This map shows the geographic impact of András György'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 András György with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites András György more than expected).
This network shows the impact of papers produced by András György. 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 András György. The network helps show where András György may publish in the future.
Co-authorship network of co-authors of András György
This figure shows the co-authorship network connecting the top 25 collaborators of András György.
A scholar is included among the top collaborators of András György 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 András György. András György is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lattimore, Tor & András György. (2021). Improved Regret for Zeroth-Order Stochastic Convex Bandits. Conference on Learning Theory. 2938–2964.3 indexed citations
3.
Lattimore, Tor & András György. (2021). Mirror Descent and the Information Ratio. Conference on Learning Theory. 2965–2992.1 indexed citations
4.
Dvijotham, Krishnamurthy, Jamie Hayes, Borja Balle, et al.. (2020). A FRAMEWORK FOR ROBUSTNESS CERTIFICATION OF SMOOTHED CLASSIFIERS USING F-DIVERGENCES. International Conference on Learning Representations.4 indexed citations
5.
György, András, et al.. (2020). ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool. Neural Information Processing Systems. 33. 17478–17488.
György, András, et al.. (2019). Communication without Interception: Defense against Deep-Learning-based Modulation Detection. arXiv (Cornell University).14 indexed citations
8.
Mann, Timothy, et al.. (2018). Learning from Delayed Outcomes with Intermediate Observations. arXiv (Cornell University).3 indexed citations
György, András, et al.. (2017). A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds.. Spiral (Imperial College London). 681–720.1 indexed citations
11.
Huang, Ruitong, Tor Lattimore, András György, & Csaba Szepesvári. (2017). Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities. Journal of Machine Learning Research. 18(145). 1–31.9 indexed citations
12.
György, András & Csaba Szepesvári. (2016). Shifting regret, mirror descent, and matrices. Spiral (Imperial College London). 2943–2951.3 indexed citations
13.
György, András, et al.. (2015). {Near-optimal max-affine estimators for convex regression}. International Conference on Artificial Intelligence and Statistics. 56–64.12 indexed citations
14.
György, András, et al.. (2015). On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments. International Conference on Machine Learning. 1283–1291.1 indexed citations
15.
Huang, Ruitong, et al.. (2015). Deterministic Independent Component Analysis. International Conference on Machine Learning. 2521–2530.2 indexed citations
16.
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
17.
György, András, et al.. (2010). A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping. SZTAKI Publication Repository (Hungarian Academy of Sciences). 852–859.6 indexed citations
18.
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
19.
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
20.
György, András, Tamás Linder, & Gábor Lugosi. (2008). Efficient tracking of the best of many experts. SZTAKI Publication Repository (Hungarian Academy of Sciences).2 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.