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Current Projects
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The set of cellular metabolic reactions forms a complex network of interactions, but even in well studied organisms the resulting pathways contain many unidentified enzymes. We study how 'structural' relations between genes in the yeast metabolic pathway are manifested in functional properties of genes and their products, including mRNA expression, protein domain content and cellular localizations. We develop compact and interpretable probabilistic models for representing protein-domain co- occurrences and gene expression time courses. Our models for completing unidentified enzymes in the pathways, achieving accuracy that is significantly superior to existing state-of-the-art approaches. |
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Gal Chechik, Daphne Koller |
| about |
Protein-protein interactions are central to all cellular processes. Discovery of mechanisms underlying protein interaction network will allow for meaningful predictions about the functions of cellular proteins, with possible applications to drug design. We are using probabilistic models to extract patterns from genomic data and make accurate predictions on protein-protein interactions. |
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Haidong Wang, Daphne Koller |
| more info |
Protein-protein interactions project page
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| about |
We consider the important challenge of recognizing a variety of deformable object classes in images. Of fundamental importance and particular difficulty in this setting is the problem of "outlining" an object, rather than simply deciding on its presence or absence. A major obstacle in learning a model that will allow us to address this task is the need for hand-segmented training images. In this paper we present a novel landmark-based, piecewise-linear model of the shape of an object class. We then formulate a learning approach that allows us to learn this model with minimal user supervision. We circumvent the need for hand-segmentation by transferring the shape "essence" of an object from drawings to complex images. We show that our method is able to automatically and effectively learn, detect and localize a variety of object classes. |
| people |
Geremy Heitz, Gal Elidan, Daphne Koller |
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Past Projects
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Acting Rationally with Incomplete Utility Information
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Traditional decision theory assumes a probability distribution over possible states and full knowledge of the user's utility function over these states. In many problems, however, the utility information is unavailable or too complex to be elicited fully. We extend the notion of rational decision making to deal with such cases. |
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Urszula Chajewska, Daphne Koller |
| more info |
Urszula's home page
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| about |
With Active Learning one allows the learner the flexibility to choose the data instances that it feels are most relevant to learn a particular task. We are investigating how active learning can substantially reduce the need for large quantities of data for classification, density estimation and discovering causal structure. |
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Simon Tong, Daphne Koller |
| more info |
DAGS Active Learning Page
Simon Tong's Research Page
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Continuous Time Bayesian Networks
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Continuous time Bayesian networks describe structured stochastic processes that evolve over continuous time. The state of the system is decomposed into a set of local variables whose values change over time. The dynamics of the system are described by specifying the behavior of each local variable as a function of its parents in a directed (possibly cyclic) graph. The model specifies, at any given point in time, the distribution over two aspects: when a local variable changes its value and the next value it takes. These distributions are determined by the variable's current value and the current values of its parents in the graph. |
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Uri Nodelman, Christian Shelton, Daphne Koller |
| about |
Game theory is a framework for describing the interrelated behavior of multiple agents acting rationally. We are interested in compact representations for structured games, including Multi-Agent Influence Diagrams (MAIDs). We are developing algorithms to exploit this structure in order to compute equilibria efficiently for large games, of the sort that might occur in real-world settings. |
| people |
Ben Blum, Daphne Koller, Christian Shelton |
| more info |
Game Tracer Software
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Hybrid Bayesian Networks
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Many real world problems are naturally described as hybrid systems, which contain both discrete and continuous components. Examples include fault diagnostics in physical systems, tracking human motions and more. We are exploring methods to deal with the challenging problems of represntation, inference and learning that come up in these systems. |
| people |
Uri Lerner, Daphne Koller |
| more info |
Uri Lerner's Publications Page
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| about |
We are developing probabilistic models for analyzing biological data
using Probabilistic relational models (PRMs) - an extension of Bayesian
networks to a relational setting, where we have multiple interdependent
objects. Using PRMs, we can incorporate multiple sources of data such
as gene expression patterns, experimental or clinical data, cellular
phenotypes, sequence data, protein 3D structural information, functional
information and more, into the analysis. This enables us to build richer
models that are more suitable for this complex domain.
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| more info |
DAGS Learning Models of Biological and Medical Data Page
GeneXPress
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| about |
Markov Decision Processes are formal models for problems in planning, control, and sequential decision making under uncertainty. In our work, we are mainly concerned with the learning of optimal controls from data and with exploiting structure for efficient computation. Focus is on multi-agent systems, partial observability, and continuous states. |
| people |
Carlos Guestrin, Christian Shelton, Daphne Koller |
| more info |
DAGS MDP Page
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| about |
Probabilistic Relational Models (PRMs) are a language based on relational logic for describing statistical models of structured data. PRMs model complex domains in terms of entities, their properties, and the relations between them. These models represent the uncertainty over the properties of an entity, capturing its probabilistic dependence both on other properties of that entity and on properties of related entities. PRMs can also represent uncertainty over the relational structure itself. |
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Nir Friedman, Lise Getoor, Daphne Koller, Uri Nodelman, Avi Pfeffer, Eran Segal, Ben Taskar |
| more info |
DAGS PRMs Page
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DAGS - Daphne's Approximate Group of Students. All rights reserved.
