Luis E. Ortiz

39 papers receiving 1.3k citations

Hit Papers

Parsing clothing in fashion photographs20122026201620212012100200300

Peers

Luis E. Ortiz
Comparison fields: 5 of 86
  • Computer Vision and Pattern Recognition 652
  • Artificial Intelligence 346
  • Management Science and Operations Research 222
  • Automotive Engineering 138
  • Electrical and Electronic Engineering 134
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Xiaoying Gan China
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Luis E. Ortiz relative to Stefano V. Albrecht United Kingdom Stefano V. Albrecht's profile →
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Citations per year

Countries citing papers authored by Luis E. Ortiz

Since Specialization
Citations

This map shows the geographic impact of Luis E. Ortiz'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 Luis E. Ortiz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luis E. Ortiz more than expected).

Fields of papers citing papers by Luis E. Ortiz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Luis E. Ortiz. 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 Luis E. Ortiz. The network helps show where Luis E. Ortiz may publish in the future.

Co-authorship network of co-authors of Luis E. Ortiz

This figure shows the co-authorship network connecting the top 25 collaborators of Luis E. Ortiz. A scholar is included among the top collaborators of Luis E. Ortiz 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 Luis E. Ortiz. Luis E. Ortiz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 2
2 0
3 1
4 1
5 1
6
Correlated Equilibria for Approximate Variational Inference in MRFs.
1
7
Computing Nash Equilibria in Generalized Interdependent Security Games
2
8 26
9 8
10 81
11
Interdependent defense games: modeling interdependent security under deliberate attacks
9
12 298
13 104
14
Sparse and Locally Constant Gaussian Graphical Models
14
15
Maximum Entropy Correlated Equilibria
6
16
Algorithms for Interdependent Security Games
45
17
Concentration inequalities for the missing mass and for histogram rule error
23
18
Nash Propagation for Loopy Graphical Games
26
19
Concentration Inequalities for the Missing Mass and for Histogram Rule Error
1
20
Value-directed sampling methods for monitoring POMDPs
9

About Luis E. Ortiz

Luis E. Ortiz is a scholar working on Management Science and Operations Research, Statistical and Nonlinear Physics and Modeling and Simulation, having authored 43 papers that have together received 1.3k indexed citations. Recurring topics across this work include Game Theory and Applications (10 papers), Bayesian Modeling and Causal Inference (6 papers) and Complex Network Analysis Techniques (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (652 citations), Management Science and Operations Research (222 citations) and Automotive Engineering (138 citations). Luis E. Ortiz has collaborated with scholars based in United States, Japan and Australia. Frequent co-authors include Kota Yamaguchi, Tamara L. Berg, M. Hadi Kiapour, Michael Kearns, Torres Berg, Alexander C. Berg, Samir R. Das, Sham M. Kakade, John Langford and Leslie Pack Kaelbling. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Scientific Reports and SLEEP.

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.

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