Ilya Kostrikov

2.3k citations
15 papers · 356 indexed · h-index 11
Journals
International Conference on Machine Learning (1 paper)arXiv (Cornell University) (5 papers)International Conference on Learning Representations (2 papers)

In The Last Decade

Ilya Kostrikov

14 papers receiving 341 citations

Peers

Ilya Kostrikov
Comparison fields: 5 of 52
  • Computer Vision and Pattern Recognition 218
  • Computer Graphics and Computer-Aided Design 35
  • Human-Computer Interaction 53
  • Artificial Intelligence 144
  • Computational Mechanics 54
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Citations per field
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Citations per year

Countries citing papers authored by Ilya Kostrikov

Since Specialization
Citations

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

Fields of papers citing papers by Ilya Kostrikov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 20 scholars most cited alongside Ilya Kostrikov, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ilya Kostrikov Line = papers co-authored together Ilya Kostrikov links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1 202329
2 20220
3
Offline Reinforcement Learning with Fisher Divergence Critic Regularization
202115
4
Automatic Data Augmentation for Generalization in Reinforcement Learning
202120
5
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
202125
6 20211
7 20199
8
Addressing Sample Inefficiency and Reward Bias in Inverse Reinforcement Learning.
20183
9
Intrinsic motivation and automatic curricula via asymmetric self-play
201827
10
Surface Networks
201815
11 201824
12 201832
13 201690
14 201446
15 201420

About Ilya Kostrikov

Ilya Kostrikov is a scholar working on Computer Graphics and Computer-Aided Design, Human-Computer Interaction, Computer Vision and Pattern Recognition, Artificial Intelligence and Geology, having authored 15 papers that have together received 356 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (10 papers), Human Pose and Action Recognition (5 papers), Hand Gesture Recognition Systems (2 papers), Adaptive Dynamic Programming Control (2 papers), Evolutionary Algorithms and Applications (2 papers), Computer Graphics and Visualization Techniques (2 papers), Adversarial Robustness in Machine Learning (2 papers) and 3D Shape Modeling and Analysis (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (218 citations), Computer Graphics and Computer-Aided Design (35 citations), Human-Computer Interaction (53 citations), Artificial Intelligence (144 citations) and Computational Mechanics (54 citations). Ilya Kostrikov has collaborated with scholars based in United States, Switzerland and Germany. Frequent co-authors include Jüergen Gall, Bastian Leibe, Umer Rafi, Rob Fergus, Sergey Levine, Jonathan Tompson, Denis Yarats, Laura Smith, Daniele Panozzo and Joan Bruna. Their work appears in journals such as International Conference on Machine Learning, arXiv (Cornell University), International Conference on Learning Representations and Computer Vision and Pattern Recognition.

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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