Pierre‐Luc Bacon

653 total citations
11 papers, 45 citations indexed

About

Pierre‐Luc Bacon is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computational Theory and Mathematics. According to data from OpenAlex, Pierre‐Luc Bacon has authored 11 papers receiving a total of 45 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 4 papers in Management Science and Operations Research and 2 papers in Computational Theory and Mathematics. Recurrent topics in Pierre‐Luc Bacon's work include Reinforcement Learning in Robotics (7 papers), Adaptive Dynamic Programming Control (2 papers) and Bayesian Modeling and Causal Inference (2 papers). Pierre‐Luc Bacon is often cited by papers focused on Reinforcement Learning in Robotics (7 papers), Adaptive Dynamic Programming Control (2 papers) and Bayesian Modeling and Causal Inference (2 papers). Pierre‐Luc Bacon collaborates with scholars based in Canada, United States and Algeria. Pierre‐Luc Bacon's co-authors include Doina Precup, Maxime Chevalier-Boisvert, Pascal Vincent, Timothy Mann, Rishabh Agarwal, Shie Mannor, Hanane Dagdougui, Daniel J. Mankowitz, Abdelaziz Touati and Joëlle Pineau and has published in prestigious journals such as Applied Energy, AI Magazine and International Conference on Machine Learning.

In The Last Decade

Pierre‐Luc Bacon

10 papers receiving 42 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Pierre‐Luc Bacon Canada	5	31	10	10	7	6	11	45
Edoardo Conti Denmark	2	41 1.3×	14 1.4×	4 0.4×	4 0.6×	4 0.7×	2	52
Vitaly Kurin United Kingdom	6	36 1.2×	7 0.7×	3 0.3×	6 0.9×	4 0.7×	7	59
R. Bardenet France	2	10 0.3×	8 0.8×	6 0.6×	3 0.4×	2 0.3×	2	23
Léonard Hussenot United States	4	40 1.3×	4 0.4×	3 0.3×	5 0.7×	10 1.7×	5	50
Kristina Marasović Croatia	3	34 1.1×	6 0.6×	4 0.4×	2 0.3×	9 1.5×	4	67
Noah Siegel United States	3	21 0.7×	3 0.3×	6 0.6×	3 0.4×	7 1.2×	6	28
W. A. T. Wan Abdullah Malaysia	2	22 0.7×	4 0.4×	3 0.3×	5 0.7×	3 0.5×	3	35
Simon Bartels Denmark	2	24 0.8×	7 0.7×	1 0.1×	6 0.9×	3 0.5×	4	48
Jiechuan Jiang China	3	47 1.5×	6 0.6×	4 0.4×	8 1.1×	2 0.3×	5	54
Chen-Yu Wei United States	5	24 0.8×	3 0.3×	21 2.1×	17 2.4×	5 0.8×	15	46

Countries citing papers authored by Pierre‐Luc Bacon

Since Specialization

Citations

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

Fields of papers citing papers by Pierre‐Luc Bacon

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pierre‐Luc Bacon. 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 Pierre‐Luc Bacon. The network helps show where Pierre‐Luc Bacon may publish in the future.

Co-authorship network of co-authors of Pierre‐Luc Bacon

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

All Works

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11 of 11 papers shown

Dagdougui, Hanane, et al.. (2024). Neural differential equations for temperature control in buildings under demand response programs. Applied Energy. 368. 123433–123433. 6 indexed citations

Agarwal, Rishabh, et al.. (2022). Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation. Proceedings of the AAAI Conference on Artificial Intelligence. 36(7). 7886–7894. 4 indexed citations

Chevalier-Boisvert, Maxime, et al.. (2020). Options of Interest: Temporal Abstraction with Interest Functions. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 4444–4451. 9 indexed citations

Bacon, Pierre‐Luc, et al.. (2019). A Lagrangian Method for Inverse Problems in Reinforcement Learning. 1 indexed citations

Touati, Abdelaziz, Pierre‐Luc Bacon, Doina Precup, & Pascal Vincent. (2018). Convergent Tree Backup and Retrace with Function Approximation. International Conference on Machine Learning. 4955–4964. 4 indexed citations

Mankowitz, Daniel J., Timothy Mann, Pierre‐Luc Bacon, Doina Precup, & Shie Mannor. (2018). Learning Robust Options. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 7 indexed citations

Bacon, Pierre‐Luc & Doina Precup. (2018). Constructing Temporal Abstractions Autonomously in Reinforcement Learning. AI Magazine. 39(1). 39–50. 4 indexed citations

Pineau, Joëlle & Pierre‐Luc Bacon. (2015). Analyzing open data from the city of Montreal. International Conference on Machine Learning. 11–16.

Bacon, Pierre‐Luc, Borja Balle, & Doina Precup. (2015). Learning and planning with timing information in Markov decision processes. Uncertainty in Artificial Intelligence. 111–120. 1 indexed citations

10.

Bacon, Pierre‐Luc. (2013). On the Bottleneck Concept for Options Discovery: Theoretical Underpinnings and Extension in Continuous State Spaces. eScholarship@McGill (McGill). 6 indexed citations

11.

Bacon, Pierre‐Luc & Doina Precup. (2013). Using label propagation for learning temporally abstract actions in reinforcement learning. 3 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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