Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Low-rank matrix completion using alternating minimization
2013454 citationsPrateek Jain, Praneeth Netrapalli et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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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
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-authorship network of co-authors of Praneeth Netrapalli
This figure shows the co-authorship network connecting the top 25 collaborators of Praneeth Netrapalli.
A scholar is included among the top collaborators of Praneeth Netrapalli 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 Praneeth Netrapalli. Praneeth Netrapalli is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Jain, Prateek, et al.. (2021). Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems. Neural Information Processing Systems. 34.1 indexed citations
2.
Raghunathan, Aditi, et al.. (2020). The Pitfalls of Simplicity Bias in Neural Networks. Neural Information Processing Systems. 33. 9573–9585.2 indexed citations
Jin, Chi, Praneeth Netrapalli, & Michael I. Jordan. (2020). What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?. International Conference on Machine Learning. 1. 4880–4889.25 indexed citations
6.
Wu, Xian, et al.. (2020). Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms. Neural Information Processing Systems. 33. 16666–16676.1 indexed citations
7.
Jin, Chi, Praneeth Netrapalli, & Michael I. Jordan. (2019). Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal.. arXiv (Cornell University).12 indexed citations
Ge, Rong, Sham M. Kakade, Rahul Kidambi, & Praneeth Netrapalli. (2019). The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure.. arXiv (Cornell University).6 indexed citations
10.
Jin, Chi, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, & Michael I. Jordan. (2019). Stochastic Gradient Descent Escapes Saddle Points Efficiently.. arXiv (Cornell University).19 indexed citations
11.
Jain, Prateek, et al.. (2019). SGD without Replacement: Sharper Rates for General Smooth Convex Functions. International Conference on Machine Learning. 4703–4711.4 indexed citations
12.
Gupta, Chirag, et al.. (2018). Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds. Neural Information Processing Systems. 31. 10814–10824.2 indexed citations
Jin, Chi, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, & Michael I. Jordan. (2017). How to escape saddle points efficiently. International Conference on Machine Learning. 1724–1732.57 indexed citations
15.
Jain, Prateek, et al.. (2017). Thresholding Based Outlier Robust PCA.. Conference on Learning Theory. 593–628.10 indexed citations
16.
Jin, Chi, Sham M. Kakade, & Praneeth Netrapalli. (2016). Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent. Neural Information Processing Systems. 29. 4520–4528.15 indexed citations
17.
Jain, Prateek, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, & Aaron Sidford. (2016). Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm.. arXiv (Cornell University).2 indexed citations
Netrapalli, Praneeth, et al.. (2013). One-Bit Compressed Sensing: Provable Support and Vector Recovery. International Conference on Machine Learning. 154–162.29 indexed citations
20.
Agarwal, Alekh, Animashree Anandkumar, & Praneeth Netrapalli. (2013). Exact Recovery of Sparsely Used Overcomplete Dictionaries.. arXiv (Cornell University).19 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.