Tightening LP Relaxations for MAP using Message Passing (2009)
David Sontag, Talya Meltzer, Amir Globerson, Tommi Jaakkola, Yair Weiss
Linear Programming (LP) relaxations have become powerful tools for finding the most probable (MAP) configuration in graphical models. These relaxations can be solved efficiently using message-passing...
Clusters and Coarse Partitions in LP Relaxations (2009)
David Sontag, Amir Globerson, Tommi Jaakkola
We propose a new class of consistency constraints for Linear Programming (LP) relaxations for finding the most probable (MAP) configuration in graphical models. Usual cluster-based LP relaxations...
Clusters and Coarse Partitions in LP Relaxations (2009)
Sontag, David, Globerson, Amir, Jaakkola, Tommi
We propose a new class of consistency constraints for Linear Programming (LP) relaxations for finding the most probable (MAP) configuration in graphical models. Usual cluster-based LP relaxations...
1 BLOG: Probabilistic Models with Unknown Objects (2008)
Brian Milch, Bhaskara Marthi, Stuart Russell, David Sontag, Daniel L. Ong, Andrey Kolobov
Human beings and AI systems must convert sensory input into some understanding of what is going on in the world around them. That is, they must make inferences about the objects and events that...
New Outer Bounds on the Marginal Polytope (2008)
We give a new class of outer bounds on the marginal polytope, and propose a cutting-plane algorithm for efficiently optimizing over these constraints. When combined with a concave upper bound on the...
We give a new class of outer bounds on the marginal polytope, and propose a cutting-plane algorithm for efficiently optimizing over these constraints. When combined with a concave upper bound on the...
On Iteratively Constraining the Marginal Polytope for Approximate Inference and MAP (2008)
We propose a cutting-plane style algorithm for finding the maximum a posteriori (MAP) state and approximately inferring marginal probabilities in discrete Markov Random Fields (MRFs). The variational...
Probabilistic modeling of systematic errors in two-hybrid experiments (2007)
We describe a novel probabilistic approach to estimating errors in two-hybrid (2H) experiments. Such experiments are frequently used to elucidate protein-protein interaction networks in a...
New outer bounds on the marginal polytope (2007)
We give a new class of outer bounds on the marginal polytope, and propose a cutting-plane algorithm for efficiently optimizing over these constraints. When combined with a concave upper bound on the...
BLOG: Probabilistic Models with Unknown Objects (2006)
Milch, Brian, Marthi, Bhaskara, Russell, Stuart, Sontag, David, Ong, Daniel L., Kolobov, Andrey
We introduce BLOG, a formal language for defining probability models with unknown objects and identity uncertainty. A BLOG model describes a generative process in which some steps add objects to the...
Blog: Probabilistic models with unknown objects (2005)
Brian Milch, Bhaskara Marthi, Stuart Russell, David Sontag, Daniel L. Ong, Andrey Kolobov
This paper introduces and illustrates BLOG, a formal language for defining probability models over worlds with unknown objects and identity uncertainty. BLOG unifies and extends several existing...
Blog: Probabilistic models with unknown objects (2005)
Brian Milch, Bhaskara Marthi, Stuart Russell, David Sontag, Daniel L. Ong, Andrey Kolobov
This paper introduces and illustrates BLOG, a formal language for defining probability models over worlds with unknown objects and identity uncertainty. BLOG unifies and extends several existing...
Approximate inference for infinite contingent Bayesian networks (2005)
Brian Milch, Bhaskara Marthi, David Sontag, Stuart Russell, Daniel L. Ong, Andrey Kolobov
In many practical problems—from tracking aircraft based on radar data to building a bibliographic database based on citation lists—we want to reason about an unbounded number of unseen objects...
Approximate Inference for Infinite Contingent Bayesian Networks (2005)
Brian Milch, Bhaskara Marthi, David Sontag, Stuart Russell, Daniel L. Ong, Andrey Kolobov
In many practical problems---from tracking aircraft based on radar data to building a bibliographic database based on citation lists---we want to reason about an unbounded number of unseen objects...
Blog: Probabilistic models with unknown objects (2005)
Brian Milch, Bhaskara Marthi, Stuart Russell, David Sontag, Daniel L. Ong, Andrey Kolobov
This paper introduces and illustrates BLOG, a formal language for defining probability models over worlds with unknown objects and identity uncertainty. BLOG unifies and extends several existing...
CS 281B Project Report: Marginalized Kernels for Object Recognition ∗ (2004)
It is relatively easy to obtain sets of images that are known to contain objects of a certain category, such as “face”, or “bicycle”. There has been promising work on using such data sets —...