Leslie Kaelbling: h-index, Total Citations, and Citation Map
Leslie Kaelbling's h-index is 85 (252 i10-index, 55,228+ total citations across 543+ publications) according to Google Scholar as of September 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 55,228 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Leslie Kaelbling's Citation Metrics
Bibliometric impact based on 543 indexed publications. Of these, 19 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 85
- i10-Index
- 252
- Total Citations
- 55,228
- Citing Countries
- 61
As of September 2026.
Leslie Kaelbling has an h-index of 85 and 55,228 total citations across 543 publications, with research cited by institutions in 61 countries.
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Global Impact Map
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Reinforcement learning: A survey
199614,468
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 55,228 times across 543 publications (h-index 85), according to Google Scholar.
Their most-cited work, “Reinforcement learning: A survey” (1996), has accumulated 14,468 citations. Other influential works include “Planning and acting in partially observable stochastic domains” (1998) with 7,333 citations and “Learning policies for partially observable environments: Scaling up” (1995) with 1,154 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.
