Irina Rish

7.8k citations
85 papers · 3.5k · 1 hit paper · h-index 22

Impact in

Papers in

Irina Rish

80 papers receiving 3.3k citations

Irina Rish's Hit Papers

An empirical study of the naive Bayes classifier 2001 · 1.7k citations
1.7k0+8+16Years since publication50010001.5k

Peers

Irina Rish
Comparison fields: 5 of 188
  • Software 185
  • Artificial Intelligence 1.5k
  • Computer Networks and Communications 904
  • Signal Processing 352
  • Health Information Management 117
Replace John Yearwood with:
John Yearwood Australia
L.M. Patnaik India
Randall Wald United States
Charles X. Ling Canada
Mirjana Ivanović Serbia
Marc K. Albert United States
Mark A. Hall New Zealand
Hao Peng China
Zhendong Niu China
Irina Rish relative to John Yearwood Australia John Yearwood's profile →
Citations per field
00.5×1.5×2.3×
John Yearwood · 1×
Citations per year

Countries citing papers authored by Irina Rish

Since Specialization
Citations

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

Fields of papers citing papers by Irina Rish

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Irina Rish. 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 Irina Rish. The network helps show where Irina Rish may publish in the future.

Co-authors

The 25 scholars most cited alongside Irina Rish, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Irina Rish Line = papers co-authored together Irina Rish links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 85 papers — load more, or switch the sort, to bring in the rest.

#Work
1
An empirical study of the naive Bayes classifier
Hit paper breakdown →
20011698
2 2003205
3 2005155
4 2008119
5 2005111
6 2020111
7 2014110
8 202299
9 200396
10 200465
11 200058
12 201245
13
Recognizing End-User Transactions in Performance Management
200035
14 200234
15
Mini-buckets: a general scheme for approximating inference
200234
16 202231
17 200828
18
Active probing strategies for problem diagnosis in distributed systems
200325
19 201324
20
Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning
202023

About Irina Rish

Irina Rish is a scholar working on Artificial Intelligence, Computer Networks and Communications, Cognitive Neuroscience, Molecular Biology and Management Science and Operations Research, having authored 85 papers that have together received 3.5k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (20 papers), Functional Brain Connectivity Studies (12 papers), Machine Learning and Algorithms (11 papers), Software System Performance and Reliability (9 papers), Neural dynamics and brain function (7 papers), Data Stream Mining Techniques (7 papers), Sparse and Compressive Sensing Techniques (6 papers) and Neural Networks and Applications (6 papers). The work is most often cited by research in Software (185 citations), Artificial Intelligence (1.5k citations), Computer Networks and Communications (904 citations), Signal Processing (352 citations) and Health Information Management (117 citations). Irina Rish has collaborated with scholars based in United States, Canada and France. Frequent co-authors include Rina Dechter, Genady Ya. Grabarnik, Alina Beygelzimer, Guillermo Cecchi, S. Ma, G. Grinstein, Ralph Linsker, Marc Brodie, Djallel Bouneffouf and Charų C. Aggarwal. Their work appears in journals such as IBM Journal of Research and Development, PLoS ONE, Scientific Reports, Journal of Vision and Schizophrenia.

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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