Sriraam Natarajan

2.8k total citations
94 papers, 1.3k citations indexed

About

Sriraam Natarajan is a scholar working on Artificial Intelligence, Information Systems and Management Science and Operations Research. According to data from OpenAlex, Sriraam Natarajan has authored 94 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 75 papers in Artificial Intelligence, 14 papers in Information Systems and 9 papers in Management Science and Operations Research. Recurrent topics in Sriraam Natarajan's work include Bayesian Modeling and Causal Inference (38 papers), Data Mining Algorithms and Applications (12 papers) and Reinforcement Learning in Robotics (11 papers). Sriraam Natarajan is often cited by papers focused on Bayesian Modeling and Causal Inference (38 papers), Data Mining Algorithms and Applications (12 papers) and Reinforcement Learning in Robotics (11 papers). Sriraam Natarajan collaborates with scholars based in United States, Germany and India. Sriraam Natarajan's co-authors include Kristian Kersting, Prasad Tadepalli, Jude Shavlik, Tushar Khot, Alan Fern, David Poole, Luc De Raedt, Babak Ahmadi, David Page and Shuo Yang and has published in prestigious journals such as Machine Learning, Knowledge-Based Systems and JAMA Network Open.

In The Last Decade

Sriraam Natarajan

88 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sriraam Natarajan United States 20 961 184 141 127 110 94 1.3k
Robert Andrews Australia 10 840 0.9× 200 1.1× 241 1.7× 130 1.0× 44 0.4× 39 1.3k
Mohammad Ehsan Basiri Iran 20 1.4k 1.5× 302 1.6× 115 0.8× 75 0.6× 54 0.5× 35 1.9k
Mohammad‐Reza Feizi‐Derakhshi Iran 20 690 0.7× 255 1.4× 165 1.2× 45 0.4× 136 1.2× 98 1.4k
Phayung Meesad Thailand 17 625 0.7× 192 1.0× 50 0.4× 158 1.2× 84 0.8× 115 1.2k
Bert Huang United States 18 766 0.8× 176 1.0× 92 0.7× 50 0.4× 112 1.0× 50 1.3k
Huan Zhang China 15 882 0.9× 105 0.6× 78 0.6× 45 0.4× 147 1.3× 65 1.3k
Joachim Diederich Australia 16 1.4k 1.4× 339 1.8× 297 2.1× 86 0.7× 69 0.6× 61 1.9k
Ralf Klinkenberg Germany 13 1.1k 1.2× 318 1.7× 61 0.4× 194 1.5× 208 1.9× 22 1.6k
Hwanjo Yu South Korea 16 731 0.8× 158 0.9× 65 0.5× 36 0.3× 150 1.4× 42 1.1k
B. K. Tripathy India 19 513 0.5× 281 1.5× 371 2.6× 157 1.2× 70 0.6× 145 1.1k

Countries citing papers authored by Sriraam Natarajan

Since Specialization
Citations

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

Fields of papers citing papers by Sriraam Natarajan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sriraam Natarajan

This figure shows the co-authorship network connecting the top 25 collaborators of Sriraam Natarajan. A scholar is included among the top collaborators of Sriraam Natarajan 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 Sriraam Natarajan. Sriraam Natarajan 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.
Natarajan, Sriraam, et al.. (2024). Building Expressive and Tractable Probabilistic Generative Models: A Review. arXiv (Cornell University).
2.
Kunapuli, Gautam, et al.. (2023). Active feature elicitation: An unified framework. Frontiers in Artificial Intelligence. 6. 1029943–1029943. 1 indexed citations
3.
4.
Natarajan, Sriraam, et al.. (2021). Speech Denoising Without Clean Training Data: A Noise2Noise Approach. arXiv (Cornell University). 2716–2720. 22 indexed citations
5.
Doppa, Janardhan Rao, et al.. (2020). Few-Shot Induction of Generalized Logical Concepts via Human Guidance. Frontiers in Robotics and AI. 7. 122–122. 1 indexed citations
6.
Yang, Shuo, et al.. (2020). A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 4460–4468. 6 indexed citations
7.
Dhami, Devendra Singh, Gautam Kunapuli, David Page, & Sriraam Natarajan. (2019). Predicting Drug-Drug Interactions from Molecular Structure Images. arXiv (Cornell University). 1 indexed citations
8.
Molina, Alejandro, Antonio Vergari, Nicola Di Mauro, et al.. (2018). Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 35 indexed citations
9.
Doppa, Janardhan Rao, et al.. (2018). Planning with actively eliciting preferences. Knowledge-Based Systems. 165. 219–227. 4 indexed citations
10.
Doppa, Janardhan Rao, et al.. (2017). Active Preference Elicitation for Planning.. National Conference on Artificial Intelligence.
11.
Dhami, Devendra Singh, et al.. (2017). Machine Learning Applications to Resting-State Functional MR Imaging Analysis. Neuroimaging Clinics of North America. 27(4). 609–620. 9 indexed citations
12.
Natarajan, Sriraam, et al.. (2016). Active Advice Seeking for Inverse Reinforcement Learning. Adaptive Agents and Multi-Agents Systems. 512–520. 8 indexed citations
13.
Kersting, Kristian & Sriraam Natarajan. (2015). Statistical Relational Artificial Intelligence: From Distributions through Actions to Optimization. KI - Künstliche Intelligenz. 29(4). 363–368.
14.
Natarajan, Sriraam, et al.. (2014). A deeper empirical analysis of CBP algorithm: grounding is the bottleneck. National Conference on Artificial Intelligence. 83–85. 2 indexed citations
15.
Natarajan, Sriraam, Kristian Kersting, Tushar Khot, & Jude Shavlik. (2014). Boosted Statistical Relational Learners. SpringerBriefs in computer science. 4 indexed citations
16.
Yang, Shuo, Tushar Khot, Kristian Kersting, et al.. (2014). Learning from Imbalanced Data in Relational Domains: A Soft Margin Approach. 1085–1090. 13 indexed citations
17.
Weiss, Jeremy C., Sriraam Natarajan, & C. David Page. (2013). Learning when to reject an importance sample. National Conference on Artificial Intelligence. 143–145. 3 indexed citations
18.
Natarajan, Sriraam, Tushar Khot, Kristian Kersting, Bernd Gutmann, & Jude Shavlik. (2010). Boosting relational dependency networks. Lirias (KU Leuven). 1–8. 6 indexed citations
19.
Shavlik, Jude & Sriraam Natarajan. (2009). Speeding up inference in Markov logic networks by preprocessing to reduce the size of the resulting grounded network. International Joint Conference on Artificial Intelligence. 1951–1956. 36 indexed citations
20.
Fern, Alan, et al.. (2007). A decision-theoretic model of assistance. International Joint Conference on Artificial Intelligence. 1879–1884. 33 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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