Sashank J. Reddi

4.7k total citations
30 papers, 592 citations indexed

About

Sashank J. Reddi is a scholar working on Artificial Intelligence, Computational Mechanics and Statistics and Probability. According to data from OpenAlex, Sashank J. Reddi has authored 30 papers receiving a total of 592 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Artificial Intelligence, 8 papers in Computational Mechanics and 7 papers in Statistics and Probability. Recurrent topics in Sashank J. Reddi's work include Stochastic Gradient Optimization Techniques (13 papers), Sparse and Compressive Sensing Techniques (8 papers) and Machine Learning and Algorithms (5 papers). Sashank J. Reddi is often cited by papers focused on Stochastic Gradient Optimization Techniques (13 papers), Sparse and Compressive Sensing Techniques (8 papers) and Machine Learning and Algorithms (5 papers). Sashank J. Reddi collaborates with scholars based in United States, Switzerland and India. Sashank J. Reddi's co-authors include Satyen Kale, Barnabás Póczos, Suvrit Sra, Sanjiv Kumar, Sai Praneeth Karimireddy, Ananda Theertha Suresh, Mehryar Mohri, Sebastian U. Stich, Manzil Zaheer and Devendra Singh Sachan and has published in prestigious journals such as HAL (Le Centre pour la Communication Scientifique Directe), arXiv (Cornell University) and PubMed.

In The Last Decade

Sashank J. Reddi

28 papers receiving 566 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Sashank J. Reddi	484	106	102	64	50	30	592
Chia-Hua Ho	315 0.7×	180 1.7×	85 0.8×	38 0.6×	21 0.4×	8	572
Nati Srebro	299 0.6×	86 0.8×	135 1.3×	37 0.6×	85 1.7×	16	414
Samantha Hansen	244 0.5×	54 0.5×	111 1.1×	23 0.4×	23 0.5×	6	392
Qi Lei	266 0.5×	96 0.9×	70 0.7×	71 1.1×	124 2.5×	35	656
Changyou Chen	425 0.9×	194 1.8×	96 0.9×	78 1.2×	9 0.2×	47	705
Zirui Zhou	523 1.1×	63 0.6×	159 1.6×	16 0.3×	105 2.1×	26	775
Niao He	198 0.4×	48 0.5×	106 1.0×	22 0.3×	40 0.8×	30	383
Virginia Smith	511 1.1×	85 0.8×	59 0.6×	18 0.3×	179 3.6×	21	652
Shalev-ShwartzShai	573 1.2×	222 2.1×	69 0.7×	12 0.2×	74 1.5×	10	844
Simon S. Du	340 0.7×	109 1.0×	90 0.9×	21 0.3×	29 0.6×	40	507

Countries citing papers authored by Sashank J. Reddi

Since Specialization

Citations

This map shows the geographic impact of Sashank J. Reddi'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 Sashank J. Reddi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sashank J. Reddi more than expected).

Fields of papers citing papers by Sashank J. Reddi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sashank J. Reddi. 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 Sashank J. Reddi. The network helps show where Sashank J. Reddi may publish in the future.

Co-authorship network of co-authors of Sashank J. Reddi

This figure shows the co-authorship network connecting the top 25 collaborators of Sashank J. Reddi. A scholar is included among the top collaborators of Sashank J. Reddi 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 Sashank J. Reddi. Sashank J. Reddi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Menon, Aditya Krishna, Ankit Singh Rawat, Sashank J. Reddi, Seung‐Yeon Kim, & Sanjiv Kumar. (2021). A statistical perspective on distillation. International Conference on Machine Learning. 7632–7642. 5 indexed citations

Reddi, Sashank J., et al.. (2021). Efficient Training of Retrieval Models using Negative Cache. Neural Information Processing Systems. 34. 2 indexed citations

Reddi, Sashank J., Rama Kumar Pasumarthi, Aditya Krishna Menon, et al.. (2021). RankDistil: Knowledge Distillation for Ranking. International Conference on Artificial Intelligence and Statistics. 2368–2376. 4 indexed citations

