Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
The prevalence of elder abuse in institutional settings: a systematic review and meta-analysis
This map shows the geographic impact of Manfred Huber'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 Manfred Huber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Manfred Huber more than expected).
This network shows the impact of papers produced by Manfred Huber. 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 Manfred Huber. The network helps show where Manfred Huber may publish in the future.
Co-authorship network of co-authors of Manfred Huber
This figure shows the co-authorship network connecting the top 25 collaborators of Manfred Huber.
A scholar is included among the top collaborators of Manfred Huber 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 Manfred Huber. Manfred Huber is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Huber, Manfred, et al.. (2015). Using Multi-Agent Options to Reduce Learning Time in Reinforcement Learning.. The Florida AI Research Society. 26–31.1 indexed citations
9.
Huber, Manfred, et al.. (2013). ICA analysis of face color for health applications. The Florida AI Research Society.3 indexed citations
10.
Huber, Manfred, et al.. (2009). Generalizing and Categorizing Skills in Reinforcement Learning Agents Using Partial Policy Homomorphisms.. The Florida AI Research Society.1 indexed citations
11.
Huber, Manfred, et al.. (2007). Effective control knowledge transfer through learning skill and representation hierarchies. International Joint Conference on Artificial Intelligence. 2054–2059.21 indexed citations
12.
Huber, Manfred, et al.. (2005). Learning Macros with an Enhanced LZ78 Algorithm. The Florida AI Research Society. 412–417.1 indexed citations
13.
Huber, Manfred, et al.. (2005). Autonomous subgoal discovery and hierarchical abstraction for reinforcement learning using Monte Carlo method. National Conference on Artificial Intelligence. 1588–1589.3 indexed citations
14.
Huber, Manfred, et al.. (2004). Developing Task Specific Sensing Strategies Using Reinforcement Learning.. The Florida AI Research Society. 738–743.
15.
Huber, Manfred, et al.. (2004). State space reduction for hierarchical reinforcement learning. The Florida AI Research Society. 509–514.8 indexed citations
16.
Huber, Manfred, et al.. (2003). Learning from Reinforcement and Advice Using Composite Reward Functions. The Florida AI Research Society. 361–365.4 indexed citations
17.
Huber, Manfred, et al.. (2003). Subgoal Discovery for Hierarchical Reinforcement Learning Using Learned Policies. The Florida AI Research Society. 346–350.28 indexed citations
18.
Platt, Robert W., Oliver Brock, Andrew H. Fagg, et al.. (2003). A Framework For Humanoid Control and Intelligence. Open Repository and Bibliography (University of Liège).10 indexed citations
19.
Huber, Manfred & Roderic A. Grupen. (1997). Learning to coordinate controllers-reinforcement learning on a control basis. ScholarWorks@UMassAmherst (University of Massachusetts Amherst). 2. 1366–1371.23 indexed citations
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.