Jayadev Acharya

1.7k total citations
52 papers, 554 citations indexed

About

Jayadev Acharya is a scholar working on Artificial Intelligence, Statistics and Probability and Computational Theory and Mathematics. According to data from OpenAlex, Jayadev Acharya has authored 52 papers receiving a total of 554 indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Artificial Intelligence, 11 papers in Statistics and Probability and 9 papers in Computational Theory and Mathematics. Recurrent topics in Jayadev Acharya's work include Machine Learning and Algorithms (20 papers), Algorithms and Data Compression (15 papers) and Privacy-Preserving Technologies in Data (12 papers). Jayadev Acharya is often cited by papers focused on Machine Learning and Algorithms (20 papers), Algorithms and Data Compression (15 papers) and Privacy-Preserving Technologies in Data (12 papers). Jayadev Acharya collaborates with scholars based in United States, India and Australia. Jayadev Acharya's co-authors include Shaveta Arora, Prasanta K. Panigrahi, Aditi Verma, Alon Orlitsky, Shengjun Pan, Ziteng Sun, Ananda Theertha Suresh, Clément L. Canonne, Huanyu Zhang and Himanshu Tyagi and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Information Theory and Critical Reviews in Food Science and Nutrition.

In The Last Decade

Jayadev Acharya

49 papers receiving 522 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Jayadev Acharya United States	11	292	165	66	62	56	52	554
Silvia Villa Italy	13	164 0.6×	105 0.6×	19 0.3×	58 0.9×	21 0.4×	48	564
Julien Ugon Australia	14	205 0.7×	158 1.0×	20 0.3×	47 0.8×	83 1.5×	59	567
Shizhong Liao China	13	249 0.9×	166 1.0×	25 0.4×	25 0.4×	26 0.5×	68	457
Krikamol Muandet Germany	9	442 1.5×	158 1.0×	18 0.3×	101 1.6×	15 0.3×	21	712
Marco Bressan Italy	13	157 0.5×	263 1.6×	66 1.0×	17 0.3×	31 0.6×	46	536
K. Chidananda Gowda India	8	436 1.5×	224 1.4×	42 0.6×	44 0.7×	32 0.6×	15	656
C. Eric United States	13	279 1.0×	157 1.0×	25 0.4×	125 2.0×	20 0.4×	41	743
Madeleine Udell United States	15	194 0.7×	112 0.7×	10 0.2×	44 0.7×	62 1.1×	41	628
Javier Yáñez Spain	16	180 0.6×	111 0.7×	36 0.5×	50 0.8×	49 0.9×	48	607
Jacek Tabor Poland	17	238 0.8×	153 0.9×	18 0.3×	29 0.5×	11 0.2×	141	909

Countries citing papers authored by Jayadev Acharya

Since Specialization

Citations

This map shows the geographic impact of Jayadev Acharya's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jayadev Acharya with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jayadev Acharya more than expected).

Fields of papers citing papers by Jayadev Acharya

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jayadev Acharya. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jayadev Acharya. The network helps show where Jayadev Acharya may publish in the future.

Co-authorship network of co-authors of Jayadev Acharya

This figure shows the co-authorship network connecting the top 25 collaborators of Jayadev Acharya. A scholar is included among the top collaborators of Jayadev Acharya based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jayadev Acharya. Jayadev Acharya is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Acharya, Jayadev, et al.. (2025). Pauli Measurements Are Not Optimal for Single-Copy Tomography. 718–729.

Acharya, Jayadev, et al.. (2023). Optimal Rates for Nonparametric Density Estimation Under Communication Constraints. IEEE Transactions on Information Theory. 70(3). 1939–1961. 2 indexed citations

Acharya, Jayadev, et al.. (2021). Inference Under Information Constraints III: Local Privacy Constraints. IEEE Journal on Selected Areas in Information Theory. 2(1). 253–267. 9 indexed citations

Acharya, Jayadev, Peter Kairouz, Yuhan Liu, & Ziteng Sun. (2021). Estimating Sparse Discrete Distributions Under Privacy and Communication Constraints. 79–98. 1 indexed citations

Acharya, Jayadev, Clément L. Canonne, Yuhan Liu, Ziteng Sun, & Himanshu Tyagi. (2021). Distributed Estimation with Multiple Samples per User: Sharp Rates and Phase Transition. Neural Information Processing Systems. 34. 2 indexed citations

Wagner, Aaron B., Elaine Hill, Ziteng Sun, et al.. (2020). Social distancing merely stabilized COVID‐19 in the United States. Stat. 9(1). e302–e302. 20 indexed citations

Acharya, Jayadev, Clément L. Canonne, & Himanshu Tyagi. (2020). Distributed Signal Detection under Communication Constraints. Conference on Learning Theory. 41–63. 3 indexed citations

Acharya, Jayadev, Keith Bonawitz, Peter Kairouz, Daniel Ramage, & Ziteng Sun. (2020). Context Aware Local Differential Privacy. International Conference on Machine Learning. 1. 52–62. 1 indexed citations

Acharya, Jayadev, Ziteng Sun, & Huanyu Zhang. (2019). Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication. International Conference on Artificial Intelligence and Statistics. 1120–1129. 13 indexed citations

10.

Acharya, Jayadev, Ziteng Sun, & Huanyu Zhang. (2018). Differentially Private Testing of Identity and Closeness of Discrete Distributions. arXiv (Cornell University). 31. 6878–6891. 6 indexed citations

11.

Acharya, Jayadev, Arnab Bhattacharyya, Constantinos Daskalakis, & Saravanan Kandasamy. (2018). Learning and Testing Causal Models with Interventions. NOT FOUND REPOSITORY (Indian Institute of Science Bangalore). 31. 9447–9460. 3 indexed citations

12.

Acharya, Jayadev, Ziteng Sun, & Huanyu Zhang. (2018). Communication Efficient, Sample Optimal, Linear Time Locally Private Discrete Distribution Estimation.. arXiv (Cornell University). 5 indexed citations

13.

Acharya, Jayadev, et al.. (2018). Maximum Selection and Sorting with Adversarial Comparators. Journal of Machine Learning Research. 19(59). 1–31. 3 indexed citations

14.

Acharya, Jayadev, Clément L. Canonne, & Gautam Kamath. (2018). . Theory of Computing. 14(1). 1–46. 1 indexed citations

15.

Acharya, Jayadev, et al.. (2017). A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions. International Conference on Machine Learning. 11–21. 11 indexed citations

16.

Acharya, Jayadev, Ilias Diakonikolas, Jerry Li, & Ludwig Schmidt. (2016). Fast algorithms for segmented regression. Edinburgh Research Explorer (University of Edinburgh). 2878–2886. 4 indexed citations

17.

Acharya, Jayadev, et al.. (2013). Optimal Probability Estimation with Applications to Prediction and Classification. Conference on Learning Theory. 764–796. 9 indexed citations

18.

Acharya, Jayadev, et al.. (2013). A Competitive Test for Uniformity of Monotone Distributions. International Conference on Artificial Intelligence and Statistics. 57–65. 7 indexed citations

19.

Acharya, Jayadev, et al.. (2012). Tight Bounds on Profile Redundancy and Distinguishability. neural information processing systems. 25. 3257–3265. 6 indexed citations

20.

Acharya, Jayadev, et al.. (2011). Competitive Closeness Testing. Conference on Learning Theory. 47–68. 24 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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