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  2021 (6)
On Learning Parametric Distributions from Quantized Samples. Septimia Sarbu; and Abdellatif Zaidi. In Proceedings of the 2021 IEEE International Symposium on Information Theory (ISIT'21), June 2021.
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Inference Under Information Constraints III: Local Privacy Constraints. Jayadev Acharya; Clément L. Canonne; Cody Freitag; Ziteng Sun; and Himanshu Tyagi. IEEE J. Sel. Areas Inf. Theory, 2(1): 253–267. 2021.
link   bibtex   54 downloads  
Unified lower bounds for interactive high-dimensional estimation under information constraints. Jayadev Acharya; Clément L. Canonne; Zuteng Sun; and Himanshu Tyagi. CoRR, abs/2010.06562. 2021.
link   bibtex   144 downloads  
Information-constrained optimization: can adaptive processing of gradients help?. Jayadev Acharya; Clément L. Canonne; Prathamesh Mayekar; and Himanshu Tyagi. CoRR, abs/2104.00979. 2021.
link   bibtex   11 downloads  
Optimal Rates for Nonparametric Density Estimation under Communication Constraints. Jayadev Acharya; Clément L. Canonne; Aditya Vikram Singh; and Himanshu Tyagi. CoRR, abs/2107.10078. 2021.
link   bibtex   105 downloads  
Local Differential Privacy Is Equivalent to Contraction of $E_γ$-Divergence. Shahab Asoodeh; Maryam Aliakbarpour; and Flávio P. Calmon. CoRR, abs/2102.01258. 2021.
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  2020 (12)
Interactive Inference under Information Constraints. Jayadev Acharya; Clément L. Canonne; Yuhan Liu; Ziteng Sun; and Himanshu Tyagi. CoRR, abs/2007.10976. 2020.
Interactive Inference under Information Constraints [link]Paper   link   bibtex   23 downloads  
Fisher Information Under Local Differential Privacy. Leighton Pate Barnes; Wei-Ning Chen; and Ayfer Özgür. IEEE J. Sel. Areas Inf. Theory, 1(3): 645–659. 2020.
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Geometric Lower Bounds for Distributed Parameter Estimation under Communication Constraints. Yanjun Han; Ayfer Özgür; and Tsachy Weissman. ArXiv e-prints, abs/1802.08417v3. September 2020.
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Inference under information constraints I: Lower bounds from chi-square contraction. Jayadev Acharya; Clément L. Canonne; and Himanshu Tyagi. IEEE Trans. Inform. Theory, 66(12): 7835–7855. 2020. Preprint available at arXiv:abs/1812.11476.
Inference under information constraints I: Lower bounds from chi-square contraction [link]Paper   doi   link   bibtex   70 downloads  
Inference Under Information Constraints II: Communication Constraints and Shared Randomness. Jayadev Acharya; Clément L. Canonne; and Himanshu Tyagi. IEEE Trans. Inf. Theory, 66(12): 7856–7877. 2020.
link   bibtex   45 downloads  
Domain Compression and its Application to Randomness-Optimal Distributed Goodness-of-Fit. Jayadev Acharya; Clément L. Canonne; Yanjun Han; Ziteng Sun; and Himanshu Tyagi. In COLT, volume 125, of Proceedings of Machine Learning Research, pages 3–40, 2020. PMLR
link   bibtex   62 downloads  
Distributed Signal Detection under Communication Constraints. Jayadev Acharya; Clément L. Canonne; and Himanshu Tyagi. In COLT, volume 125, of Proceedings of Machine Learning Research, pages 41–63, 2020. PMLR
link   bibtex   21 downloads  
Lecture notes on: Information-theoretic methods for high-dimensional statistics. Yihong Wu. 2020.
Lecture notes on: Information-theoretic methods for high-dimensional statistics [pdf]Paper   link   bibtex   3 downloads  
Lower bounds for learning distributions under communication constraints via fisher information. Leighton Pate Barnes; Yanjun Han; and Ayfer Özgür. J. Mach. Learn. Res., 21: Paper No. 236, 30. 2020.
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Private Identity Testing for High-Dimensional Distributions. Clément L. Canonne; Gautam Kamath; Audra McMillan; Jonathan Ullman; and Lydia Zakynthinou. In Advances in Neural Information Processing Systems 33, 2020. Preprint available at arXiv:abs/1905.11947
link   bibtex   103 downloads  
Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms. Thomas Berrett; and Cristina Butucea. In NeurIPS, 2020.
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Local differential privacy: elbow effect in optimal density estimation and adaptation over Besov ellipsoids. Cristina Butucea; Amandine Dubois; Martin Kroll; and Adrien Saumard. Bernoulli, 26(3): 1727–1764. 2020.
