Melinda Gervasio
About
In The Last Decade
Melinda Gervasio
34 papers receiving 298 citations
Peers
Comparison fields: 5 of 61
- Artificial Intelligence 231
- Computer Networks and Communications 72
- Information Systems 63
- Information Systems and Management 59
- Computer Vision and Pattern Recognition 55
Countries citing papers authored by Melinda Gervasio
This map shows the geographic impact of Melinda Gervasio'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 Melinda Gervasio with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Melinda Gervasio more than expected).
Fields of papers citing papers by Melinda Gervasio
This network shows the impact of papers produced by Melinda Gervasio. 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 Melinda Gervasio. The network helps show where Melinda Gervasio may publish in the future.
Co-authorship network of co-authors of Melinda Gervasio
This figure shows the co-authorship network connecting the top 25 collaborators of Melinda Gervasio. A scholar is included among the top collaborators of Melinda Gervasio 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 Melinda Gervasio. Melinda Gervasio is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Interestingness Elements for Explainable Reinforcement Learning through Introspection. | 7 |
| 2 | Explanation to Avert Surprise. | 5 |
| 3 | 7 | |
| 4 | 33 | |
| 5 | Learning by Demonstration to Support Military Planning and Decision Making | 3 |
| 6 | Question Asking to Inform Preference Learning: A Case Study. | 2 |
| 7 | Iteration Learning by Demonstration. | 7 |
| 8 | Evaluating User-Adaptive Systems: Lessons from Experiences with a Personalized Meeting Scheduling Assistant | 9 |
| 9 | 7 | |
| 10 | 8 | |
| 11 | Question Asking to Inform Procedure Learning | 4 |
| 12 | Multi-Criteria Evaluation in User-Centric Distributed Scheduling Agents. | 1 |
| 13 | A Personalized Time Management Assistant: Research Directions. | 6 |
| 14 | 38 | |
| 15 | Mixed-Initiative Issues for a Personalized Time Management Assistant | 5 |
| 16 | Learning User Evaluation Functions for Adaptive Scheduling Assistance | 14 |
| 17 | Learning to predict user operations for adaptive scheduling | 6 |
| 18 | Learning to integrate reactivity and deliberation in uncertain planning and scheduling problems | 1 |
| 19 | Learning general completable reactive plans | 8 |
| 20 | Learning Completable Reactive Plans through Achievability Proofs | 2 |
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.