Brian Milch

1.8k citations
19 papers · 924 · h-index 12

Impact in

Papers in

Brian Milch

18 papers receiving 828 citations

Peers

Brian Milch
Comparison fields: 5 of 63
  • Management Science and Operations Research 280
  • Artificial Intelligence 687
  • Signal Processing 116
  • Information Systems 206
  • Computer Networks and Communications 137
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Citations per year

Countries citing papers authored by Brian Milch

Since Specialization
Citations

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

Fields of papers citing papers by Brian Milch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Brian Milch, 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 Brian Milch Line = papers co-authored together Brian Milch links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 2003211
2 2007182
3
Identity Uncertainty and Citation Matching
2002176
4 2003106
5
Lifted probabilistic inference with counting formulas
200888
6
Approximate inference for infinite contingent Bayesian networks
200527
7 200525
8 200224
9
Probabilistic models with unknown objects
200623
10
First-Order Probabilistic Models for Information Extraction
200318
11
Multi-Agent Filtering with Infinitely Nested Beliefs
200815
12 201314
13
Ignorable Information in Multi-Agent Scenarios
20086
14
Random-World Semantics and Syntactic Independence for Expressive Languages
20083
15 20032
16 20082
17
Reasoning about Large Populations with Lifted Probabilistic Inference
20071
18
Artificial General Intelligence through Large-Scale, Multimodal Bayesian Learning
20081
19
Structured models for multi-agent interactions
20010

About Brian Milch

Brian Milch is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications, Information Systems and Computer Vision and Pattern Recognition, having authored 19 papers that have together received 924 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (12 papers), Data Quality and Management (7 papers), Advanced Text Analysis Techniques (3 papers), Game Theory and Applications (3 papers), Video Analysis and Summarization (3 papers), Topic Modeling (3 papers), Advanced Database Systems and Queries (3 papers) and Web Data Mining and Analysis (3 papers). The work is most often cited by research in Management Science and Operations Research (280 citations), Artificial Intelligence (687 citations), Signal Processing (116 citations), Information Systems (206 citations) and Computer Networks and Communications (137 citations). Brian Milch has collaborated with scholars based in United States, Germany and Spain. Frequent co-authors include Daphne Koller, Stuart Russell, Bhaskara Marthi, Andrey Kolobov, Ilya Shpitser, Hanna Pasula, David Sontag, Sergey Brin, Monika Henzinger and Bay-Wei Chang. Their work appears in journals such as Games and Economic Behavior, IEEE Intelligent Systems, World Wide Web, The MIT Press eBooks and arXiv (Cornell University).

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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