adaptation with Domain Classifiers. CoRR (2020)
Mingqing Chen, Ananda Theertha Suresh, Rajiv Mathews, Adeline Wong, Cyril Allauzen, Françoise Beaufays, Michael Riley
Federated Learning of N-gram Language Models. CoRR (2019)
Mingqing Chen, Ananda Theertha Suresh, Rajiv Mathews, Adeline Wong, Cyril Allauzen, Françoise Beaufays, Michael Riley
Federated Learning of N-Gram Language Models. CoNLL (2019)
Nived Rajaraman, Prafulla Chandra, Andrew Thangaraj, Ananda Theertha Suresh
Convergence of Chao Unseen Species Estimator. ISIT (2019)
Venkatadheeraj Pichapati, Ananda Theertha Suresh, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar
AdaCliP: Adaptive Clipping for Private SGD. CoRR (2019)
Ananda Theertha Suresh, Brian Roark, Michael Riley, Vlad Schogol
Approximating probabilistic models as weighted finite automata. CoRR (2019)
Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh
Agnostic Federated Learning. CoRR (2019)
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, H. Brendan McMahan
Can You Really Backdoor Federated Learning? CoRR (2019)
Ananda Theertha Suresh
Differentially private anonymized histograms. CoRR (2019)
Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh
Agnostic Federated Learning. ICML (2019)
Ananda Theertha Suresh
Differentially Private Anonymized Histograms. NeurIPS (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)
Ehsan Variani, Ananda Theertha Suresh, Mitchel Weintraub
West: Word Encoded Sequence Transducers. ICASSP (2019)
Ankit Singh Rawat, Jiecao Chen, Felix X. Yu, Ananda Theertha Suresh, Sanjiv Kumar
Sampled Softmax with Random Fourier Features. CoRR (2019)
Ankit Singh Rawat, Jiecao Chen, Felix X. Yu, Ananda Theertha Suresh, Sanjiv Kumar
Sampled Softmax with Random Fourier Features. NeurIPS (2019)
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning. CoRR (2019)
Ananda Theertha Suresh, Brian Roark, Michael Riley, Vlad Schogol
Distilling weighted finite automata from arbitrary probabilistic models. FSMNLP (2019)
Jayadev Acharya, Ananda Theertha Suresh
Optimal multiclass overfitting by sequence reconstruction from Hamming queries. CoRR (2019)
Yi Hao, Alon Orlitsky, Ananda Theertha Suresh, Yihong Wu
Data Amplification: A Unified and Competitive Approach to Property Estimation. CoRR (2019)
Jayadev Acharya, Moein Falahatgar, Ashkan Jafarpour, Alon Orlitsky, Ananda Theertha Suresh
Maximum Selection and Sorting with Adversarial Comparators. J. Mach. Learn. Res. 19 (2018)
Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Brendan McMahan
cpSGD: Communication-efficient and differentially-private distributed SGD. NeurIPS (2018)
Ehsan Variani, Ananda Theertha Suresh, Mitchel Weintraub
WEST: Word Encoded Sequence Transducers. CoRR (2018)
Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan
cpSGD: Communication-efficient and differentially-private distributed SGD. CoRR (2018)
Yi Hao, Alon Orlitsky, Ananda Theertha Suresh, Yihong Wu
Data Amplification: A Unified and Competitive Approach to Property Estimation. NeurIPS (2018)
Jayadev Acharya, Hirakendu Das, Alon Orlitsky, Ananda Theertha Suresh
A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions. ICML (2017)
Nikhilesh Rajaraman, Andrew Thangaraj, Ananda Theertha Suresh
Minimax risk for missing mass estimation. ISIT (2017)
Moein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati, Ananda Theertha Suresh
Maximum Selection and Ranking under Noisy Comparisons. CoRR (2017)
Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai
Model-Powered Conditional Independence Test. NIPS (2017)
Shankar Kumar, Michael Nirschl, Daniel Niels Holtmann-Rice, Hank Liao, Ananda Theertha Suresh, Felix X. Yu
Lattice rescoring strategies for long short term memory language models in speech recognition. ASRU (2017)
Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan
Distributed Mean Estimation with Limited Communication. ICML (2017)

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Publication Activity (10 Years)

Top Topics

Top Venues

Ananda Theertha Suresh