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Raz Kupferman

dblp:33/3008 · DBLP profile ↗
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7ranked-venue papers
0as 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 · 6Theory of computation · 1

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
Probabilistic and Bayesian machine learning · 100%

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

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › probabilistic inference › approximate inference
belief propagation
0.112010
Continuous-Time Belief Propagation · ICML 2010
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models › bayesian network
continuous time bayesian networks
0.112010
Mean Field Variational Approximation for Continuous-Time Bayesian Networks · J. Mach. Learn. Res. 2010
Machine learning › Probabilistic and Bayesian machine learning › probabilistic inference
continuous-time inference
0.112010
Continuous-Time Belief Propagation · ICML 2010
Machine learning › Probabilistic and Bayesian machine learning › structured models
graphical models
0.112010
Mean Field Variational Approximation for Continuous-Time Bayesian Networks · J. Mach. Learn. Res. 2010
Machine learning › Probabilistic and Bayesian machine learning › probabilistic inference › approximate inference › variational inference
mean-field approximation
0.112010
Mean Field Variational Approximation for Continuous-Time Bayesian Networks · J. Mach. Learn. Res. 2010
Machine learning › Probabilistic and Bayesian machine learning › probabilistic inference › approximate inference
variational inference
0.112010
Mean Field Variational Approximation for Continuous-Time Bayesian Networks · J. Mach. Learn. Res. 2010

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

mean-field variational approximation · 0.1
YearPublicationVenuePosition
2012 No justified complaints: on fair sharing of multiple resources
abstract
Fair allocation has been studied intensively in both economics and computer science. This subject has aroused renewed interest with the advent of virtualization and cloud computing. Prior work has typically focused on mechanisms for fair sharing of a single resource. We consider a variant where each user is entitled to a certain fraction of the system's resources, and has a fixed usage profile describing how much he would want from each resource. We provide a new definition for the simultaneous fair allocation of multiple continuously-divisible resources that we call bottleneck-based fairness (BBF). Roughly speaking, an allocation of resources is considered fair if every user either gets all the resources he wishes for, or else gets at least his entitlement on some bottleneck resource, and therefore cannot complain about not receiving more. We show that BBF has several desirable properties such as providing an incentive for sharing, and also promotes high overall utilization of resources; we also compare BBF carefully to another notion of fairness proposed recently, dominant resource fairness.
Danny Dolev, Dror G. Feitelson, Joseph Y. Halpern, Raz Kupferman, Nathan Linial
ITCS4
2010 Continuous-Time Belief Propagation
Tal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman
ICML4
2010 Mean Field Variational Approximation for Continuous-Time Bayesian Networks
Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman
J. Mach. Learn. Res.4
2009 Mean Field Variational Approximation for Continuous-Time Bayesian Networks
Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman
UAI4
2008 Gibbs Sampling in Factorized Continuous-Time Markov Processes
Tal El-Hay, Nir Friedman, Raz Kupferman
UAI3
2006 Continuous Time Markov Networks
Tal El-Hay, Nir Friedman, Daphne Koller, Raz Kupferman
UAI4
2006 Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian Networks
Nir Friedman, Raz Kupferman
UAI2