4.0k total citations 23 papers, 482 citations indexed
About
Ofir Nachum is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology.
According to data from OpenAlex, Ofir Nachum has authored 23 papers receiving a total of 482 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 2 papers in Molecular Biology. Recurrent topics in Ofir Nachum's work include Reinforcement Learning in Robotics (15 papers), Adversarial Robustness in Machine Learning (3 papers) and Machine Learning and ELM (2 papers). Ofir Nachum is often cited by papers focused on Reinforcement Learning in Robotics (15 papers), Adversarial Robustness in Machine Learning (3 papers) and Machine Learning and ELM (2 papers). Ofir Nachum collaborates with scholars based in United States, Canada and United Kingdom. Ofir Nachum's co-authors include Shixiang Gu, Sergey Levine, Honglak Lee, Ariel Gordon, Bo Chen, Hao Wu, Edward Choi, Tien-Ju Yang, Elad Eban and Mohammad Norouzi and has published in prestigious journals such as arXiv (Cornell University), International Conference on Machine Learning and International Conference on Learning Representations.
Citations per year, relative to Ofir Nachum Ofir Nachum (= 1×)
peers
Alexandra-Bianca Borlea
Countries citing papers authored by Ofir Nachum
Since
Specialization
Citations
This map shows the geographic impact of Ofir Nachum'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 Ofir Nachum with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ofir Nachum more than expected).
This network shows the impact of papers produced by Ofir Nachum. 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 Ofir Nachum. The network helps show where Ofir Nachum may publish in the future.
Co-authorship network of co-authors of Ofir Nachum
This figure shows the co-authorship network connecting the top 25 collaborators of Ofir Nachum.
A scholar is included among the top collaborators of Ofir Nachum 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 Ofir Nachum. Ofir Nachum is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Matsuo, Yutaka, et al.. (2021). Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization. International Conference on Learning Representations.1 indexed citations
4.
Kostrikov, Ilya, Rob Fergus, Jonathan Tompson, & Ofir Nachum. (2021). Offline Reinforcement Learning with Fisher Divergence Critic Regularization. International Conference on Machine Learning. 5774–5783.15 indexed citations
Chow, Yinlam, Ofir Nachum, Aleksandra Faust, Edgar A. Duéñez‐Guzmán, & Mohammad Ghavamzadeh. (2020). Safe Policy Learning for Continuous Control. 801–821.3 indexed citations
Nachum, Ofir, Yinlam Chow, Bo Dai, & Lihong Li. (2019). DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections. arXiv (Cornell University). 32. 2315–2325.10 indexed citations
10.
Jiang, Heinrich & Ofir Nachum. (2019). Identifying and Correcting Label Bias in Machine Learning. International Conference on Artificial Intelligence and Statistics. 702–712.9 indexed citations
Jiang, Heinrich, et al.. (2019). Robustness Guarantees for Density Clustering. 3342–3351.3 indexed citations
13.
Nachum, Ofir, et al.. (2019). Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real. 110–121.8 indexed citations
14.
Ghavamzadeh, Mohammad, Ofir Nachum, & Yinlam Chow. (2018). Path Consistency Learning in Tsallis Entropy Regularized MDPs. International Conference on Machine Learning. 979–988.4 indexed citations
15.
Nachum, Ofir, Mohammad Norouzi, George Tucker, & Dale Schuurmans. (2018). Learning Gaussian Policies from Smoothed Action Value Functions.1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
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incomplete records, variations in author disambiguation, differences in journal indexing, and
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