Rohit Babbar

962 total citations
20 papers, 487 citations indexed

About

Rohit Babbar is a scholar working on Artificial Intelligence, Information Systems and Molecular Biology. According to data from OpenAlex, Rohit Babbar has authored 20 papers receiving a total of 487 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 4 papers in Information Systems and 3 papers in Molecular Biology. Recurrent topics in Rohit Babbar's work include Text and Document Classification Technologies (13 papers), Machine Learning and Data Classification (6 papers) and Machine Learning and Algorithms (5 papers). Rohit Babbar is often cited by papers focused on Text and Document Classification Technologies (13 papers), Machine Learning and Data Classification (6 papers) and Machine Learning and Algorithms (5 papers). Rohit Babbar collaborates with scholars based in Finland, United States and Germany. Rohit Babbar's co-authors include Bernhard Schölkopf, Carlee Joe‐Wong, Mario Di Francesco, Han Xiao, Nidhi Singh, Marek Wydmuch, Krzysztof Dembczyński, Hubert Preißl, Andreas Fritsche and Martin Heni and has published in prestigious journals such as Machine Learning, Frontiers in Endocrinology and ACM SIGKDD Explorations Newsletter.

In The Last Decade

Rohit Babbar

19 papers receiving 477 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Rohit Babbar Finland	9	343	108	106	102	39	20	487
Jinjing Zhou China	4	284 0.8×	64 0.6×	47 0.4×	161 1.6×	48 1.2×	5	391
Sung Y. Shin United States	10	154 0.4×	152 1.4×	98 0.9×	77 0.8×	48 1.2×	61	376
Prashant Narayankar India	5	140 0.4×	67 0.6×	76 0.7×	142 1.4×	20 0.5×	7	279
Maxim Deryabin Russia	10	292 0.9×	93 0.9×	167 1.6×	101 1.0×	41 1.1×	31	406
Adel R. Alharbi Saudi Arabia	11	144 0.4×	78 0.7×	105 1.0×	145 1.4×	39 1.0×	49	338
M. A. Rizvi India	10	95 0.3×	97 0.9×	51 0.5×	69 0.7×	47 1.2×	41	333
C. Nalini India	7	104 0.3×	84 0.8×	60 0.6×	47 0.5×	28 0.7×	39	292
Lingfan Yu China	4	188 0.5×	62 0.6×	40 0.4×	114 1.1×	57 1.5×	6	316
Safia Abbas Egypt	10	113 0.3×	46 0.4×	66 0.6×	60 0.6×	23 0.6×	41	256
Prerna Mahajan India	7	133 0.4×	63 0.6×	85 0.8×	109 1.1×	22 0.6×	16	300

Countries citing papers authored by Rohit Babbar

Since Specialization

Citations

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

Fields of papers citing papers by Rohit Babbar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rohit Babbar

This figure shows the co-authorship network connecting the top 25 collaborators of Rohit Babbar. A scholar is included among the top collaborators of Rohit Babbar 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 Rohit Babbar. Rohit Babbar 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

Babbar, Rohit, et al.. (2025). How Well Calibrated are Extreme Multi-label Classifiers? An Empirical Analysis. 1397–1408.

Hsieh, Cho‐Jui, et al.. (2025). UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification. 4124–4133. 1 indexed citations

Hsieh, Cho‐Jui, et al.. (2024). Gandalf: Learning Label-label Correlations in Extreme Multi-label Classification via Label Features. Aaltodoc (Aalto University). 1360–1371. 1 indexed citations

Babbar, Rohit, et al.. (2023). Meta-classifier free negative sampling for extreme multilabel classification. Machine Learning. 113(2). 675–697. 1 indexed citations

Babbar, Rohit, et al.. (2023). InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification. 760–769. 4 indexed citations

Babbar, Rohit, et al.. (2022). Adversarial examples for extreme multilabel text classification. Aaltodoc (Aalto University). 4 indexed citations

Wydmuch, Marek, et al.. (2022). On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1547–1557. 14 indexed citations

Babbar, Rohit, et al.. (2022). Explainable Publication Year Prediction of Eighteenth Century Texts with the BERT Model. Aaltodoc (Aalto University). 7 indexed citations

Babbar, Rohit, et al.. (2022). Speeding-up one-versus-all training for extreme classification via mean-separating initialization. Machine Learning. 111(11). 3953–3976. 8 indexed citations

10.

Wydmuch, Marek, et al.. (2021). Propensity-scored Probabilistic Label Trees. arXiv (Cornell University). 2252–2256. 5 indexed citations

11.

Babbar, Rohit, et al.. (2021). Convex Surrogates for Unbiased Loss Functions in Extreme Classification With Missing Labels. 3711–3720. 17 indexed citations

12.

Joe‐Wong, Carlee, et al.. (2020). Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading. Aaltodoc (Aalto University). 854–863. 140 indexed citations

13.

Babbar, Rohit, et al.. (2019). A Simple and Effective Scheme for Data Pre-processing in Extreme Classification. Aaltodoc (Aalto University). 1 indexed citations

14.

Xiao, Han, et al.. (2019). Bonsai -- Diverse and Shallow Trees for Extreme Multi-label\n Classification. arXiv (Cornell University). 63 indexed citations

15.

Babbar, Rohit & Bernhard Schölkopf. (2019). Data scarcity, robustness and extreme multi-label classification. Machine Learning. 108(8-9). 1329–1351. 73 indexed citations

16.

Babbar, Rohit, Martin Heni, Andreas Peter, et al.. (2018). Prediction of Glucose Tolerance without an Oral Glucose Tolerance Test. Frontiers in Endocrinology. 9. 82–82. 15 indexed citations

17.

Babbar, Rohit & Bernhard Schölkopf. (2017). DiSMEC. 721–729. 100 indexed citations

18.

Babbar, Rohit, et al.. (2016). TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification. 234–242. 3 indexed citations

19.

Babbar, Rohit, et al.. (2014). On power law distributions in large-scale taxonomies. ACM SIGKDD Explorations Newsletter. 16(1). 47–56. 9 indexed citations

20.

Babbar, Rohit & Nidhi Singh. (2010). Clustering based approach to learning regular expressions over large alphabet for noisy unstructured text. 43–50. 21 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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