Karimireddy, Sai Praneeth, Martin Jaggi, Satyen Kale, et al.. (2021). Breaking the centralized barrier for cross-device federated learning. Neural Information Processing Systems. 34. 25 indexed citations

Menon, Aditya Krishna, Ankit Singh Rawat, Sashank J. Reddi, & Sanjiv Kumar. (2020). Can gradient clipping mitigate label noise. International Conference on Learning Representations. 28 indexed citations

Zhang, Jingzhao, Sai Praneeth Karimireddy, Andreas Veit, et al.. (2020). Why are Adaptive Methods Good for Attention Models. Neural Information Processing Systems. 33. 15383–15393. 2 indexed citations

Ruan, Yangjun, Yuanhao Xiong, Sashank J. Reddi, Sanjiv Kumar, & Cho‐Jui Hsieh. (2020). Learning to Learn by Zeroth-Order Oracle. arXiv (Cornell University). 1 indexed citations

Menon, Aditya Krishna, Ankit Singh Rawat, Sashank J. Reddi, & Sanjiv Kumar. (2019). Multilabel reductions: what is my loss optimising?. Neural Information Processing Systems. 32. 10600–10611. 9 indexed citations

Guo, Chuan, Xiang Wu, Daniel Holtmann-Rice, et al.. (2019). Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces. Neural Information Processing Systems. 32. 4943–4953. 15 indexed citations

10.

Zhang, Jingzhao, Sai Praneeth Karimireddy, Andreas Veit, et al.. (2019). Why ADAM Beats SGD for Attention Models. 23 indexed citations

11.

Zaheer, Manzil, Sashank J. Reddi, Devendra Singh Sachan, Satyen Kale, & Sanjiv Kumar. (2018). Adaptive Methods for Nonconvex Optimization. Neural Information Processing Systems. 31. 9793–9803. 102 indexed citations

12.

Reddi, Sashank J. & Barnabás Póczos. (2018). Scale Invariant Conditional Dependence Measures. Figshare. 28. 1355–1363. 3 indexed citations

13.

Reddi, Sashank J. & Barnabás Póczos. (2018). k-NN Regression on Functional Data with Incomplete Observations. Figshare. 692–701.

14.

Reddi, Sashank J., Suvrit Sra, Barnabás Póczos, & Alexander J. Smola. (2016). Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization. Neural Information Processing Systems. 29. 1145–1153. 43 indexed citations

15.

Zhang, Hongyi, Sashank J. Reddi, & Suvrit Sra. (2016). Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds. arXiv (Cornell University). 29. 4599–4607. 29 indexed citations

16.

Reddi, Sashank J., Ahmed Hefny, Suvrit Sra, Barnabás Póczos, & Alex Smola. (2015). On variance reduction in stochastic gradient descent and its asynchronous variants. Neural Information Processing Systems. 28. 2647–2655. 22 indexed citations

17.

Reddi, Sashank J., Aaditya Ramdas, Barnabás Póczos, Aarti Singh, & Larry Wasserman. (2015). On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives. International Conference on Artificial Intelligence and Statistics. 772–780. 6 indexed citations

18.

Reddi, Sashank J., Barnabás Póczos, & Alex Smola. (2015). Communication efficient coresets for empirical loss minimization. Uncertainty in Artificial Intelligence. 752–761. 9 indexed citations

19.

Reddi, Sashank J., Aaditya Ramdas, Barnabás Póczos, Aarti Singh, & Larry Wasserman. (2014). Kernel MMD, the Median Heuristic and Distance Correlation in High Dimensions.. arXiv (Cornell University). 4 indexed citations

20.

Reddi, Sashank J., Sunita Sarawagi, & Sundar Vishwanathan. (2010). MAP estimation in Binary MRFs via Bipartite Multi-cuts. Neural Information Processing Systems. 23. 955–963.

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.

Explore authors with similar magnitude of impact