
Inferring MultiDimensional Rates of Aging from CrossSectional Data
Modeling how individuals evolve over time is a fundamental problem in th...
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Probability Estimation in Face of Irrelevant Information
In this paper, we consider one aspect of the problem of applying decisio...
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Generating New Beliefs From Old
In previous work [BGHK92, BGHK93], we have studied the randomworlds app...
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ContextSpecific Independence in Bayesian Networks
Bayesian networks provide a language for qualitatively representing the ...
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Nonuniform Dynamic Discretization in Hybrid Networks
We consider probabilistic inference in general hybrid networks, which in...
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ObjectOriented Bayesian Networks
Bayesian networks provide a modeling language and associated inference a...
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Tractable Inference for Complex Stochastic Processes
The monitoring and control of any dynamic system depends crucially on th...
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SPOOK: A System for Probabilistic ObjectOriented Knowledge Representation
In previous work, we pointed out the limitations of standard Bayesian ne...
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A General Algorithm for Approximate Inference and its Application to Hybrid Bayes Nets
The clique tree algorithm is the standard method for doing inference in ...
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Discovering the Hidden Structure of Complex Dynamic Systems
Dynamic Bayesian networks provide a compact and natural representation f...
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Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (2001)
This is the Proceedings of the Seventeenth Conference on Uncertainty in ...
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Probabilistic Models for Agents' Beliefs and Decisions
Many applications of intelligent systems require reasoning about the men...
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Policy Iteration for Factored MDPs
Many large MDPs can be represented compactly using a dynamic Bayesian ne...
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Utilities as Random Variables: Density Estimation and Structure Discovery
Decision theory does not traditionally include uncertainty over utility ...
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Exact Inference in Networks with Discrete Children of Continuous Parents
Many real life domains contain a mixture of discrete and continuous vari...
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Continuous Time Bayesian Networks
In this paper we present a language for finite state continuous time Bay...
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Monitoring a Complez Physical System using a Hybrid Dynamic Bayes Net
The Reverse Water Gas Shift system (RWGS) is a complex physical system d...
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Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBNs) describe structured stochastic...
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Expectation Propagation for Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBNs) describe structured stochastic...
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MAP Estimation of SemiMetric MRFs via Hierarchical Graph Cuts
We consider the task of obtaining the maximum a posteriori estimate of d...
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Tuned Models of Peer Assessment in MOOCs
In massive open online courses (MOOCs), peer grading serves as a critica...
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Update Rules for Parameter Estimation in Bayesian Networks
This paper reexamines the problem of parameter estimation in Bayesian n...
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Being Bayesian about Network Structure
In many domains, we are interested in analyzing the structure of the und...
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Discriminative Probabilistic Models for Relational Data
In many supervised learning tasks, the entities to be labeled are relate...
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Learning Hierarchical Object Maps Of NonStationary Environments with mobile robots
Building models, or maps, of robot environments is a highly active resea...
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Learning Module Networks
Methods for learning Bayesian network structure can discover dependency ...
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Learning Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBNs) describe structured stochastic...
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OrderingBased Search: A Simple and Effective Algorithm for Learning Bayesian Networks
One of the basic tasks for Bayesian networks (BNs) is that of learning a...
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Learning Factor Graphs in Polynomial Time & Sample Complexity
We study computational and sample complexity of parameter and structure ...
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Discovering shared and individual latent structure in multiple time series
This paper proposes a nonparametric Bayesian method for exploratory data...
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FirstOrder Conditional Logic Revisited
Conditional logics play an important role in recent attempts to formulat...
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Recovering Articulated Object Models from 3D Range Data
We address the problem of unsupervised learning of complex articulated o...
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Modeling Latent Variable Uncertainty for Lossbased Learning
We consider the problem of parameter estimation using weakly supervised ...
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Daphne Koller
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Daphne Koller is an Israeli and American Professor at the Stanford University Department of Computer Science and an MacArthur Fellow. She is one of the founders of the online learning platform Coursera. Her general field of research is artificial intelligence and its biomedical applications. The 2004 MIT Technology Review article entitled “10 Emerging Technologies That Will Change Your World” on Bayesian machine learning featured Koller.
In 1985 Koller graduated from the Hebrew University of Jerusalem at the age of 17 and in 1986, at the age of 18, received his Masters degree from the same institution. She completed her PhD with Joseph Halpern at Stanford in 1993.
Following her PhD, Koller did postdoctoral research from 1993 to 1995 at the University of California, Berkeley, and in 1995 joined the Stanford University Faculty of Computer Science. In 2004 she was appointed a MacArthur Fellow, in 2011 was elected to the National Academy of Engineering and in 2014 she was elected to the American Academy of Arts and Sciences.
Koller was the first ever $150,000 ACMInfosys Foundation Award in Computing Sciences in April 2008. Coursera was launched by she and Andrew Ng, fellow Stanford IT professor at the AI Laboratory. She served as coCEO with Ng and then as Coursera President. She has been recognized for her contributions to online learners by being named one of the 10 most important people of Newsweek in 2010, one of Time magazine’s 100 most influential people in 2012, and one of Fast Company’s most creative people in 2014. She left Calico in 2018 to join Insitro, a drug discovery startup. Koller focuses primarily on representation, inference and learning and decisionmaking, with a focus on computer vision and computer biology applications. In collaboration with Suchi Saria and Anna Penn of Stanford University, Koller developed PhysiScore that employs a number of information elements to predict whether premature babies are likely to have health problems. From February 2012, she provided a free online course on the subject, including Lise Getoor, Mehran Sahami, Suchi Saria, Eran Segal, and Ben Taskar.
Her distinctions and awards include:
1994: Award for Arthur Samuel Thesis
1996: Faculty Fellowship of the Sloan Foundation
1998: Young Investigator Award of the Naval Research Office
1999: Early Career Presidential Award for Scientists and Engineers
2001: Computers and Pension Prize for IJCAI
2003: Stanford Cox Medal
2004: Oswald G. Villard Fellow for Stanford University Undergraduate Teaching
2007: ACM Computing Prize
2010: 10 Most Important People of Newsweek
2010: 100 Game Changers from Huffington Post
2011: Elected to the National Engineering Academy
2013: 100 Most Influential People of Time Magazine
2014: American Academy of Arts and Sciences Fellow Elected
2014: Most Creative Business People Fast Company
2017: ISCB Fellow elected to the International Computational Biology Society
Koller has contributed one chapter to the 2018 book Architects of intelligence: The Truth About AI by the American futurist Martin Ford from the People Building it.
Koller is married to Opus Capital’s venture capitalist Dan Avida.