Ananda Theertha Suresh
Google Research, New York |
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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 interested in theoretical and algorithmic aspects of machine learning, information theory, and statistics.
Publications
Wyner-Ziv Estimators: Efficient Distributed Mean Estimation with Side Information, Manuscript
with P. Mayekar and H. Tyagi [pdf]FedBoost: A Communication-Efficient Algorithm for Federated Learning, ICML 2020
with J. Hamer and M. Mohri [pdf]Robust hypothesis testing and distribution estimation in Hellinger distance, Manuscript [pdf]
Multiple-Source Adaptation with Domain Classifiers, Manuscript
with C. Cortes, M. Mohri, and N. Zhang [pdf]Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs, Manuscript
with A. Girgis, D. Data, S Diggavi, and P. Kairouz [pdf]Mime: Mimicking centralized stochastic algorithms in federated learning, Manuscript
with S. Karimireddy, M. Jaggi, S. Kale, M. Mohri, S. Reddi, and S. Stich [pdf]A Theory of Multiple-Source Adaptation with Limited Target Labeled Data, Manuscript
with Y. Mansour, M. Mohri, J. Ro, and K. Wu [pdf]Learning discrete distributions: user vs item-level privacy, NeurIPS 2020
with Y. Liu, F. Yu, S. Kumar, and M. Riley [pdf]Relative deviation margin bounds, Manuscript
with C. Cortes and M. Mohri [pdf]Three approaches for personalization with applications to federated learning, Manuscript
with Y. Mansour, M. Mohri, and J. Ro [pdf]Optimal multiclass overfitting by sequence reconstruction from hamming queries, ALT 2020
with J. Acharya [pdf] (Best paper award)Can you really backdoor federated learning?, NeurIPS Federated Learning for Data Privacy and Confidentiality workshop 2019
with Z. Sun, P. Kairouz, and B. McMahan [pdf]Scaffold: Stochastic controlled averaging for federated learning, ICML 2020
with S. Karimireddy, S. Kale, M. Mohri, S. Reddi, and S. Stich [pdf]Federated learning of N-gram language models, CoNLL 2019
with M. Chen, R. Mathews, A. Wong, C. Allauzen, F. Beaufays, and M. Riley [pdf]AdaCliP: Adaptive clipping for private SGD, TPDP workshop 2020
with V. Pichapati, F. Yu, S Reddi, and S. Kumar [pdf]Convergence of Chao unseen species estimator, ISIT 2019
with N. Rajaraman, P. Chandra, and A. Thangaraj [pdf]Approximating probabilistic models as weighted finite automata, Manuscript
with B. Roark, M. Riley, and V. Schogol [pdf]West: Word encoded sequence transducers, ICASSP 2019
with E. Variani and M. Weintraub [pdf]Agnostic federated learning, ICML 2019
with M. Mohri and G. Sivek [pdf]Differentially private anonymized histograms, NeurIPS 2019 [pdf]
Distilling weighted finite automata from arbitrary probabilistic models, FSMLNP 2019
with B. Roark, M. Riley, and V. Schogol [pdf]Sampled softmax with random fourier features, NeurIPS 2019
with A. Rawat, J. Chen, F. Yu, and S. Kumar [pdf]Advances and Open Problems in Federated learning, Manuscript
with P. Kairouz et al [pdf]Maximum selection and sorting with adversarial comparators, JMLR 2018
with J. Acharya, M. Falahatgar, A. Jafarpor, and A. Orlitsky [pdf]Data amplification: A unified and competitive approach to property estimation, NeurIPS 2018
with Y. Hao, A. Orlitsky, and Y. Wu [pdf]cp-sgd: Communication-efficient and differentially-private distributed SGD, NeurIPS 2018
with N. Agarwal, F. Yu, S. Kumar, and B. McMahan [pdf] (Spotlight presentation)Minimax risk for missing mass estimation, ISIT 2017
with N. Rajaraman, P. Chandra, and A. Thangaraj [pdf]Model-powered conditional independence test, NeurIPS 2017
