Jason Pazis

659 total citations
11 papers, 205 citations indexed

About

Jason Pazis is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Control and Systems Engineering. According to data from OpenAlex, Jason Pazis has authored 11 papers receiving a total of 205 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 8 papers in Computational Theory and Mathematics and 2 papers in Control and Systems Engineering. Recurrent topics in Jason Pazis's work include Reinforcement Learning in Robotics (10 papers), Formal Methods in Verification (4 papers) and Adaptive Dynamic Programming Control (3 papers). Jason Pazis is often cited by papers focused on Reinforcement Learning in Robotics (10 papers), Formal Methods in Verification (4 papers) and Adaptive Dynamic Programming Control (3 papers). Jason Pazis collaborates with scholars based in United States and Greece. Jason Pazis's co-authors include Michail G. Lagoudakis, Jonathan P. How, Christopher Amato, Ronald Parr, John Vian and Shayegan Omidshafiei and has published in prestigious journals such as Autonomous Agents and Multi-Agent Systems, arXiv (Cornell University) and International Conference on Machine Learning.

In The Last Decade

Jason Pazis

11 papers receiving 196 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jason Pazis United States	7	139	54	49	35	33	11	205
Zongzhang Zhang China	9	175 1.3×	38 0.7×	50 1.0×	59 1.7×	21 0.6×	40	250
Manuela L. Bujorianu United Kingdom	7	30 0.2×	103 1.9×	105 2.1×	25 0.7×	17 0.5×	39	236
Douglas Aberdeen Australia	10	196 1.4×	44 0.8×	16 0.3×	48 1.4×	11 0.3×	17	250
Yi Ouyang United States	10	66 0.5×	17 0.3×	81 1.7×	102 2.9×	47 1.4×	29	220
Hugo Daniel Macedo Denmark	7	39 0.3×	26 0.5×	25 0.5×	34 1.0×	15 0.5×	16	140
Marc Bui France	6	35 0.3×	16 0.3×	21 0.4×	54 1.5×	44 1.3×	42	171
Alireza Farhadi Iran	10	45 0.3×	31 0.6×	215 4.4×	102 2.9×	28 0.8×	45	314
Ilai Bistritz United States	7	77 0.6×	26 0.5×	14 0.3×	163 4.7×	178 5.4×	28	299
Valdivino Alexandre de Santiago Júnior Brazil	8	67 0.5×	38 0.7×	19 0.4×	56 1.6×	16 0.5×	40	263
Pablo Rabanal Spain	8	78 0.6×	36 0.7×	22 0.4×	44 1.3×	19 0.6×	21	149

Countries citing papers authored by Jason Pazis

Since Specialization

Citations

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

Fields of papers citing papers by Jason Pazis

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jason Pazis

This figure shows the co-authorship network connecting the top 25 collaborators of Jason Pazis. A scholar is included among the top collaborators of Jason Pazis 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 Jason Pazis. Jason Pazis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

Omidshafiei, Shayegan, et al.. (2020). Crossmodal attentive skill learner: learning in Atari and beyond with audio–video inputs. Autonomous Agents and Multi-Agent Systems. 34(1). 1 indexed citations

Omidshafiei, Shayegan, Jason Pazis, Christopher Amato, Jonathan P. How, & John Vian. (2017). Deep Decentralized Multi-task Multi-Agent RL under Partial Observability. arXiv (Cornell University). 6 indexed citations

Omidshafiei, Shayegan, Jason Pazis, Christopher Amato, Jonathan P. How, & John Vian. (2017). Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability. arXiv (Cornell University). 2681–2690. 93 indexed citations

Pazis, Jason, Ronald Parr, & Jonathan P. How. (2016). Improving PAC Exploration Using the Median Of Means. DSpace@MIT (Massachusetts Institute of Technology). 29. 3891–3899. 2 indexed citations

Pazis, Jason & Ronald Parr. (2016). Efficient PAC-Optimal Exploration in Concurrent, Continuous State MDPs with Delayed Updates. Proceedings of the AAAI Conference on Artificial Intelligence. 30(1). 2 indexed citations

Pazis, Jason & Ronald Parr. (2013). PAC Optimal Exploration in Continuous Space Markov Decision Processes. Proceedings of the AAAI Conference on Artificial Intelligence. 27(1). 774–781. 24 indexed citations

Pazis, Jason, et al.. (2011). Generalized Value Functions for Large Action Sets. International Conference on Machine Learning. 1185–1192. 13 indexed citations

Pazis, Jason & Michail G. Lagoudakis. (2011). Reinforcement learning in multidimensional continuous action spaces. 97–104. 20 indexed citations

Pazis, Jason & Ronald Parr. (2011). Non-Parametric Approximate Linear Programming for MDPs. Proceedings of the AAAI Conference on Artificial Intelligence. 25(1). 459–464. 13 indexed citations

10.

Pazis, Jason & Michail G. Lagoudakis. (2009). Binary action search for learning continuous-action control policies. 793–800. 23 indexed citations

11.

Pazis, Jason & Michail G. Lagoudakis. (2009). Learning continuous-action control policies. 169–176. 8 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.

Explore authors with similar magnitude of impact