Ananda Theertha Suresh _News & Media

Ananda Theertha Suresh Google Research, New Yorkemail: theertha at google dot com[Google scholar] I am a research scientist at Google Research, New York. I obtained my PhD in electrical and computer engineering from University of California, San Diego, where I was advised by Alon Orlitsky. Prior to joining UCSD, I obtained a Bachelor's degree in Engineering Physics from Indian Institute of Technology, Madras. Research I am broadly interested in theoretical and algorithmic aspects of machine learning, information theory, and statistics. My current research focus includes differential privacy, federated learning, and domain adaptation. Publications On the benefits of maximum likelihood estimation for Regression and Forecasting, ICLR 2022 withP. Awasthi, A. Das, R. Sen [pdf] Robust Estimation for Random Graphs, Manuscript withJ. Acharya, A. Jain, G. Kamath, and H. Zhang[pdf] Learning with User-Level Privacy, NeurIPS 2021 withD. Levy, Z. Sun, K. Amin, S. Kale, A. Kulesza, and M. Mohri[pdf]Remember what you want to forget: Algorithms for machine unlearning, NeurIPS 2021 withA. Sekhari, J. Acharya, and G. Kamath[pdf] Breaking the centralized barrier for cross-device federated learning, NeurIPS 2021 withS. Karimireddy, M. Jaggi, S. Kale, M. Mohri, S. Reddi, and S. Stich[pdf] Boosting with Multiple Sources, NeurIPS 2021 withC. Cortes, M. Mohri, and D. Storcheus[pdf] FedJAX: Federated learning simulation with JAX, NeurIPS FL workshop 2021 withJ. H. Ro and K. Wu[pdf] On the Renyi Differential Privacy of the Shuffle Model, CCS 2021 withA. Girgis, D. Data, S Diggavi, and P. Kairouz[pdf] (Best paper award) Communication-Efficient Agnostic Federated Averaging, Interspeech 2021 withJ. Ro, M. Chen, R. Mathews, and M. Mohri[pdf] A discriminative technique for multiple-source adaptation, ICML 2021 withC. Cortes, M. Mohri, and N. Zhang[pdf]Relative deviation margin bounds, ICML 2021 with C. Cortes and M. Mohri[pdf] Wyner-Ziv Estimators: Efficient Distributed Mean Estimation with Side Information, AISTATS 2021 with P. Mayekar and H. Tyagi[pdf]Robust hypothesis testing and distribution estimation in Hellinger distance, AISTATS 2021[pdf]Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs, IEEE Journal on Selected Areas in Information Theory withA. Girgis, D. Data, S Diggavi, and P. Kairouz[longer version] Shuffled Model of Differential Privacy in Federated Learning, AISTATS 2021 withA. Girgis, D. Data, S Diggavi, and P. Kairouz[pdf] A Theory of Multiple-Source Adaptation with Limited Target Labeled Data, AISTATS 2021 withY. Mansour, M. Mohri, J. Ro, and K. Wu[pdf]HD-cos Networks: Efficient Neural Architectures for Secure Multi-Party Computation, Manuscript with W. Jitkrittum, M. Lukasik, F. Yu, and G. Wang[pdf] A field guide to federated optimization, Manuscript withJ. Wang et al[pdf] Learning discrete distributions: user vs item-level privacy, NeurIPS 2020 withY. Liu, F. Yu, S. Kumar, and M. Riley[pdf]Optimal multiclass overfitting by sequence reconstruction from hamming queries, ALT 2020 withJ. Acharya [pdf] (Best paper award)Scaffold: Stochastic controlled averaging for federated learning, ICML 2020 withS. Karimireddy, S. Kale, M. Mohri, S. Reddi, and S. Stich[pdf]FedBoost: A Communication-Efficient Algorithm for Federated Learning, ICML 2020 withJ. Hamer and M. Mohri[pdf]Three approaches for personalization with applications to federated learning, Manuscript withY. Mansour, M. Mohri, and J. Ro[pdf] Can you really backdoor federated learning?, NeurIPS Federated Learning for Data Privacy and Confidentiality workshop 2019 withZ. Sun, P. Kairouz, and B. McMahan[pdf]AdaCliP: Adaptive clipping for private SGD, TPDP workshop 2020 withV. Pichapati, F. Yu, S Reddi, and S. Kumar[pdf] Federated learning of N-gram language models, CoNLL 2019 withM. Chen, R. Mathews, A. Wong, C. Allauzen, F. Beaufays, and M. Riley[pdf]Convergence of Chao unseen species estimator, ISIT 2019 withN. Rajaraman, P. Chandra, and A. Thangaraj[pdf]Approximating probabilistic models as weighted finite automata, Computational Linguistics Journal withB. Roark, M. Riley, and V. Schogol[pdf]West: Word encoded sequence transducers, ICASSP 2019 withE. Variani and M. Weintraub[pdf]Agnostic federated learning, ICML 2019 withM. Mohri and G. Sivek[pdf]Differentially private anonymized histograms, NeurIPS 2019[pdf]Distilling weighted finite automata from arbitrary probabilistic models, FSMLNP 2019 withB. Roark, M. Riley, and V. Schogol[pdf]Sampled softmax with random fourier features, NeurIPS 2019 withA. Rawat, J. Chen, F. Yu, and S. Kumar[pdf]Advances and Open Problems in Federated learning, Manuscript withP. Kairouz et al[pdf]Maximum