Akshat Kumar

1.2k total citations
73 papers, 526 citations indexed

About

Akshat Kumar is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computer Networks and Communications. According to data from OpenAlex, Akshat Kumar has authored 73 papers receiving a total of 526 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Artificial Intelligence, 16 papers in Management Science and Operations Research and 15 papers in Computer Networks and Communications. Recurrent topics in Akshat Kumar's work include Reinforcement Learning in Robotics (15 papers), Bayesian Modeling and Causal Inference (13 papers) and Auction Theory and Applications (11 papers). Akshat Kumar is often cited by papers focused on Reinforcement Learning in Robotics (15 papers), Bayesian Modeling and Causal Inference (13 papers) and Auction Theory and Applications (11 papers). Akshat Kumar collaborates with scholars based in Singapore, United States and India. Akshat Kumar's co-authors include Shlomo Zilberstein, Hoong Chuin Lau, Pradeep Varakantham, Duc Thien Nguyen, Adrian Petcu, Boi Faltings, Daniel Sheldon, Marc Toussaint, William Yeoh and Aakar Gupta and has published in prestigious journals such as Journal of Artificial Intelligence Research, Journal of Physics A Mathematical and Theoretical and ACM Transactions on Intelligent Systems and Technology.

In The Last Decade

Akshat Kumar

63 papers receiving 503 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Akshat Kumar Singapore	13	220	138	97	77	68	73	526
Zongtao Duan China	12	201 0.9×	106 0.8×	47 0.5×	115 1.5×	105 1.5×	59	718
Aleksander Byrski Poland	12	281 1.3×	164 1.2×	40 0.4×	55 0.7×	16 0.2×	84	528
Chris Hinde United Kingdom	13	162 0.7×	76 0.6×	97 1.0×	18 0.2×	69 1.0×	65	503
Faruk Polat Türkiye	12	242 1.1×	95 0.7×	34 0.4×	58 0.8×	23 0.3×	77	625
Lillian J. Ratliff United States	13	120 0.5×	41 0.3×	96 1.0×	122 1.6×	65 1.0×	59	541
Francisco Herrera Triguero Spain	10	200 0.9×	59 0.4×	37 0.4×	38 0.5×	26 0.4×	26	380
Fangwei Zhang China	10	115 0.5×	50 0.4×	151 1.6×	115 1.5×	24 0.4×	39	435
Derek Long Australia	9	400 1.8×	119 0.9×	36 0.4×	76 1.0×	19 0.3×	18	647
Linbo Luo Singapore	15	181 0.8×	59 0.4×	25 0.3×	149 1.9×	34 0.5×	40	637
Marco Valtorta United States	13	332 1.5×	93 0.7×	86 0.9×	27 0.4×	21 0.3×	51	529

Countries citing papers authored by Akshat Kumar

Since Specialization

Citations

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

Fields of papers citing papers by Akshat Kumar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Akshat Kumar

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

Kumar, Akshat, et al.. (2021). Action Selection for Composable Modular Deep Reinforcement Learning. Autonomous Agents and Multi-Agent Systems. 565–573. 1 indexed citations

Kumar, Akshat, et al.. (2020). Hierarchical Multiagent Reinforcement Learning for Maritime Traffic Management. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 1278–1286. 6 indexed citations

Kumar, Akshat, et al.. (2019). Graph Based Optimization for Multiagent Cooperation. Adaptive Agents and Multi-Agents Systems. 1497–1505. 2 indexed citations

Kumar, Akshat, et al.. (2018). A REVIEW ON UNMANNED WATER SURFACE VEHICLE. International Journal of Advanced Research in Computer Science. 9. 95–99. 4 indexed citations

Lau, Hoong Chuin, et al.. (2017). A Multi-Agent System for Coordinating Vessel Traffic. Adaptive Agents and Multi-Agents Systems. 1814–1816. 1 indexed citations

Varakantham, Pradeep, et al.. (2016). Robust Influence Maximization: (Extended Abstract). Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 1395–1396. 5 indexed citations

Varakantham, Pradeep, et al.. (2016). Robust Influence Maximization: (Extended Abstract). Adaptive Agents and Multi-Agents Systems. 1395–1396. 3 indexed citations

Nguyen, Thien Huu, Akshat Kumar, Hoong Chuin Lau, & Daniel Sheldon. (2016). Approximate inference using DC programming for collective graphical models. International Conference on Artificial Intelligence and Statistics. 51. 685–693. 4 indexed citations

Kumar, Akshat, et al.. (2015). Probabilistic inference based message-passing for resource constrained DCOPs. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 411–417. 5 indexed citations

10.

Kumar, Akshat, et al.. (2015). Message Passing for Collective Graphical Models. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 853–861. 17 indexed citations

11.

Sheldon, Daniel, et al.. (2013). Approximate Inference in Collective Graphical Models. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 28(3). 1004–1012. 22 indexed citations

12.

Wu, Xiaojian, Akshat Kumar, Daniel Sheldon, & Shlomo Zilberstein. (2013). Parameter learning for latent network diffusion. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 2923–2930. 5 indexed citations

13.

Yeoh, William, Akshat Kumar, & Shlomo Zilberstein. (2013). Automated generation of interaction graphs for value-factored dec-POMDPs. International Joint Conference on Artificial Intelligence. 411–417. 1 indexed citations

14.

Kumar, Akshat, Shlomo Zilberstein, & Marc Toussaint. (2012). Message Passing Algorithms for MAP Estimation Using DC Programming. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 22. 656–664. 4 indexed citations

15.

Kumar, Akshat & Shlomo Zilberstein. (2011). Message-passing algorithms for large structured decentralized POMDPs. Adaptive Agents and Multi-Agents Systems. 1087–1088. 1 indexed citations

16.

Kumar, Akshat & Shlomo Zilberstein. (2010). Point-based backup for decentralized POMDPs: complexity and new algorithms. Adaptive Agents and Multi-Agents Systems. 1. 1315–1322. 24 indexed citations

17.

Kumar, Akshat & Shlomo Zilberstein. (2010). MAP Estimation for Graphical Models by Likelihood Maximization. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 23. 1180–1188. 7 indexed citations

18.

Kumar, Akshat, Boi Faltings, & Adrian Petcu. (2009). Distributed constraint optimization with structured resource constraints. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 2. 923–930. 38 indexed citations

19.

Kumar, Akshat, Adrian Petcu, & Boi Faltings. (2008). H-DPOP: using hard constraints for search space pruning in DCOP. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 1. 325–330. 11 indexed citations

20.

Kumar, Akshat, Adrian Petcu, & Boi Faltings. (2007). H-DPOP: Using Hard Constraints to Prune the Search Space. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 7 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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