Citations per year, relative to Olivier Bachem Olivier Bachem (= 1×)
peers
Luca Cazzanti
Countries citing papers authored by Olivier Bachem
Since
Specialization
Citations
This map shows the geographic impact of Olivier Bachem'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 Olivier Bachem with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Olivier Bachem more than expected).
This network shows the impact of papers produced by Olivier Bachem. 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 Olivier Bachem. The network helps show where Olivier Bachem may publish in the future.
Co-authorship network of co-authors of Olivier Bachem
This figure shows the co-authorship network connecting the top 25 collaborators of Olivier Bachem.
A scholar is included among the top collaborators of Olivier Bachem 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 Olivier Bachem. Olivier Bachem is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Dadashi, Robert, Nino Vieillard, Léonard Hussenot, et al.. (2022). Offline Reinforcement Learning as Anti-exploration. Proceedings of the AAAI Conference on Artificial Intelligence. 36(7). 8106–8114.9 indexed citations
4.
Andrychowicz, Marcin, Anton Raichuk, Piotr Stańczyk, et al.. (2021). What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study. International Conference on Learning Representations.26 indexed citations
5.
Wüthrich, Manuel, Peter Gehler, Ole Winther, et al.. (2021). The Role of Pretrained Representations for the OOD Generalization of RL Agents. arXiv (Cornell University).2 indexed citations
6.
Locatello, Francesco, Michael Tschannen, Stefan Bauer, et al.. (2020). Disentangling Factors of Variations Using Few Labels. arXiv (Cornell University).21 indexed citations
7.
Minderer, Matthias, Olivier Bachem, Neil Houlsby, & Michael Tschannen. (2020). Automatic Shortcut Removal for Self-Supervised Representation Learning. International Conference on Machine Learning. 1. 6927–6937.2 indexed citations
Djolonga, Josip, Mario Lučić, Marco Cuturi, et al.. (2019). Evaluating Generative Models using Divergence Frontiers. arXiv (Cornell University).1 indexed citations
10.
Steenkiste, Sjoerd van, Francesco Locatello, Jürgen Schmidhuber, & Olivier Bachem. (2019). Are Disentangled Representations Helpful for Abstract Visual Reasoning. MPG.PuRe (Max Planck Society). 32. 14178–14191.31 indexed citations
11.
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
12.
Wüthrich, Manuel, Francesco Locatello, Martin Breidt, et al.. (2019). On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. MPG.PuRe (Max Planck Society). 32. 15661–15672.14 indexed citations
13.
Zhai, Xiaohua, Joan Puigcerver, Alexander Kolesnikov, et al.. (2019). The Visual Task Adaptation Benchmark. arXiv (Cornell University).22 indexed citations
14.
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
15.
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
16.
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
17.
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
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.