Gergely Neu

1.5k total citations
18 papers, 243 citations indexed

About

Gergely Neu is a scholar working on Management Science and Operations Research, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Gergely Neu has authored 18 papers receiving a total of 243 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Management Science and Operations Research, 11 papers in Artificial Intelligence and 8 papers in Computer Networks and Communications. Recurrent topics in Gergely Neu's work include Advanced Bandit Algorithms Research (13 papers), Reinforcement Learning in Robotics (6 papers) and Optimization and Search Problems (5 papers). Gergely Neu is often cited by papers focused on Advanced Bandit Algorithms Research (13 papers), Reinforcement Learning in Robotics (6 papers) and Optimization and Search Problems (5 papers). Gergely Neu collaborates with scholars based in Spain, Canada and Hungary. Gergely Neu's co-authors include Csaba Szepesvári, András György, Cristina Cano, András Antos, Sergio Barrachina‐Muñoz, Francesc Wilhelmi, Boris Bellalta, Anders Jönsson, Fan Lü and Sean Meyn and has published in prestigious journals such as IEEE Transactions on Automatic Control, IEEE Transactions on Information Theory and Machine Learning.

In The Last Decade

Gergely Neu

18 papers receiving 230 citations

Peers

Gergely Neu
Alicia P. Wolfe United States
Stéphane Vialle France
Naraig Manjikian Canada
David A. Penry United States
David Holmer United States
Herbert Rubens United States
Nils Kammenhuber Germany
Chenhao Xie China
Newsha Ardalani United States
Fangting Huang China
Alicia P. Wolfe United States
Gergely Neu
Citations per year, relative to Gergely Neu Gergely Neu (= 1×) peers Alicia P. Wolfe

Countries citing papers authored by Gergely Neu

Since Specialization
Citations

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

Fields of papers citing papers by Gergely Neu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gergely Neu

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

All Works

18 of 18 papers shown
1.
Lugosi, Gábor, Mihalis G. Markakis, & Gergely Neu. (2023). On the Hardness of Learning from Censored and Nonstationary Demand. RePEc: Research Papers in Economics. 6(2). 63–83. 1 indexed citations
2.
Lü, Fan, Prashant G. Mehta, Sean Meyn, & Gergely Neu. (2022). Convex Analytic Theory for Convex Q-Learning. 2022 IEEE 61st Conference on Decision and Control (CDC). 130. 4065–4071. 4 indexed citations
3.
Lü, Fan, Prashant G. Mehta, Sean Meyn, & Gergely Neu. (2021). Convex Q-Learning. 4749–4756. 7 indexed citations
4.
Neu, Gergely, et al.. (2019). Beating SGD Saturation with Tail-Averaging and Minibatching. Neural Information Processing Systems. 32. 12568–12577. 4 indexed citations
5.
Riquelme, Carlos, Hugo Penedones, Damien Vincent, et al.. (2019). Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. arXiv (Cornell University). 32. 11872–11882. 3 indexed citations
6.
Wilhelmi, Francesc, Cristina Cano, Gergely Neu, et al.. (2019). Collaborative spatial reuse in wireless networks via selfish multi-armed bandits. Repositori digital de la UPF (Universitat Pompeu Fabra). 41 indexed citations
7.
Lugosi, Gábor, Mihalis G. Markakis, & Gergely Neu. (2019). On the Hardness of Learning from Censored Demand. SSRN Electronic Journal. 1 indexed citations
8.
Wilhelmi, Francesc, Sergio Barrachina‐Muñoz, Boris Bellalta, et al.. (2018). Potential and pitfalls of Multi-Armed Bandits for decentralized Spatial Reuse in WLANs. Journal of Network and Computer Applications. 127. 26–42. 39 indexed citations
9.
Cano, Cristina & Gergely Neu. (2018). Wireless Optimisation via Convex Bandits. 41–47. 3 indexed citations
10.
Neu, Gergely & Vicenç Gómez. (2017). Fast rates for online learning in Linearly Solvable Markov Decision Processes. Conference on Learning Theory. 1567–1588. 1 indexed citations
11.
Liu, Tongliang, Gábor Lugosi, Gergely Neu, & Dacheng Tao. (2017). Algorithmic stability and hypothesis complexity. UTS ePRESS (University of Technology Sydney). 2159–2167. 3 indexed citations
12.
Devroye, Luc, Gábor Lugosi, & Gergely Neu. (2015). Random-Walk Perturbations for Online Combinatorial Optimization. IEEE Transactions on Information Theory. 61(7). 4099–4106. 3 indexed citations
13.
Neu, Gergely, András György, Csaba Szepesvári, & András Antos. (2014). Online Markov Decision Processes Under Bandit Feedback. IEEE Transactions on Automatic Control. 59(3). 676–691. 21 indexed citations
14.
Neu, Gergely, András György, & Csaba Szepesvári. (2012). The adversarial stochastic shortest path problem with unknown transition probabilities. SZTAKI Publication Repository (Hungarian Academy of Sciences). 805–813. 16 indexed citations
15.
György, András & Gergely Neu. (2011). Near-optimal rates for limited-delay universal lossy source coding. 2218–2222. 7 indexed citations
16.
Neu, Gergely, András Antos, András György, & Csaba Szepesvári. (2010). Online Markov Decision Processes under Bandit Feedback. SZTAKI Publication Repository (Hungarian Academy of Sciences). 23. 1804–1812. 38 indexed citations
17.
Neu, Gergely, András György, & Csaba Szepesvári. (2010). The Online Loop-free Stochastic Shortest-Path Problem.. SZTAKI Publication Repository (Hungarian Academy of Sciences). 231–243. 14 indexed citations
18.
Neu, Gergely & Csaba Szepesvári. (2009). Training parsers by inverse reinforcement learning. Machine Learning. 77(2-3). 303–337. 37 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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