Shagun Sodhani
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Cognitive Neuroscience
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Co-authors
- Sarath ChandarYoshua BengioYarin GalJoëlle PineauDoina PrecupMarta KwiatkowskaClare LyleAmy Zhang
- Topics
- Multimodal Machine Learning Applications (2 papers)Domain Adaptation and Few-Shot Learning (2 papers)Reinforcement Learning in Robotics (2 papers)
- Journals
- Neural ComputationarXiv (Cornell University)PolyPublie (École Polytechnique de Montréal)
- Partner nations
- CanadaIsraelSwitzerland
In The Last Decade
Shagun Sodhani
6 papers receiving 62 citations
Peers
Comparison fields: 5 of 32
- Artificial Intelligence 53
- Computer Vision and Pattern Recognition 30
- Cognitive Neuroscience 6
- Electrical and Electronic Engineering 6
- Control and Systems Engineering 5
Countries citing papers authored by Shagun Sodhani
This map shows the geographic impact of Shagun Sodhani'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 Shagun Sodhani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shagun Sodhani more than expected).
Fields of papers citing papers by Shagun Sodhani
This network shows the impact of papers produced by Shagun Sodhani. 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 Shagun Sodhani. The network helps show where Shagun Sodhani may publish in the future.
Co-authorship network of co-authors of Shagun Sodhani
This figure shows the co-authorship network connecting the top 25 collaborators of Shagun Sodhani. A scholar is included among the top collaborators of Shagun Sodhani 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 Shagun Sodhani. Shagun Sodhani is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | GraphLog: A Benchmark for Measuring Logical Generalization in Graph Neural Networks | 1 |
| 2 | 9 | |
| 3 | Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives | 2 |
| 4 | Multi-Task Reinforcement Learning as a Hidden-Parameter Block MDP | 2 |
| 5 | Invariant Causal Prediction for Block MDPs | 4 |
| 6 | 48 |
About Shagun Sodhani
Shagun Sodhani is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 6 papers that have together received 66 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Reinforcement Learning in Robotics (2 papers). The work is most often cited by research in Artificial Intelligence (53 citations), Computer Vision and Pattern Recognition (30 citations) and Structural Biology (1 citation). Shagun Sodhani has collaborated with scholars based in Canada, Israel and Switzerland. Frequent co-authors include Sarath Chandar, Yoshua Bengio, Yarin Gal, Joëlle Pineau, Doina Precup, Marta Kwiatkowska, Clare Lyle, Amy Zhang, Angelos Filos and Koustuv Sinha. Their work appears in journals such as Neural Computation, arXiv (Cornell University) and PolyPublie (École Polytechnique de Montréal).
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.