Ananda Theertha Suresh

Ananda Theertha Suresh

Google Research, New York
email: 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 interested in theoretical and algorithmic aspects of machine learning, information theory, and statistics.

Publications

  1. Wyner-Ziv Estimators: Efficient Distributed Mean Estimation with Side Information, Manuscript
    with P. Mayekar and H. Tyagi [pdf]

  2. FedBoost: A Communication-Efficient Algorithm for Federated Learning, ICML 2020
    with J. Hamer and M. Mohri [pdf]

  3. Robust hypothesis testing and distribution estimation in Hellinger distance, Manuscript [pdf]

  4. Multiple-Source Adaptation with Domain Classifiers, Manuscript
    with C. Cortes, M. Mohri, and N. Zhang [pdf]

  5. Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs, Manuscript
    with A. Girgis, D. Data, S Diggavi, and P. Kairouz [pdf]

  6. Mime: Mimicking centralized stochastic algorithms in federated learning, Manuscript
    with S. Karimireddy, M. Jaggi, S. Kale, M. Mohri, S. Reddi, and S. Stich [pdf]

  7. A Theory of Multiple-Source Adaptation with Limited Target Labeled Data, Manuscript
    with Y. Mansour, M. Mohri, J. Ro, and K. Wu [pdf]

  8. Learning discrete distributions: user vs item-level privacy, NeurIPS 2020
    with Y. Liu, F. Yu, S. Kumar, and M. Riley [pdf]

  9. Relative deviation margin bounds, Manuscript
    with C. Cortes and M. Mohri [pdf]

  10. Three approaches for personalization with applications to federated learning, Manuscript
    with Y. Mansour, M. Mohri, and J. Ro [pdf]

  11. Optimal multiclass overfitting by sequence reconstruction from hamming queries, ALT 2020
    with J. Acharya [pdf] (Best paper award)

  12. 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]

  13. Scaffold: Stochastic controlled averaging for federated learning, ICML 2020
    with S. Karimireddy, S. Kale, M. Mohri, S. Reddi, and S. Stich [pdf]

  14. Federated learning of N-gram language models, CoNLL 2019
    with M. Chen, R. Mathews, A. Wong, C. Allauzen, F. Beaufays, and M. Riley [pdf]

  15. AdaCliP: Adaptive clipping for private SGD, TPDP workshop 2020
    with V. Pichapati, F. Yu, S Reddi, and S. Kumar [pdf]

  16. Convergence of Chao unseen species estimator, ISIT 2019
    with N. Rajaraman, P. Chandra, and A. Thangaraj [pdf]

  17. Approximating probabilistic models as weighted finite automata, Manuscript
    with B. Roark, M. Riley, and V. Schogol [pdf]

  18. West: Word encoded sequence transducers, ICASSP 2019
    with E. Variani and M. Weintraub [pdf]

  19. Agnostic federated learning, ICML 2019
    with M. Mohri and G. Sivek [pdf]

  20. Differentially private anonymized histograms, NeurIPS 2019 [pdf]

  21. Distilling weighted finite automata from arbitrary probabilistic models, FSMLNP 2019
    with B. Roark, M. Riley, and V. Schogol [pdf]

  22. Sampled softmax with random fourier features, NeurIPS 2019
    with A. Rawat, J. Chen, F. Yu, and S. Kumar [pdf]

  23. Advances and Open Problems in Federated learning, Manuscript
    with P. Kairouz et al [pdf]

  24. Maximum selection and sorting with adversarial comparators, JMLR 2018
    with J. Acharya, M. Falahatgar, A. Jafarpor, and A. Orlitsky [pdf]

  25. Data amplification: A unified and competitive approach to property estimation, NeurIPS 2018
    with Y. Hao, A. Orlitsky, and Y. Wu [pdf]

  26. cp-sgd: Communication-efficient and differentially-private distributed SGD, NeurIPS 2018
    with N. Agarwal, F. Yu, S. Kumar, and B. McMahan [pdf] (Spotlight presentation)

  27. Minimax risk for missing mass estimation, ISIT 2017
    with N. Rajaraman, P. Chandra, and A. Thangaraj [pdf]

  28. Model-powered conditional independence test, NeurIPS 2017
    with R. Sen, K. Shanmugam, A. Dimakis, and S. Shakkottai [pdf]

