Adith Swaminathan

1.9k total citations · 1 hit paper
22 papers, 756 citations indexed

About

Adith Swaminathan is a scholar working on Artificial Intelligence, Management Science and Operations Research and Information Systems. According to data from OpenAlex, Adith Swaminathan has authored 22 papers receiving a total of 756 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 12 papers in Management Science and Operations Research and 8 papers in Information Systems. Recurrent topics in Adith Swaminathan's work include Advanced Bandit Algorithms Research (11 papers), Machine Learning and Algorithms (7 papers) and Optimization and Search Problems (5 papers). Adith Swaminathan is often cited by papers focused on Advanced Bandit Algorithms Research (11 papers), Machine Learning and Algorithms (7 papers) and Optimization and Search Problems (5 papers). Adith Swaminathan collaborates with scholars based in United States, United Kingdom and Netherlands. Adith Swaminathan's co-authors include Thorsten Joachims, Tobias Schnabel, Ashudeep Singh, Lin Ma, Bailu Ding, Sudipto Das, Maarten de Rijke, Alekh Agarwal, Emma Brunskill and Peter I. Frazier and has published in prestigious journals such as Journal of Machine Learning Research, AI Magazine and arXiv (Cornell University).

In The Last Decade

Adith Swaminathan

22 papers receiving 716 citations

Hit Papers

Unbiased Learning-to-Rank with Biased Feedback 2017 2026 2020 2023 2017 50 100 150 200 250
What are hit papers?

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.

  1. Unbiased Learning-to-Rank with Biased Feedback
    2017 252 citations Thorsten Joachims, Adith Swaminathan et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Adith Swaminathan United States 11 466 382 334 84 81 22 756
Laurent Charlin Canada 13 695 1.5× 593 1.6× 200 0.6× 155 1.8× 65 0.8× 20 977
Denis Charles United States 10 362 0.8× 185 0.5× 165 0.5× 61 0.7× 216 2.7× 21 703
Yungho Leu Taiwan 11 216 0.5× 116 0.3× 237 0.7× 50 0.6× 88 1.1× 32 640
Paolo Viappiani Switzerland 11 212 0.5× 171 0.4× 154 0.5× 45 0.5× 129 1.6× 33 459
Toby Walker United States 5 144 0.3× 164 0.4× 81 0.2× 27 0.3× 41 0.5× 5 471
Georges Dupret United States 14 283 0.6× 553 1.4× 78 0.2× 142 1.7× 79 1.0× 28 818
Ewa Dominowska United Kingdom 2 215 0.5× 396 1.0× 132 0.4× 152 1.8× 79 1.0× 2 659
Gordon Sun United States 12 422 0.9× 301 0.8× 74 0.2× 124 1.5× 39 0.5× 18 614
Azin Ashkan Canada 13 452 1.0× 669 1.8× 235 0.7× 192 2.3× 213 2.6× 22 1.0k

Countries citing papers authored by Adith Swaminathan

Since Specialization
Citations

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

Fields of papers citing papers by Adith Swaminathan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Adith Swaminathan

This figure shows the co-authorship network connecting the top 25 collaborators of Adith Swaminathan. A scholar is included among the top collaborators of Adith Swaminathan 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 Adith Swaminathan. Adith Swaminathan 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.
Joachims, Thorsten, Ben London, Yi Su, Adith Swaminathan, & Lequn Wang. (2021). Recommendations as treatments. AI Magazine. 42(3). 19–30. 7 indexed citations
2.
Swaminathan, Adith, et al.. (2020). Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration. Neural Information Processing Systems. 33. 1264–1274. 6 indexed citations
3.
Ma, Lin, Bailu Ding, Sudipto Das, & Adith Swaminathan. (2020). Active Learning for ML Enhanced Database Systems. 175–191. 42 indexed citations
4.
Joachims, Thorsten, et al.. (2020). REVEAL 2020: Bandit and Reinforcement Learning from User Interactions. 628–629. 2 indexed citations
5.
Broll, Brian, et al.. (2019). Customizing Scripted Bots: Sample Efficient Imitation Learning for Human-like Behavior in Minecraft. 2 indexed citations
6.
Swaminathan, Adith, et al.. (2019). Off-Policy Policy Gradient with State Distribution Correction. Uncertainty in Artificial Intelligence. 1180–1190. 4 indexed citations
7.
Joachims, Thorsten, et al.. (2019). REVEAL 2019. 568–569. 1 indexed citations
8.
Makar, Maggie, Adith Swaminathan, & Emre Kıcıman. (2019). A Distillation Approach to Data Efficient Individual Treatment Effect Estimation. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 4544–4551. 3 indexed citations
9.
Joachims, Thorsten, Adith Swaminathan, & Maarten de Rijke. (2018). Deep Learning with Logged Bandit Feedback. International Conference on Learning Representations. 27 indexed citations
10.
Joachims, Thorsten, Adith Swaminathan, Yves Raimond, Olivier Koch, & Flavian Vasile. (2018). REVEAL 2018. 514–515. 6 indexed citations
11.
Joachims, Thorsten, Adith Swaminathan, & Tobias Schnabel. (2018). Unbiased Learning-to-Rank with Biased Feedback. 5284–5288. 28 indexed citations
12.
Joachims, Thorsten, Adith Swaminathan, & Tobias Schnabel. (2017). Unbiased Learning-to-Rank with Biased Feedback. 781–789. 252 indexed citations breakdown →
13.
Joachims, Thorsten & Adith Swaminathan. (2016). Counterfactual Evaluation and Learning for Search, Recommendation and Ad Placement. 1199–1201. 27 indexed citations
14.
Schnabel, Tobias, et al.. (2016). Recommendations as Treatments: Debiasing Learning and Evaluation. arXiv (Cornell University). 48. 1670–1679. 66 indexed citations
15.
Schnabel, Tobias, Adith Swaminathan, Peter I. Frazier, & Thorsten Joachims. (2016). Unbiased Comparative Evaluation of Ranking Functions. 109–118. 9 indexed citations
16.
Swaminathan, Adith & Thorsten Joachims. (2015). The self-normalized estimator for counterfactual learning. Neural Information Processing Systems. 28. 3231–3239. 89 indexed citations
17.
Swaminathan, Adith & Thorsten Joachims. (2015). Batch learning from logged bandit feedback through counterfactual risk minimization. Journal of Machine Learning Research. 16(1). 1731–1755. 73 indexed citations
18.
Schnabel, Tobias, Adith Swaminathan, & Thorsten Joachims. (2015). Unbiased Ranking Evaluation on a Budget. 935–937. 1 indexed citations
19.
Swaminathan, Adith & Thorsten Joachims. (2015). Counterfactual Risk Minimization: Learning from Logged Bandit Feedback. arXiv (Cornell University). 814–823. 43 indexed citations
20.
Swaminathan, Adith, et al.. (2012). Temporal corpus summarization using submodular word coverage. 754–763. 36 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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