Daniel J. Mankowitz
Impact in
- Artificial Intelligence top 5%
- Reinforcement Learning in Robotics
- Adversarial Robustness in Machine Learning
- Domain Adaptation and Few-Shot Learning
- Control and Systems Engineering top 10%
- Robot Manipulation and Learning
Papers in
-
- Reinforcement Learning in Robotics 16
- Machine Learning and Algorithms 4
- Adversarial Robustness in Machine Learning 3
- AI-based Problem Solving and Planning 2
- Data Stream Mining Techniques 1
-
- Advanced Bandit Algorithms Research 4
- Simulation Techniques and Applications 2
- Co-authors
- Nir Levine (3 shared papers)Gabriel Dulac-Arnold (3 shared papers)Cosmin Păduraru (4 shared papers)Jerry Li (3 shared papers)Todd Hester (2 shared papers)Sven Gowal (1 shared paper)Shie Mannor (12 shared papers)Tom Zahavy (5 shared papers)
- Journals
- Fusion Engineering and Design (1 paper)Machine Learning (1 paper)International Conference on Learning Representations (1 paper)Uncertainty in Artificial Intelligence (1 paper)arXiv (Cornell University) (5 papers)
- Partner nations
- IsraelUnited StatesUnited Kingdom
In The Last Decade
Daniel J. Mankowitz
19 papers receiving 523 citations
Daniel J. Mankowitz's Hit Papers
Peers
Comparison fields: 5 of 81
- Artificial Intelligence 301
- Control and Systems Engineering 127
- Computer Vision and Pattern Recognition 81
- Automotive Engineering 46
- Management Science and Operations Research 38
Countries citing papers authored by Daniel J. Mankowitz
This map shows the geographic impact of Daniel J. Mankowitz'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 Daniel J. Mankowitz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel J. Mankowitz more than expected).
Fields of papers citing papers by Daniel J. Mankowitz
This network shows the impact of papers produced by Daniel J. Mankowitz. 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 Daniel J. Mankowitz. The network helps show where Daniel J. Mankowitz may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel J. Mankowitz, 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 | Challenges of real-world reinforcement learning: definitions, benchmarks and analysis Hit paper breakdown → | 2021 | 320 |
| 2 | 2017 | 116 | |
| 3 | Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning | 2018 | 21 |
| 4 | 2019 | 19 | |
| 5 | Reward Constrained Policy Optimization | 2018 | 11 |
| 6 | Adaptive Skills Adaptive Partitions (ASAP) | 2016 | 8 |
| 7 | Time-regularized interrupting options | 2014 | 7 |
| 8 | 2018 | 7 | |
| 9 | Learning How Not to Act in Text-based Games | 2018 | 5 |
| 10 | 2024 | 5 | |
| 11 | Shallow Updates for Deep Reinforcement Learning | 2017 | 5 |
| 12 | A Bayesian Approach to Robust Reinforcement Learning | 2019 | 4 |
| 13 | RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. | 2020 | 2 |
| 14 | Time-Regularized Interrupting Options (TRIO) | 2014 | 2 |
| 15 | A Constrained Multi-Objective Reinforcement Learning Framework | 2021 | 2 |
| 16 | 2020 | 2 | |
| 17 | 2019 | 2 | |
| 18 | Learning When to Switch between Skills in a High Dimensional Domain | 2015 | 1 |
| 19 | Robust Reinforcement Learning for Continuous Control with Model Misspecification | 2020 | 1 |
About Daniel J. Mankowitz
Daniel J. Mankowitz is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computational Theory and Mathematics, Computer Networks and Communications and Management Information Systems, having authored 19 papers that have together received 540 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (16 papers), Advanced Bandit Algorithms Research (4 papers), Machine Learning and Algorithms (4 papers), Adversarial Robustness in Machine Learning (3 papers), AI-based Problem Solving and Planning (2 papers), Simulation Techniques and Applications (2 papers), Data Stream Mining Techniques (1 paper) and Optimization and Search Problems (1 paper). The work is most often cited by research in Artificial Intelligence (301 citations), Control and Systems Engineering (127 citations), Computer Vision and Pattern Recognition (81 citations), Automotive Engineering (46 citations) and Management Science and Operations Research (38 citations). Daniel J. Mankowitz has collaborated with scholars based in Israel, United States and United Kingdom. Frequent co-authors include Nir Levine, Gabriel Dulac-Arnold, Cosmin Păduraru, Jerry Li, Todd Hester, Sven Gowal, Shie Mannor, Tom Zahavy, Chen Tessler and Timothy Mann. Their work appears in journals such as Fusion Engineering and Design, Machine Learning, International Conference on Learning Representations, Uncertainty in Artificial Intelligence and arXiv (Cornell University).
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.