  29. Multiscale quantization for fast similarity search, NeurIPS 2017
    with X. Wu, R. Guo, D. Holtmann-Rice, D. Simcha, F. Yu, and S. Kumar [pdf]

  30. 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]

  31. Distributed mean estimation with limited communication, ICML 2017
    with F. Yu, H. B. McMahan, and S. Kumar [pdf]

  32. A unified maximum likelihood approach for optimal distribution property estimation, ICML 2017
    with J. Acharya, H. Das, and A. Orlitsky [pdf]

  33. Maximum selection and ranking under noisy comparisions, ICML 2017
    with M. Falahatgar, A. Orlitsky, and V. Pichapati [pdf]

  34. Sample complexity of population recovery, COLT 2017
    with Y. Polyanskiy and Y. Wu [pdf]

  35. Orthogonal random features, NeurIPS 2016
    with F. Yu, K. Choromanski, D. Holtmann-Rice, and S. Kumar [pdf] (Oral presentation)

  36. 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]

  37. Optimal prediction of the number of unseen species, PNAS 2016
    with A. Orlitsky and Y. Wu [pdf]

  38. Learning Markov distributions: Does estimation trump compression?, ISIT 2016
    with M. Falahatgar, A. Orlitsky, and V. Pichapati [pdf]

  39. Estimating the number of defectives with group testing, ISIT 2016
    with M. Falahatgar, A. Jafarpour, A. Orlitsky, and V. Pichapati [pdf]

  40. Competitive distribution estimation: Why is Good-Turing good, NeurIPS 2015
    with A. Orlitsky [pdf] [talk] (Best paper award)

  41. Faster algorithms for testing under conditional sampling, COLT 2015
    with M. Falahatgar, A. Jafarpour, A. Orlitsky, and V. Pichapati [jmlr]

  42. On learning distributions from their samples, COLT 2015
    with S. Kamath, A. Orlitsky, and V. Pichapati [jmlr]

  43. Automata and graph compression, ISIT 2015
    with M. Mohri and M. Riley [pdf] [implementation]

  44. Universal compression of power-law distributions, ISIT 2015
    with M. Falahatgar, A. Jafarpour, A. Orlitsky, and V. Pichapati [pdf]

  45. Sparse solutions to nonnegative linear systems and applications, AISTATS 2015
    with A. Bhaskara and M. Zaghimoghaddam [arXiv]

  46. The complexity of estimating Renyi entropy, SODA 2015
    with J. Acharya, A. Orlitsky and H. Tyagi [arXiv]

  47. Near-optimal-sample estimators for spherical Gaussian mixtures, NeurIPS 2014
    with J. Acharya, A. Jafarpour, and A. Orlitsky [arXiv] [talk at simons]

  48. Sorting with adversarial comparators and application to density estimation, ISIT 2014
    with J. Acharya, A. Jafarpour, and A. Orlitsky [pdf]

  49. Efficient compression of monotone and m-modal distributions, ISIT 2014
    with J. Acharya, A. Jafarpour, and A. Orlitsky [pdf]

  50. Poissonization and universal compression of envelope classes, ISIT 2014
    with J. Acharya, A. Jafarpour, and A. Orlitsky [pdf]

  51. Sublinear algorithms for outlier detection and generalized closeness testing, ISIT 2014
    with J. Acharya, A. Jafarpour, and A. Orlitsky [pdf]

  52. Optimal probability estimation with applications to prediction and classification, COLT 2013
    with J. Acharya, A. Jafarpour, and A. Orlitsky [pdf] [talk]

  53. Tight Bounds for Universal Compression of Large Alphabets, ISIT 2013
    with J. Acharya, H. Das, A. Jafarpour, and A. Orlitsky [pdf]

  54. A competitive test for uniformity of monotone distributions, AISTATS 2013
    with J. Acharya, A. Jafarpour, and A. Orlitsky [pdf]

  55. Competitive classification and closeness testing, COLT 2012
    with J. Acharya, H. Das, A. Jafarpour, A. Orlitsky, and S. Pan [pdf] [talk]

  56. On the query computation and verification of functions, ISIT 2012
    with H. Das, A. Jafarpour, A. Orlitsky, and S. Pan [pdf]

  57. 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]

  58. Strong secrecy for erasure wiretap channels, ITW 2010
    A. T. Suresh, A. Subramanian, A. Thangaraj, M. Bloch, and S. W. McLaughlin [pdf]

  59. 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)