Adith Swaminathan
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- Advanced Bandit Algorithms Research 11
- Information Systems top 2%
- Recommender Systems and Techniques 4
- Information Retrieval and Search Behavior 3
- Artificial Intelligence top 5%
- Machine Learning and Algorithms 7
- Topic Modeling 3
- Reinforcement Learning in Robotics 3
- Statistics and Probability top 5%
- Statistical Methods and Inference 3
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- Optimization and Search Problems 5
- Co-authors
- Thorsten JoachimsTobias SchnabelAshudeep SinghLin MaBailu DingSudipto DasMaarten de RijkeAlekh Agarwal
- Partner nations
- United StatesUnited KingdomNetherlands
In The Last Decade
Adith Swaminathan
22 papers receiving 716 citations
Hit Papers
Peers
Comparison fields: 5 of 59
- Management Science and Operations Research 334
- Information Systems 382
- Computer Science Applications 80
- Artificial Intelligence 466
- Statistics and Probability 69
Countries citing papers authored by Adith Swaminathan
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
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
The 22 scholars most cited alongside Adith Swaminathan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 7 | |
| 2 | Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration | 2020 | 6 |
| 3 | 2020 | 42 | |
| 4 | 2020 | 2 | |
| 5 | Customizing Scripted Bots: Sample Efficient Imitation Learning for Human-like Behavior in Minecraft | 2019 | 2 |
| 6 | Off-Policy Policy Gradient with State Distribution Correction | 2019 | 4 |
| 7 | 2019 | 1 | |
| 8 | 2019 | 3 | |
| 9 | Deep Learning with Logged Bandit Feedback | 2018 | 27 |
| 10 | 2018 | 6 | |
| 11 | 2018 | 28 | |
| 12 | Unbiased Learning-to-Rank with Biased Feedbackbreakdown → | 2017 | 252 |
| 13 | 2016 | 27 | |
| 14 | 2016 | 66 | |
| 15 | 2016 | 9 | |
| 16 | The self-normalized estimator for counterfactual learning | 2015 | 89 |
| 17 | Batch learning from logged bandit feedback through counterfactual risk minimization | 2015 | 73 |
| 18 | 2015 | 1 | |
| 19 | 2015 | 43 | |
| 20 | 2012 | 36 |
About Adith Swaminathan
Adith Swaminathan is a scholar working on Management Science and Operations Research, Artificial Intelligence and Computer Science Applications, having authored 22 papers that have together received 756 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (11 papers), Machine Learning and Algorithms (7 papers), Optimization and Search Problems (5 papers), Recommender Systems and Techniques (4 papers), Information Retrieval and Search Behavior (3 papers), Topic Modeling (3 papers), Statistical Methods and Inference (3 papers) and Reinforcement Learning in Robotics (3 papers). The work is most often cited by research in Management Science and Operations Research (334 citations), Information Systems (382 citations) and Computer Science Applications (80 citations). Adith Swaminathan has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent 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.
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.