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.
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.
On the benefits of maximum likelihood estimation for Regression and Forecasting, ICLR 2022
with
P. Awasthi, A. Das, R. Sen
[pdf]
Robust Estimation for Random Graphs, Manuscript
with
J. Acharya, A. Jain, G. Kamath, and H. Zhang
[pdf]
Learning with User-Level Privacy, NeurIPS 2021
with
D. 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
with
A. Sekhari, J. Acharya, and G. Kamath
[pdf]
Breaking the centralized barrier for cross-device federated learning, NeurIPS 2021
with
S. Karimireddy, M. Jaggi, S. Kale, M. Mohri, S. Reddi, and S. Stich
[pdf]
Boosting with Multiple Sources, NeurIPS 2021
with
C. Cortes, M. Mohri, and D. Storcheus
[pdf]
FedJAX: Federated learning simulation with JAX, NeurIPS FL workshop 2021
with
J. H. Ro and K. Wu
[pdf]
On the Renyi Differential Privacy of the Shuffle Model, CCS 2021
with
A. Girgis, D. Data, S Diggavi, and P. Kairouz
[pdf]
(Best paper award)
Communication-Efficient Agnostic Federated Averaging, Interspeech 2021
with
J. Ro, M. Chen, R. Mathews, and M. Mohri
[pdf]
A discriminative technique for multiple-source adaptation, ICML 2021
with
C. 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
with
A. Girgis, D. Data, S Diggavi, and P. Kairouz
[longer version]
Shuffled Model of Differential Privacy in Federated Learning, AISTATS 2021
with
A. Girgis, D. Data, S Diggavi, and P. Kairouz
[pdf]
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data, AISTATS 2021
with
Y. 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
with
J. Wang et al
[pdf]
Learning discrete distributions: user vs item-level privacy, NeurIPS 2020
with
Y. Liu, F. Yu, S. Kumar, and M. Riley
[pdf]
Optimal multiclass overfitting by sequence reconstruction from hamming queries, ALT 2020
with
J. Acharya
[pdf]
(Best paper award)
Scaffold: Stochastic controlled averaging for federated learning, ICML 2020
with
S. Karimireddy, S. Kale, M. Mohri, S. Reddi, and S. Stich
[pdf]
FedBoost: A Communication-Efficient Algorithm for Federated Learning, ICML 2020
with
J. Hamer and M. Mohri
[pdf]
Three approaches for personalization with applications to federated learning, Manuscript
with
Y. Mansour, M. Mohri, and J. Ro
[pdf]
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]
AdaCliP: Adaptive clipping for private SGD, TPDP workshop 2020
with
V. Pichapati, F. Yu, S Reddi, and S. Kumar
[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]
Convergence of Chao unseen species estimator, ISIT 2019
with
N. Rajaraman, P. Chandra, and A. Thangaraj
[pdf]
Approximating probabilistic models as weighted finite automata, Computational Linguistics Journal
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]
(Best paper award honorable mention)
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)