Sergey Levine

63.2k citations
228 papers · 12.1k indexed · 10 hit papers · h-index 53

Sergey Levine

221 papers receiving 11.6k citations

Hit Papers

Learning Fine-Grained ...197201520262018202250010001.5k

Peers

Sergey Levine
Comparison fields: 5 of 155
  • Control and Systems Engineering 5.2k
  • Computer Vision and Pattern Recognition 4.3k
  • Artificial Intelligence 6.0k
  • Human-Computer Interaction 392
  • Automotive Engineering 804
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Citations per field
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Citations per year

Countries citing papers authored by Sergey Levine

Since Specialization
Citations

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

Fields of papers citing papers by Sergey Levine

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Sergey Levine, 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 Sergey Levine Line = papers co-authored together Sergey Levine links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20256
2 20251
3 202423
4 20245
5 202413
6
Simple and Effective VAE Training with Calibrated Decoders
20211
7
Bayesian Adaptation for Covariate Shift
20216
8
Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning
20212
9 20201
10
Model Based Reinforcement Learning for Atari
202023
11
Learning Social Learning
20200
12
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives
20202
13
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors
20207
14
Datasets for Data-Driven Reinforcement Learning
20202
15
Learning to Walk in the Real World with Minimal Human Effort
202016
16
Learning To Reach Goals Without Reinforcement Learning
20193
17
Extending Deep Model Predictive Control with Safety Augmented Value Estimation from Demonstrations.
20191
18
Meta-Reinforcement Learning of Structured Exploration Strategies
201814
19
Addressing Sample Inefficiency and Reward Bias in Inverse Reinforcement Learning.
20183
20
Nonlinear Inverse Reinforcement Learning with Gaussian Processes
2011145

About Sergey Levine

Sergey Levine is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computer Vision and Pattern Recognition, having authored 228 papers that have together received 12.1k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (105 papers), Robot Manipulation and Learning (57 papers), Human Pose and Action Recognition (33 papers), Adversarial Robustness in Machine Learning (28 papers), Domain Adaptation and Few-Shot Learning (25 papers), Human Motion and Animation (19 papers), Multimodal Machine Learning Applications (19 papers) and Robotic Path Planning Algorithms (13 papers). The work is most often cited by research in Control and Systems Engineering (5.2k citations), Computer Vision and Pattern Recognition (4.3k citations) and Artificial Intelligence (6.0k citations). Sergey Levine has collaborated with scholars based in United States, Canada and Germany. Frequent co-authors include Pieter Abbeel, Chelsea Finn, Vladlen Koltun, Michael I. Jordan, Philipp Moritz, John Schulman, Shixiang Gu, Jitendra Malik, Timothy Lillicrap and Xue Bin Peng. Their work appears in journals such as ACM Transactions on Graphics, IEEE Robotics and Automation Letters, The International Journal of Robotics Research, IEEE Transactions on Robotics and Journal of NeuroEngineering and Rehabilitation.

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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