Leslie Kaelbling: h-index, Total Citations, and Citation Map
Leslie Kaelbling's h-index is 86 (248 i10-index, 54,162+ total citations across 537+ publications) according to Google Scholar as of July 2026. Leslie Kaelbling is affiliated with Unknown affiliation.
Leslie Kaelbling is a researcher affiliated with Unknown affiliation, specializing in various fields. Their work has been cited 54,162 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Leslie Kaelbling's Citation Metrics
Bibliometric impact based on 537 indexed publications. Of these, 19 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 86
- i10-Index
- 248
- Total Citations
- 54,162
- Citing Countries
- 55
As of July 2026.
Leslie Kaelbling has an h-index of 86 and 54,162 total citations across 537 publications, with research cited by institutions in 55 countries.
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Global Impact Map
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Reinforcement learning: A survey
199614,398
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
10 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher advanced reinforcement learning by developing input generalization algorithms and synthesizing the field's foundational knowledge through a highly cited survey.
The researcher pioneered methods for learning symbolic models from stochastic domains, establishing a foundational framework for abstract high-level planning that bridges low-level skills and symbolic representations.
The researcher advanced practical reinforcement learning in continuous spaces, establishing a foundational framework later extended to effective mobile robot control.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Leslie Kaelbling's research
Leslie Kaelbling is a researcher at Unknown affiliation. Their work has been cited 54,162 times across 537 publications (h-index 86), according to Google Scholar.
Their most-cited work, “Reinforcement learning: A survey” (1996), has accumulated 14,398 citations. Other influential works include “Planning and acting in partially observable stochastic domains” (1998) with 7,079 citations and “Learning policies for partially observable environments: Scaling up” (1995) with 1,135 citations.
Citations of Leslie Kaelbling's research come primarily from United States, China and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