with R. Sen, K. Shanmugam, A. Dimakis, and S. Shakkottai [pdf]Multiscale quantization for fast similarity search, NeurIPS 2017
with X. 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
with S. Kumar, M. Nirschl, D. Holtmann-Rice, H. Liao, and F. Yu [pdf]Distributed mean estimation with limited communication, ICML 2017
with F. Yu, H. B. McMahan, and S. Kumar [pdf]A unified maximum likelihood approach for optimal distribution property estimation, ICML 2017
with J. Acharya, H. Das, and A. Orlitsky [pdf]Maximum selection and ranking under noisy comparisions, ICML 2017
with M. Falahatgar, A. Orlitsky, and V. Pichapati [pdf]Sample complexity of population recovery, COLT 2017
with Y. Polyanskiy and Y. Wu [pdf]Orthogonal random features, NeurIPS 2016
with F. Yu, K. Choromanski, D. Holtmann-Rice, and S. Kumar [pdf] (Oral presentation)Federated learning: Strategies for improving communication efficiency, NeurIPS PMPML workshop 2016
with J. Konecny, H. B. McMahan, F. X. Yu, P. Richtarik, and D. Bacon [pdf]Optimal prediction of the number of unseen species, PNAS 2016
with A. Orlitsky and Y. Wu [pdf]Learning Markov distributions: Does estimation trump compression?, ISIT 2016
with M. Falahatgar, A. Orlitsky, and V. Pichapati [pdf]Estimating the number of defectives with group testing, ISIT 2016
with M. Falahatgar, A. Jafarpour, A. Orlitsky, and V. Pichapati [pdf]Competitive distribution estimation: Why is Good-Turing good, NeurIPS 2015
with A. Orlitsky [pdf] [talk] (Best paper award)Faster algorithms for testing under conditional sampling, COLT 2015
with M. Falahatgar, A. Jafarpour, A. Orlitsky, and V. Pichapati [jmlr]On learning distributions from their samples, COLT 2015
with S. Kamath, A. Orlitsky, and V. Pichapati [jmlr]Automata and graph compression, ISIT 2015
with M. Mohri and M. Riley [pdf] [implementation]Universal compression of power-law distributions, ISIT 2015
with M. Falahatgar, A. Jafarpour, A. Orlitsky, and V. Pichapati [pdf]Sparse solutions to nonnegative linear systems and applications, AISTATS 2015
with A. Bhaskara and M. Zaghimoghaddam [arXiv]The complexity of estimating Renyi entropy, SODA 2015
with J. Acharya, A. Orlitsky and H. Tyagi [arXiv]Near-optimal-sample estimators for spherical Gaussian mixtures, NeurIPS 2014
with J. Acharya, A. Jafarpour, and A. Orlitsky [arXiv] [talk at simons]Sorting with adversarial comparators and application to density estimation, ISIT 2014
with J. Acharya, A. Jafarpour, and A. Orlitsky [pdf]Efficient compression of monotone and m-modal distributions, ISIT 2014
with J. Acharya, A. Jafarpour, and A. Orlitsky [pdf]Poissonization and universal compression of envelope classes, ISIT 2014
with J. Acharya, A. Jafarpour, and A. Orlitsky [pdf]Sublinear algorithms for outlier detection and generalized closeness testing, ISIT 2014
with J. Acharya, A. Jafarpour, and A. Orlitsky [pdf]Optimal probability estimation with applications to prediction and classification, COLT 2013
with J. Acharya, A. Jafarpour, and A. Orlitsky [pdf] [talk]Tight Bounds for Universal Compression of Large Alphabets, ISIT 2013
with J. Acharya, H. Das, A. Jafarpour, and A. Orlitsky [pdf]A competitive test for uniformity of monotone distributions, AISTATS 2013
with J. 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
with H. Das, A. Jafarpour, A. Orlitsky, and S. Pan [pdf]Strong and weak secrecy in wiretap channels, invited paper at ISTC 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)