Ilya Tolstikhin

2.8k total citations · 1 hit paper
9 papers, 398 citations indexed

About

Ilya Tolstikhin is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics. According to data from OpenAlex, Ilya Tolstikhin has authored 9 papers receiving a total of 398 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 2 papers in Statistical and Nonlinear Physics. Recurrent topics in Ilya Tolstikhin's work include Generative Adversarial Networks and Image Synthesis (4 papers), Machine Learning and Algorithms (3 papers) and Bayesian Modeling and Causal Inference (2 papers). Ilya Tolstikhin is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (4 papers), Machine Learning and Algorithms (3 papers) and Bayesian Modeling and Causal Inference (2 papers). Ilya Tolstikhin collaborates with scholars based in Germany, United States and Australia. Ilya Tolstikhin's co-authors include Bernhard Schölkopf, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bharath K. Sriperumbudur, Yevgeny Seldin, Paul K. Rubenstein, Krikamol Muandet, David López-Paz and Daniel Keysers and has published in prestigious journals such as Apollo (University of Cambridge), arXiv (Cornell University) and Neural Information Processing Systems.

In The Last Decade

Ilya Tolstikhin

8 papers receiving 377 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Wasserstein Auto-Encoders

2018 262 citations Ilya Tolstikhin, Olivier Bousquet et al. MPG.PuRe (Max Planck Society) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ilya Tolstikhin Germany	5	247	219	42	33	23	9	398
Huayi Tang China	6	370 1.5×	336 1.5×	32 0.8×	27 0.8×	15 0.7×	9	544
Devansh Arpit United States	8	219 0.9×	336 1.5×	32 0.8×	38 1.2×	10 0.4×	13	488
Pavel Izmailov United States	8	248 1.0×	333 1.5×	29 0.7×	13 0.4×	17 0.7×	11	514
Guillaume Alain Canada	7	215 0.9×	186 0.8×	86 2.0×	15 0.5×	18 0.8×	8	410
Predrag Neskovic United States	7	133 0.5×	184 0.8×	33 0.8×	14 0.4×	34 1.5×	18	349
Tingting Zhao China	12	274 1.1×	116 0.5×	14 0.3×	44 1.3×	27 1.2×	31	439
Russ R. Salakhutdinov United States	9	153 0.6×	218 1.0×	21 0.5×	16 0.5×	21 0.9×	16	348
Timur Garipov Russia	4	179 0.7×	260 1.2×	22 0.5×	13 0.4×	15 0.7×	9	391
Changqing Zhang China	11	236 1.0×	231 1.1×	31 0.7×	20 0.6×	9 0.4×	21	420

Countries citing papers authored by Ilya Tolstikhin

Since Specialization

Citations

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

Fields of papers citing papers by Ilya Tolstikhin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ilya Tolstikhin

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

All Works

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9 of 9 papers shown

1.

Alabdulmohsin, Ibrahim, et al.. (2020). What Do Neural Networks Learn When Trained With Random Labels. Neural Information Processing Systems. 33. 19693–19704. 1 indexed citations

2.

Rubenstein, Paul K., Bernhard Schölkopf, & Ilya Tolstikhin. (2018). Learning Disentangled Representations with Wasserstein Auto-Encoders. MPG.PuRe (Max Planck Society). 3 indexed citations

3.

Rubenstein, Paul K., Bernhard Schölkopf, & Ilya Tolstikhin. (2018). Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders. MPG.PuRe (Max Planck Society). 3 indexed citations

4.

Tolstikhin, Ilya, Olivier Bousquet, Sylvain Gelly, & Bernhard Schölkopf. (2018). Wasserstein Auto-Encoders. MPG.PuRe (Max Planck Society). 262 indexed citations breakdown →

5.

Tolstikhin, Ilya, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, & Bernhard Schölkopf. (2017). AdaGAN: Boosting Generative Models. arXiv (Cornell University). 30. 5424–5433. 72 indexed citations

6.

Tolstikhin, Ilya, Bharath K. Sriperumbudur, & Bernhard Schölkopf. (2016). Minimax estimation of maximum mean discrepancy with radial kernels. MPG.PuRe (Max Planck Society). 29. 1938–1946. 38 indexed citations

7.

Simon-Gabriel, Carl-Johann, et al.. (2016). Consistent Kernel Mean Estimation for Functions of Random Variables. Apollo (University of Cambridge). 29. 1732–1740. 4 indexed citations

8.

López-Paz, David, Krikamol Muandet, Bernhard Schölkopf, & Ilya Tolstikhin. (2015). Towards a Learning Theory of Causation.

9.

Tolstikhin, Ilya & Yevgeny Seldin. (2013). PAC-Bayes-empirical-Bernstein inequality. QUT ePrints (Queensland University of Technology). 15 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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