selection and sorting with adversarial comparators, JMLR 2018 withJ. Acharya, M. Falahatgar, A. Jafarpor, and A. Orlitsky[pdf]Data amplification: A unified and competitive approach to property estimation, NeurIPS 2018 withY. Hao, A. Orlitsky, and Y. Wu[pdf]cp-sgd: Communication-efficient and differentially-private distributed SGD, NeurIPS 2018 withN. Agarwal, F. Yu, S. Kumar, and B. McMahan[pdf] (Spotlight presentation) Minimax risk for missing mass estimation, ISIT 2017 withN. Rajaraman, P. Chandra, and A. Thangaraj[pdf]Model-powered conditional independence test, NeurIPS 2017 withR. Sen, K. Shanmugam, A. Dimakis, and S. Shakkottai[pdf]Multiscale quantization for fast similarity search, NeurIPS 2017 withX. Wu, R. Guo, D. Holtmann-Rice, D. Simcha, F. Yu, and S. Kumar[pdf]Lattice rescoring strategies for long short term memory language models in speech recognition, ASRU 2017 withS. Kumar, M. Nirschl, D. Holtmann-Rice, H. Liao, and F. Yu[pdf]Distributed mean estimation with limited communication, ICML 2017 withF. Yu, H. B. McMahan, and S. Kumar[pdf]A unified maximum likelihood approach for optimal distribution property estimation, ICML 2017 withJ. Acharya, H. Das, and A. Orlitsky[pdf] (Best paper award honorable mention)Maximum selection and ranking under noisy comparisions, ICML 2017 withM. Falahatgar, A. Orlitsky, and V. Pichapati[pdf]Sample complexity of population recovery, COLT 2017 withY. Polyanskiy and Y. Wu[pdf]Orthogonal random features, NeurIPS 2016 withF. Yu, K. Choromanski, D. Holtmann-Rice, and S. Kumar[pdf] (Oral presentation)Federated learning: Strategies for improving communication efficiency, NeurIPS PMPML workshop 2016 withJ. Konecny, H. B. McMahan, F. X. Yu, P. Richtarik, and D. Bacon [pdf] Optimal prediction of the number of unseen species, PNAS 2016 withA. Orlitsky and Y. Wu[pdf]Learning Markov distributions: Does estimation trump compression?, ISIT 2016 withM. Falahatgar, A. Orlitsky, and V. Pichapati[pdf]Estimating the number of defectives with group testing, ISIT 2016 withM. Falahatgar, A. Jafarpour, A. Orlitsky, and V. Pichapati[pdf]Competitive distribution estimation: Why is Good-Turing good, NeurIPS 2015 withA. Orlitsky[pdf] [talk] (Best paper award)Faster algorithms for testing under conditional sampling,COLT 2015 withM. Falahatgar, A. Jafarpour, A. Orlitsky, and V. Pichapati[jmlr]On learning distributions from their samples,COLT 2015 withS. Kamath, A. Orlitsky, and V. Pichapati[jmlr]Automata and graph compression, ISIT 2015 withM. Mohri and M. Riley[pdf] [implementation] Universal compression of power-law distributions, ISIT 2015 withM. Falahatgar, A. Jafarpour, A. Orlitsky, and V. Pichapati[pdf]Sparse solutions to nonnegative linear systems and applications, AISTATS 2015 withA. Bhaskara and M. Zaghimoghaddam [arXiv]The complexity of estimating Renyi entropy,SODA 2015 withJ. Acharya, A. Orlitsky and H. Tyagi [arXiv]Near-optimal-sample estimators for spherical Gaussian mixtures,NeurIPS 2014 withJ. Acharya, A. Jafarpour, and A. Orlitsky [arXiv] [talk at simons]Sorting with adversarial comparators and application to density estimation,ISIT 2014 withJ. Acharya, A. Jafarpour, and A. Orlitsky [pdf]Efficient compression of monotone and m-modal distributions,ISIT 2014 withJ. Acharya, A. Jafarpour, and A. Orlitsky [pdf]Poissonization and universal compression of envelope classes,ISIT 2014 withJ. Acharya, A. Jafarpour, and A. Orlitsky [pdf]Sublinear algorithms for outlier detection and generalized closeness testing,ISIT 2014 withJ. Acharya, A. Jafarpour, and A. Orlitsky [pdf]Optimal probability estimation with applications to prediction and classification,COLT 2013 withJ. Acharya, A. Jafarpour, and A. Orlitsky [pdf] [talk] Tight Bounds for Universal Compression of Large Alphabets,ISIT 2013 withJ. Acharya, H. Das, A. Jafarpour, and A. Orlitsky [pdf]A competitive test for uniformity of monotone distributions,AISTATS 2013 withJ. Acharya, A. Jafarpour, and A. Orlitsky [pdf]Competitive classification and closeness testing,COLT 2012 with J. Acharya, H. Das, A. Jafarpour, A. Orlitsky, and S. Pan [pdf] [talk]On the query computation and verification of functions,ISIT 2012 withH. Das, A. Jafarpour, A. Orlitsky, and S. Pan [pdf]Strong and weak secrecy in wiretap channels, invited paper atISTC 2010 A. Subramanian, A. T. Suresh, S. Raj, A. Thangaraj, M. Bloch, and S. W. McLaughlin [pdf]Strong secrecy for erasure wiretap channels,ITW 2010 A. T. Suresh, A. Subramanian, A. Thangaraj, M. Bloch, and S. W. McLaughlin [pdf]On optimal timer-based distributed selection For rate-adaptive multi-user diversity systems,NCC 2010 A. T. Suresh, N. B. Mehta, and V. Shah [pdf](Best paper award in communications track)