Finale Doshi‐Velez
About
In The Last Decade
Finale Doshi‐Velez
97 papers receiving 3.3k citations
Hit Papers
Peers
Comparison fields: 5 of 172
- Artificial Intelligence 1.9k
- Health Informatics 667
- Radiology, Nuclear Medicine and Imaging 329
- Computer Vision and Pattern Recognition 299
- Cognitive Neuroscience 280
Countries citing papers authored by Finale Doshi‐Velez
This map shows the geographic impact of Finale Doshi‐Velez'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‐Velez 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‐Velez more than expected).
Fields of papers citing papers by Finale Doshi‐Velez
This network shows the impact of papers produced by Finale Doshi‐Velez. 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‐Velez. The network helps show where Finale Doshi‐Velez may publish in the future.
Co-authorship network of co-authors of Finale Doshi‐Velez
This figure shows the co-authorship network connecting the top 25 collaborators of Finale Doshi‐Velez. A scholar is included among the top collaborators of Finale Doshi‐Velez 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 Finale Doshi‐Velez. Finale Doshi‐Velez is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Title | Journal | Authors | Indexed citations |
|---|---|---|---|---|
| 1 | Estimating Upper Extremity Fugl-Meyer Assessment Scores From Reaching Motions Using Wearable Sensors | IEEE Journal of Biomedical and Health Informatics | Yu Zhou, Tommaso Proietti et al. | 3 |
| 2 | Preference-based assistance optimization for lifting and lowering with a soft back exosuit | Science Advances | Weiwei Pan, Finale Doshi‐Velez et al. | 3 |
| 3 | Efficiently identifying individuals at high risk for treatment resistance in major depressive disorder using electronic health records | Journal of Affective Disorders | Isaac Lage, Thomas H. McCoy et al. | 10 |
| 4 | Do clinicians follow heuristics in prescribing antidepressants? | Journal of Affective Disorders | Isaac Lage, Melanie F. Pradier et al. | 3 |
| 5 | Machine Learning Techniques for Accountability | AI Magazine | Been Kim, Finale Doshi‐Velez | 13 |
| 6 | Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning | arXiv (Cornell University) | Kai Wang, Haipeng Chen et al. | 8 |
| 7 | Incorporating Interpretable Output Constraints in Bayesian Neural Networks | Neural Information Processing Systems | Moritz A. Graule, Himabindu Lakkaraju et al. | 1 |
| 8 | Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Sepsis Treatment | arXiv (Cornell University) | Mingyu Lu, Daby Sow et al. | 1 |
| 9 | Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs | Neural Information Processing Systems | Joseph Futoma, Finale Doshi‐Velez et al. | 1 |
| 10 | Predicting change in diagnosis from major depression to bipolar disorder after antidepressant initiation | Neuropsychopharmacology | Melanie F. Pradier, Michael C. Hughes et al. | 18 |
| 11 | Unsupervised Learning of PCFGs with Normalizing Flow | Finale Doshi‐Velez, Timothy A. Miller et al. | 17 | |
| 12 | Semi-Supervised Prediction-Constrained Topic Models | International Conference on Artificial Intelligence and Statistics | Michael C. Hughes, Thomas H. McCoy et al. | 6 |
| 13 | A Roadmap for a Rigorous Science of Interpretability. | arXiv (Cornell University) | Finale Doshi‐Velez, Been Kim | 55 |
| 14 | Promoting Domain-Specific Terms in Topic Models with Informative Priors. | arXiv (Cornell University) | Angela Fan, Finale Doshi‐Velez et al. | 1 |
| 15 | Mind the Gap: a generative approach to interpretable feature selection and extraction | Neural Information Processing Systems | Been Kim, Julie Shah et al. | 53 |
| 16 | Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning | DSpace@MIT (Massachusetts Institute of Technology) | Finale Doshi‐Velez, David Pfau et al. | 6 |
| 17 | Unfolding physiological state: mortality modelling in intensive care units | DSpace@MIT (Massachusetts Institute of Technology) | Marzyeh Ghassemi, Finale Doshi‐Velez et al. | 32 |
| 18 | Transfer Learning by Discovering Latent Task Parametrizations | Neural Information Processing Systems | Finale Doshi‐Velez, George Konidaris | 1 |
| 19 | Nonparametric Bayesian Policy Priors for Reinforcement Learning | DSpace@MIT (Massachusetts Institute of Technology) | Finale Doshi‐Velez, David Wingate et al. | 18 |
| 20 | The Infinite Partially Observable Markov Decision Process | Neural Information Processing Systems | Finale Doshi‐Velez | 44 |
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.