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Ivo Shterev

dblp:25/3702 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2012
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Language models and text generation · 43% Probabilistic and Bayesian machine learning · 25% Learning paradigms · 25%
Network and information security
1 paper
Digital forensics and information hiding · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation
watermarking		 0.112012
Bayesian Watermark Attacks · ICML 2012
Digital forensics and information hiding › watermarking › watermarking security
watermark attack		 0.112012
Bayesian Watermark Attacks · ICML 2012
Digital forensics and information hiding
watermarking		 0.112012
Bayesian Watermark Attacks · ICML 2012
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference
bayesian nonparametric model		 0.112008
Hierarchical kernel stick-breaking process for multi-task image analysis · ICML 2008
Machine learning › Learning paradigms
multi-task learning		 0.112008
Hierarchical kernel stick-breaking process for multi-task image analysis · ICML 2008
Computer vision › Segmentation and scene understanding
image segmentation		 0.012008
Hierarchical kernel stick-breaking process for multi-task image analysis · ICML 2008

Methods — techniques the papers use, named apart from their topics

bayesian methods · 0.3variational bayesian inference · 0.1kernel stick-breaking process · 0.1dirichlet process · 0.1
YearPublicationVenuePosition
2012 Bayesian Watermark Attacks
Ivo Shterev, David B. Dunson
 ICML1
2008 Hierarchical kernel stick-breaking process for multi-task image analysis
abstract
The kernel stick-breaking process (KSBP) is employed to segment general imagery, imposing the condition that patches (small blocks of pixels) that are spatially proximate are more likely to be associated with the same cluster (segment). The number of clusters is not set a priori and is inferred from the hierarchical Bayesian model. Further, KSBP is integrated with a shared Dirichlet process prior to simultaneously model multiple images, inferring their inter-relationships. This latter application may be useful for sorting and learning relationships between multiple images. The Bayesian inference algorithm is based on a hybrid of variational Bayesian analysis and local sampling. In addition to providing details on the model and associated inference framework, example results are presented for several image-analysis problems.
Chunping Wang 0001, Ivo Shterev, Lawrence Carin, David B. Dunson
 ICML3