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.
A Probabilistic Approach to WLAN User Location Estimation
2002659 citationsTeemu Roos, Petri Myllymäki et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Teemu Roos'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 Teemu Roos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Teemu Roos more than expected).
This network shows the impact of papers produced by Teemu Roos. 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 Teemu Roos. The network helps show where Teemu Roos may publish in the future.
Co-authorship network of co-authors of Teemu Roos
This figure shows the co-authorship network connecting the top 25 collaborators of Teemu Roos.
A scholar is included among the top collaborators of Teemu Roos 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 Teemu Roos. Teemu Roos is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Watanabe, Kazuho & Teemu Roos. (2015). Achievability of asymptotic minimax regret by horizon-dependent and horizon-independent strategies. Journal of Machine Learning Research. 16(1). 2357–2375.
Eggeling, Ralf, Teemu Roos, Petri Myllymäki, & Ivo Große. (2014). Robust learning of inhomogeneous PMMs. Työväentutkimus Vuosikirja. 229–237.5 indexed citations
Myllymäki, Petri, Teemu Roos, & Tommi Jaakkola. (2010). Proceedings of the Fifth European Workshop on Probabilistic Graphical Models. Työväentutkimus Vuosikirja.5 indexed citations
13.
Silander, Tomi, Teemu Roos, & Petri Myllymäki. (2009). Locally Minimax Optimal Predictive Modeling with Bayesian Networks. International Conference on Artificial Intelligence and Statistics. 504–511.5 indexed citations
Silander, Tomi, Teemu Roos, Petri Kontkanen, & Petri Myllymäki. (2008). Factorized normalized maximum likelihood criterion for learning Bayesian network structures. 257–272.31 indexed citations
16.
Roos, Teemu, et al.. (2006). A Compression-Based Method for Stemmatic Analysis. European Conference on Artificial Intelligence. 805–806.3 indexed citations
17.
Roos, Teemu, Peter Grünwald, Petri Myllymäki, & Kirsi Tirri. (2005). Generalization to Unseen Cases. Data Archiving and Networked Services (DANS). 18. 1129–1136.3 indexed citations
18.
Roos, Teemu, Petri Myllymäki, & Kirsi Tirri. (2005). On the Behavior of MDL Denoising. Työväentutkimus Vuosikirja.10 indexed citations
19.
Grünwald, Peter, et al.. (2003). When discriminative learning of Bayesian network parameters is easy. International Joint Conference on Artificial Intelligence. 491–496.9 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.