Matthew Hausknecht
- Computer Vision and Pattern Recognition top 0.5%
- Artificial Intelligence top 1%
- Biomedical Engineering top 10%
- Control and Systems Engineering top 5%
- Human-Computer Interaction top 2%
- Co-authors
- Joe Yue-Hei NgRajat MongaSudheendra VijayanarasimhanOriol VinyalsGeorge TodericiPeter StoneTsz-Chiu AuRisto Miikkulainen
- Topics
- Reinforcement Learning in Robotics (6 papers)Artificial Intelligence in Games (6 papers)Topic Modeling (5 papers)
- Journals
- Journal of Experimental Psychology GeneralIEEE Transactions on Neural Networks and Learning SystemsNeural Networks
- Partner nations
- United StatesUnited KingdomIndia
In The Last Decade
Matthew Hausknecht
21 papers receiving 1.9k citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Computer Vision and Pattern Recognition 1.3k
- Artificial Intelligence 966
- Biomedical Engineering 298
- Control and Systems Engineering 175
- Human-Computer Interaction 140
Countries citing papers authored by Matthew Hausknecht
This map shows the geographic impact of Matthew Hausknecht'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 Matthew Hausknecht with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew Hausknecht more than expected).
Fields of papers citing papers by Matthew Hausknecht
This network shows the impact of papers produced by Matthew Hausknecht. 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 Matthew Hausknecht. The network helps show where Matthew Hausknecht may publish in the future.
Co-authorship network of co-authors of Matthew Hausknecht
This figure shows the co-authorship network connecting the top 25 collaborators of Matthew Hausknecht. A scholar is included among the top collaborators of Matthew Hausknecht 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 Matthew Hausknecht. Matthew Hausknecht is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | ALFWorld: Aligning Text and Embodied Environments for Interactive Learning | 31 |
| 2 | 32 | |
| 3 | 51 | |
| 4 | Customizing Scripted Bots: Sample Efficient Imitation Learning for Human-like Behavior in Minecraft | 2 |
| 5 | Counting to Explore and Generalize in Text-based Games | 1 |
| 6 | 1 | |
| 7 | Now I Remember! Episodic Memory For Reinforcement Learning | 1 |
| 8 | 17 | |
| 9 | 24 | |
| 10 | The Impact of Determinism on Learning Atari 2600 Games | 11 |
| 11 | Beyond short snippets: Deep networks for video classificationbreakdown → | 1422 |
| 12 | 90 | |
| 13 | 30 | |
| 14 | 21 | |
| 15 | 61 | |
| 16 | 85 | |
| 17 | 54 | |
| 18 | Absent causes, present effects: How omissions cause events | 4 |
| 19 | Austin Villa 2010 Standard Platform Team Report | 1 |
| 20 | 9 |
About Matthew Hausknecht
Matthew Hausknecht is a scholar working on Artificial Intelligence, Transportation and Software, having authored 21 papers that have together received 2.0k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (6 papers), Artificial Intelligence in Games (6 papers) and Topic Modeling (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.3k citations), Artificial Intelligence (966 citations) and Human-Computer Interaction (140 citations). Matthew Hausknecht has collaborated with scholars based in United States, United Kingdom and India. Frequent co-authors include Joe Yue-Hei Ng, Rajat Monga, Sudheendra Vijayanarasimhan, Oriol Vinyals, George Toderici, Peter Stone, Tsz-Chiu Au, Risto Miikkulainen, Joel Lehman and Phillip Wolff. Their work appears in journals such as Journal of Experimental Psychology General, IEEE Transactions on Neural Networks and Learning Systems and Neural Networks.
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.