Mario Lučić

7.2k total citations
30 papers, 710 citations indexed

About

Mario Lučić is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Mario Lučić has authored 30 papers receiving a total of 710 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 18 papers in Computer Vision and Pattern Recognition and 7 papers in Signal Processing. Recurrent topics in Mario Lučić's work include Generative Adversarial Networks and Image Synthesis (6 papers), Machine Learning and Algorithms (6 papers) and Data Management and Algorithms (5 papers). Mario Lučić is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (6 papers), Machine Learning and Algorithms (6 papers) and Data Management and Algorithms (5 papers). Mario Lučić collaborates with scholars based in United States, Switzerland and Germany. Mario Lučić's co-authors include Olivier Bachem, Andreas Krause, Neil Houlsby, Xiaohua Zhai, Marvin Ritter, Ting Chen, Sylvain Gelly, S. Hamed Hassani, Mehdi S. M. Sajjadi and Olivier Bousquet and has published in prestigious journals such as ACM Transactions on Graphics, Journal of Machine Learning Research and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

In The Last Decade

Mario Lučić

30 papers receiving 679 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mario Lučić United States	15	436	350	98	63	62	30	710
Changyou Chen United States	17	531 1.2×	511 1.5×	80 0.8×	44 0.7×	94 1.5×	55	1.0k
Shuyang Gu China	8	723 1.7×	156 0.4×	62 0.6×	180 2.9×	106 1.7×	26	984
Jun Han China	15	390 0.9×	139 0.4×	76 0.8×	175 2.8×	106 1.7×	76	774
Hanjiang Lai China	16	1.3k 2.9×	308 0.9×	88 0.9×	37 0.6×	126 2.0×	53	1.5k
Hong Peng China	15	246 0.6×	255 0.7×	74 0.8×	12 0.2×	67 1.1×	55	678
Junpeng Wang United States	11	430 1.0×	308 0.9×	71 0.7×	73 1.2×	20 0.3×	38	666
Jishang Wei United States	12	198 0.5×	215 0.6×	54 0.6×	36 0.6×	17 0.3×	26	438
Hirotaka Hachiya Japan	14	146 0.3×	332 0.9×	47 0.5×	30 0.5×	28 0.5×	40	542
Michael Laszlo United States	9	155 0.4×	345 1.0×	102 1.0×	150 2.4×	71 1.1×	21	637

Countries citing papers authored by Mario Lučić

Since Specialization

Citations

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

Fields of papers citing papers by Mario Lučić

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mario Lučić

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

Gritsenko, Alexey A., Xuehan Xiong, Josip Djolonga, et al.. (2024). End-to-End Spatio-Temporal Action Localisation with Video Transformers. 18373–18383. 5 indexed citations

Duckworth, Daniel, Peter Hedman, Christian Reiser, et al.. (2024). SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Exploration. ACM Transactions on Graphics. 43(4). 1–13. 20 indexed citations

Georgescu, Mariana-Iuliana, et al.. (2023). Audiovisual Masked Autoencoders. 16098–16108. 21 indexed citations

Sajjadi, Mehdi S. M., Aravindh Mahendran, Thomas Kipf, et al.. (2023). RUST: Latent Neural Scene Representations from Unposed Imagery. 17297–17306. 6 indexed citations

Tschannen, Michael, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, & Mario Lučić. (2020). On Mutual Information Maximization for Representation Learning. arXiv (Cornell University). 15 indexed citations

Locatello, Francesco, Stefan Bauer, Mario Lučić, et al.. (2020). A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. Journal of Machine Learning Research. 21(209). 1–62. 2 indexed citations

Djolonga, Josip, Mario Lučić, Marco Cuturi, et al.. (2019). Evaluating Generative Models using Divergence Frontiers. arXiv (Cornell University). 1 indexed citations

Lučić, Mario, Michael Tschannen, Marvin Ritter, et al.. (2019). High-Fidelity Image Generation With Fewer Labels. International Conference on Machine Learning. 4183–4192. 16 indexed citations

Zhai, Xiaohua, Joan Puigcerver, Alexander Kolesnikov, et al.. (2019). The Visual Task Adaptation Benchmark. arXiv (Cornell University). 22 indexed citations

10.

Chen, Ting, Xiaohua Zhai, Marvin Ritter, Mario Lučić, & Neil Houlsby. (2019). Self-Supervised GANs via Auxiliary Rotation Loss. 12146–12155. 182 indexed citations

11.

Chen, Ting, Mario Lučić, Neil Houlsby, & Sylvain Gelly. (2018). On Self Modulation for Generative Adversarial Networks. arXiv (Cornell University). 14 indexed citations

12.

Bachem, Olivier, Mario Lučić, & Silvio Lattanzi. (2018). One-shot Coresets: The Case of k-Clustering. International Conference on Artificial Intelligence and Statistics. 784–792. 6 indexed citations

13.

Sajjadi, Mehdi S. M., Olivier Bachem, Mario Lučić, Olivier Bousquet, & Sylvain Gelly. (2018). Assessing Generative Models via Precision and Recall. arXiv (Cornell University). 31. 5228–5237. 52 indexed citations

14.

Lučić, Mario, Matthew Faulkner, Andreas Krause, & Dan Feldman. (2018). Training Gaussian mixture models at scale via coresets. Journal of Machine Learning Research. 18(1). 5885–5909. 15 indexed citations

15.

Bachem, Olivier, Mario Lučić, & Andreas Krause. (2017). Distributed and Provably Good Seedings for k-Means in Constant Rounds. International Conference on Machine Learning. 70. 292–300. 8 indexed citations

16.

Bachem, Olivier, Mario Lučić, S. Hamed Hassani, & Andreas Krause. (2017). Uniform Deviation Bounds for k-Means Clustering. International Conference on Machine Learning. 70. 283–291. 6 indexed citations

17.

Lučić, Mario, Matthew Faulkner, Andreas Krause, & Dan Feldman. (2017). Training Mixture Models at Scale via Coresets. arXiv (Cornell University). 8 indexed citations

18.

Bachem, Olivier, Mario Lučić, Hamed Hassani, & Andreas Krause. (2016). Fast and Provably Good Seedings for k-Means. Neural Information Processing Systems. 29. 55–63. 46 indexed citations

19.

Bachem, Olivier, Mario Lučić, S. Hamed Hassani, & Andreas Krause. (2016). Approximate K-Means++ in Sublinear Time. Proceedings of the AAAI Conference on Artificial Intelligence. 30(1). 78 indexed citations

20.

Lučić, Mario, Olivier Bachem, & Andreas Krause. (2016). Linear-time outlier detection via sensitivity. 1795–1801. 1 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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