Sai Praneeth Karimireddy

2.2k citations
19 papers · 301 indexed · h-index 8
Topics
Stochastic Gradient Optimization Techniques (10 papers)Privacy-Preserving Technologies in Data (6 papers)Sparse and Compressive Sensing Techniques (4 papers)
Journals
Journal of Machine Learning Researchnpj Digital MedicineInfoscience (Ecole Polytechnique Fédérale de Lausanne)

In The Last Decade

Sai Praneeth Karimireddy

16 papers receiving 293 citations

Peers

Sai Praneeth Karimireddy
Comparison fields: 5 of 43
  • Artificial Intelligence 273
  • Computer Networks and Communications 66
  • Computer Vision and Pattern Recognition 49
  • Electrical and Electronic Engineering 38
  • Computer Science Applications 30
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Citations per field
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Citations per year

Countries citing papers authored by Sai Praneeth Karimireddy

Since Specialization
Citations

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

Fields of papers citing papers by Sai Praneeth Karimireddy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sai Praneeth Karimireddy. 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 Sai Praneeth Karimireddy. The network helps show where Sai Praneeth Karimireddy may publish in the future.

Co-authorship network of co-authors of Sai Praneeth Karimireddy

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

All Works

19 of 19 papers shown
#WorkIndexed citations
1 0
2 0
3 0
4 3
5 2
6
Breaking the centralized barrier for cross-device federated learning
25
7
Learning from History for Byzantine Robust Optimization
26
8
Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data
13
9
The Error-Feedback framework: SGD with Delayed Gradients
3
10
Why are Adaptive Methods Good for Attention Models
2
11
Practical Low-Rank Communication Compression in Decentralized Deep Learning
14
12
Accelerating Gradient Boosting Machine
4
13
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization
15
14
Why ADAM Beats SGD for Attention Models
23
15 54
16
SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning.
114
17
Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems
1
18 1
19 1

About Sai Praneeth Karimireddy

Sai Praneeth Karimireddy is a scholar working on Health Informatics, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 19 papers that have together received 301 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (10 papers), Privacy-Preserving Technologies in Data (6 papers) and Sparse and Compressive Sensing Techniques (4 papers). The work is most often cited by research in Artificial Intelligence (273 citations), Computer Science Applications (30 citations) and Computer Networks and Communications (66 citations). Sai Praneeth Karimireddy has collaborated with scholars based in Switzerland, United States and Norway. Frequent co-authors include Sebastian U. Stich, Martin Jaggi, Sashank J. Reddi, Ananda Theertha Suresh, Mehryar Mohri, Satyen Kale, Thijs Vogels, Suvrit Sra, Andreas Veit and Sanjiv Kumar. Their work appears in journals such as Journal of Machine Learning Research, npj Digital Medicine and Infoscience (Ecole Polytechnique Fédérale de Lausanne).

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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