Finale Doshi
Impact in
- Artificial Intelligence top 5%
- Reinforcement Learning in Robotics
- Speech and dialogue systems
- Bayesian Methods and Mixture Models
- Machine Learning and Algorithms
- Bayesian Modeling and Causal Inference
- Multi-Agent Systems and Negotiation
- Topic Modeling
Papers in
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- Reinforcement Learning in Robotics 4
- Bayesian Modeling and Causal Inference 3
- Multi-Agent Systems and Negotiation 3
- Speech and dialogue systems 2
- Machine Learning and Algorithms 2
- Bayesian Methods and Mixture Models 2
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- Mechanics and Biomechanics Studies 1
- Co-authors
- Nicholas Roy (5 shared papers)Joëlle Pineau (3 shared papers)Yee Whye Teh (1 shared paper)Jurgen Van Gael (1 shared paper)Nicholas Roy (3 shared papers)Alborz Geramifard (1 shared paper)Joshua Redding (1 shared paper)Jonathan P. How (1 shared paper)
- Journals
- Connection Science (1 paper)International Conference on Machine Learning (2 papers)UCL Discovery (University College London) (1 paper)PubMed (1 paper)Adaptive Agents and Multi-Agents Systems (1 paper)
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
Finale Doshi
11 papers receiving 262 citations
Peers
Comparison fields: 5 of 52
- Artificial Intelligence 211
- Human-Computer Interaction 19
- Statistics and Probability 27
- Computer Vision and Pattern Recognition 48
- Social Psychology 48
Countries citing papers authored by Finale Doshi
This map shows the geographic impact of Finale Doshi'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 Finale Doshi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Finale Doshi more than expected).
Fields of papers citing papers by Finale Doshi
This network shows the impact of papers produced by Finale Doshi. 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 Finale Doshi. The network helps show where Finale Doshi may publish in the future.
Co-authors
The 17 scholars most cited alongside Finale Doshi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Variational Inference for the Indian Buffet Process | 2009 | 63 |
| 2 | 2007 | 42 | |
| 3 | 2008 | 42 | |
| 4 | 2008 | 38 | |
| 5 | 2008 | 26 | |
| 6 | Online Discovery of Feature Dependencies | 2011 | 25 |
| 7 | Infinite Dynamic Bayesian Networks | 2011 | 17 |
| 8 | 2008 | 14 | |
| 9 | Reinforcement Learning with Limited Reinforcement: Using Bayes Risk for Active Learning in POMDPs. | 2008 | 6 |
| 10 | 2007 | 6 | |
| 11 | The Safe Distance Between Airplanes and the Complexity of an Airspace Sector | 2000 | 2 |
About Finale Doshi
Finale Doshi is a scholar working on Artificial Intelligence, Biomedical Engineering, Molecular Biology, Civil and Structural Engineering and Social Psychology, having authored 11 papers that have together received 281 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (4 papers), Bayesian Modeling and Causal Inference (3 papers), Multi-Agent Systems and Negotiation (3 papers), Speech and dialogue systems (2 papers), Machine Learning and Algorithms (2 papers), Bayesian Methods and Mixture Models (2 papers), Statistical Methods and Bayesian Inference (1 paper) and Mechanics and Biomechanics Studies (1 paper). The work is most often cited by research in Artificial Intelligence (211 citations), Human-Computer Interaction (19 citations), Statistics and Probability (27 citations), Computer Vision and Pattern Recognition (48 citations) and Social Psychology (48 citations). Finale Doshi has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Nicholas Roy, Joëlle Pineau, Yee Whye Teh, Jurgen Van Gael, Nicholas Roy, Alborz Geramifard, Joshua Redding, Jonathan P. How, Nicholas Roy and David Wingate. Their work appears in journals such as Connection Science, International Conference on Machine Learning, UCL Discovery (University College London), PubMed and Adaptive Agents and Multi-Agents Systems.
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.