Praneeth Netrapalli

4.2k citations

44 papers · 1.1k indexed · 1 hit paper · h-index 15

Impact in

Computational Mathematics top 2%
- Tensor decomposition and applications
Computational Mechanics top 2%
- Sparse and Compressive Sensing Techniques

Papers in ⓘ

Artificial Intelligence 35
- Stochastic Gradient Optimization Techniques 17
- Machine Learning and Algorithms 7
- Natural Language Processing Techniques 4
- Neural Networks and Applications 4
- Machine Learning and ELM 3
Computational Mechanics 16
- Sparse and Compressive Sensing Techniques 16

Co-authors: Sujay Sanghavi (8 shared papers)Prateek Jain (8 shared papers)Chi Jin (9 shared papers)Michael I. Jordan (5 shared papers)Sham M. Kakade (11 shared papers)Animashree Anandkumar (5 shared papers)Rong Ge (2 shared papers)Alekh Agarwal (3 shared papers)
Journals: ACM SIGMETRICS Performance Evaluation Review (1 paper)Journal of the Indian Institute of Science (1 paper)Journal of the ACM (1 paper)IEEE Transactions on Information Theory (1 paper)Conference on Learning Theory (5 papers)
Partner nations: United States India United Kingdom

In The Last Decade

Praneeth Netrapalli

40 papers receiving 1.1k citations

Hit Papers

align trajectories log scale

Low-rank matrix completion using alternating minimization 2013 · 454 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2013 Low-rank matrix completion using alternating minimization

Peers

Countries citing papers authored by Praneeth Netrapalli

Since Specialization

Citations

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

Fields of papers citing papers by Praneeth Netrapalli

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Praneeth Netrapalli, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Praneeth Netrapalli Line = papers co-authored together Praneeth Netrapalli links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

Showing the 20 most-cited of 44 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	Low-rank matrix completion using alternating minimization Prateek Jain, Praneeth Netrapalli, Sujay Sanghavi Hit paper breakdown →	2013	454
2	Learning the graph of epidemic cascades Praneeth Netrapalli, Sujay Sanghavi	2012	90
3	Stochastic Gradient Descent and Its Variants in Machine Learning Journal of the Indian Institute of Science ·Praneeth Netrapalli	2019	71
4	Non-convex Robust PCA CaltechAUTHORS (California Institute of Technology) ·Praneeth Netrapalli, U. N. Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain	2014	64
5	How to escape saddle points efficiently International Conference on Machine Learning ·Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan	2017	57
6	Learning the graph of epidemic cascades ACM SIGMETRICS Performance Evaluation Review ·Praneeth Netrapalli, Sujay Sanghavi	2012	48
7	On Nonconvex Optimization for Machine Learning Journal of the ACM ·Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan	2021	36
8	One-Bit Compressed Sensing: Provable Support and Vector Recovery International Conference on Machine Learning ·Sivakant Gopi, Praneeth Netrapalli, Prateek Jain, Aditya Nori	2013	29
9	Learning Sparsely Used Overcomplete Dictionaries Conference on Learning Theory ·Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon	2014	28
10	What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? International Conference on Machine Learning ·Chi Jin, Praneeth Netrapalli, Michael I. Jordan	2020	25
11	Greedy learning of Markov network structure Praneeth Netrapalli, Siddhartha Banerjee, Sujay Sanghavi, Sanjay Shakkottai	2010	25
12	Exact Recovery of Sparsely Used Overcomplete Dictionaries. arXiv (Cornell University) ·Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli	2013	19
13	Stochastic Gradient Descent Escapes Saddle Points Efficiently. arXiv (Cornell University) ·Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan	2019	19
14	Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent Neural Information Processing Systems ·Chi Jin, Sham M. Kakade, Praneeth Netrapalli	2016	15
15	P-SIF: Document Embeddings Using Partition Averaging Proceedings of the AAAI Conference on Artificial Intelligence ·Vivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli, Piyush Rai, Partha Talukdar	2020	15
16	A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries IEEE Transactions on Information Theory ·Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli	2016	13
17	Provable non-convex robust PCA CaltechAUTHORS (California Institute of Technology) ·Praneeth Netrapalli, U. N. Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain	2014	12
18	Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal. arXiv (Cornell University) ·Chi Jin, Praneeth Netrapalli, Michael I. Jordan	2019	12
19	Thresholding Based Outlier Robust PCA. Conference on Learning Theory ·Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli	2017	10
20	Accelerating Stochastic Gradient Descent arXiv (Cornell University) ·Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford	2017	10

About Praneeth Netrapalli

Praneeth Netrapalli is a scholar working on Artificial Intelligence, Computational Mechanics, Statistics and Probability, Numerical Analysis and Signal Processing, having authored 44 papers that have together received 1.1k indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (17 papers), Sparse and Compressive Sensing Techniques (16 papers), Machine Learning and Algorithms (7 papers), Blind Source Separation Techniques (5 papers), Natural Language Processing Techniques (4 papers), Neural Networks and Applications (4 papers), Markov Chains and Monte Carlo Methods (4 papers) and Machine Learning and ELM (3 papers). The work is most often cited by research in Computational Mathematics (65 citations), Computational Mechanics (516 citations), Signal Processing (206 citations), Acoustics and Ultrasonics (13 citations) and Computer Vision and Pattern Recognition (262 citations). Praneeth Netrapalli has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Sujay Sanghavi, Prateek Jain, Chi Jin, Michael I. Jordan, Sham M. Kakade, Animashree Anandkumar, Rong Ge, Alekh Agarwal, Prateek Jain and Aditya Nori. Their work appears in journals such as ACM SIGMETRICS Performance Evaluation Review, Journal of the Indian Institute of Science, Journal of the ACM, IEEE Transactions on Information Theory and Conference on Learning Theory.

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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