Local differential privacy: elbow effect in optimal density estimation and adaptation over Besov ellipsoids [link]Paper   doi   link   bibtex  
  2019 (5)
Locally Private Gaussian Estimation. Matthew Joseph; Janardhan Kulkarni; Jieming Mao; and Steven Z. Wu. In H. Wallach; H. Larochelle; A. Beygelzimer; F. Alché-Buc; E. Fox; and R. Garnett., editor(s), Advances in Neural Information Processing Systems 32, pages 2984–2993. Curran Associates, Inc., 2019.
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Fisher Information for Distributed Estimation under a Blackboard Communication Protocol. Leighton P. Barnes; Yanjun Han; and Ayfer Özgür. In ISIT, pages 2704–2708, 2019. IEEE
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Lower Bounds for Locally Private Estimation via Communication Complexity. John Duchi; and Ryan Rogers. In Alina Beygelzimer; and Daniel Hsu., editor(s), Proceedings of the Thirty-Second Conference on Learning Theory, volume 99, of Proceedings of Machine Learning Research, pages 1161–1191, Phoenix, USA, June 2019. PMLR
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Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication. Jayadev Acharya; Ziteng Sun; and Huanyu Zhang. In Kamalika Chaudhuri; and Masashi Sugiyama., editor(s), Proceedings of Machine Learning Research, volume 89, pages 1120–1129, 16–18 Apr 2019. PMLR
Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication [link]Paper   link   bibtex   1 download  
Communication and Memory Efficient Testing of Discrete Distributions. Ilias Diakonikolas; Themis Gouleakis; Daniel M. Kane; and Sankeerth Rao. In COLT, volume 99, of Proceedings of Machine Learning Research, pages 1070–1106, 2019. PMLR
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  2018 (4)
Geometric Lower Bounds for Distributed Parameter Estimation under Communication Constraints. Yanjun Han; Ayfer Özgür; and Tsachy Weissman. In Proceedings of the 31st Conference on Learning Theory, COLT 2018, volume 75, of Proceedings of Machine Learning Research, pages 3163–3188, 2018. PMLR The arXiv (v3) version from 2020 corrects some issues and includes more results.
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Distributed Statistical Estimation of High-Dimensional and Non-parametric Distributions. Yanjun Han; Pritam Mukherjee; Ayfer Özgür; and Tsachy Weissman. In Proceedings of the 2018 IEEE International Symposium on Information Theory (ISIT'18), pages 506–510, 2018.
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Minimax optimal procedures for locally private estimation. John C. Duchi; Michael I. Jordan; and Martin J. Wainwright. J. Amer. Statist. Assoc., 113(521): 182–201. 2018.
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Optimal schemes for discrete distribution estimation under locally differential privacy. Min Ye; and Alexander Barg. IEEE Trans. Inform. Theory, 64(8): 5662–5676. 2018.
Optimal schemes for discrete distribution estimation under locally differential privacy [link]Paper   doi   link   bibtex  
  2017 (1)
Information-theoretic lower bounds on Bayes risk in decentralized estimation. Aolin Xu; and Maxim Raginsky. IEEE Transactions on Information Theory, 63(3): 1580–1600. 2017.
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  2016 (1)
Communication lower bounds for statistical estimation problems via a distributed data processing inequality. Mark Braverman; Ankit Garg; Tengyu Ma; Huy L. Nguyen; and David P. Woodruff. In Symposium on Theory of Computing Conference, STOC'16, pages 1011–1020, 2016. ACM
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  2014 (2)
On Communication Cost of Distributed Statistical Estimation and Dimensionality. Ankit Garg; Tengyu Ma; and Huy L. Nguyen. In Advances in Neural Information Processing Systems 27, pages 2726–2734, 2014.
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Fundamental limits of online and distributed algorithms for statistical learning and estimation. Ohad Shamir. In Advances in Neural Information Processing Systems 27, pages 163–171, 2014.
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  2013 (1)
Information-theoretic lower bounds for distributed statistical estimation with communication constraints. Yuchen Zhang; John Duchi; Michael I. Jordan; and Martin J. Wainwright. In Advances in Neural Information Processing Systems 26, pages 2328–2336, 2013.
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  2009 (1)
Information-theoretic limits on sparsity recovery in the high-dimensional and noisy setting. Martin J. Wainwright. IEEE Trans. Inform. Theory, 55(12): 5728–5741. 2009.
Information-theoretic limits on sparsity recovery in the high-dimensional and noisy setting [link]Paper   doi   link   bibtex   1 download