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...
CS281B/Stat241B: Advanced Topics in Learning & Decision Making (2008)
Cross Validation, Information Criteria, Lecturer Michael, I. Jordan, Scribes Charless Fowlkes, Bhaskara Marthi
1 Cross Validation Cross validation is one of the simplest and often most practical approaches to model selection. The setupis that we are given training data D and test data D test, and we would...
CS294-2 Markov Chain Monte Carlo: Foundations Applications Fall 2002 (2008)
Lecturer Alistair, Sinclair Scribes, Kamin Whitehouse, Bhaskara Marthi
niversal constant C. Proof: We will assume W = 0; the extension to general W is simple. Let the w i be scaled so that min|w i = 1. Assume that max|w i = B. We will first show that there exists a...
research.microsoft.com/{#cmbishop,#ablake} (2007)
Christopher M. Bishop, Andrew Blake, Bhaskara Marthi
www.cs.berkeley.edu/#bhaskara/ We consider the problem of enhancing the resolution of video through the addition of perceptually plausible high frequency information. Our approach is based on a...
CS294-2 Markov Chain Monte Carlo: Foundations Applications Fall 2002 (2007)
Lecturer Alistair, Sinclair Scribes, Richard Bourgon, Bhaskara Marthi
n e. This upper bound is in fact tight, up to the lower order term, and we now sketch why that's true: 4-1 We can identify a speci c event with a probability under p x substantially dierent from...
Automatic shaping and decomposition of reward functions (2007)
This paper investigates the problem of automatically learning how torestructure the reward function of a Markov decision process so as tospeed up reinforcement learning. We begin by describing a...
Automatic shaping and decomposition of reward functions (2007)
This paper investigates the problem of automatically learning how torestructure the reward function of a Markov decision process so as tospeed up reinforcement learning. We begin by describing a...
Bhaskara Marthi, Stuart J. Russell, Jason Wolfe, All Rights Reserved, Bhaskara Marthi, Leslie Kaelbling, ...
personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the...
This paper investigates the problem of automatically learning how to restructure the reward function of a Markov decision process so as to speed up reinforcement learning. We begin by describing a...
Towards robustness in query auditing (2006)
Shubha U. Nabar, Bhaskara Marthi, Krishnaram Kenthapadi, Nina Mishra, Rajeev Motwani
We consider the online query auditing problem for statistical databases. Given a stream of aggregate queries posed over sensitive data, when should queries be denied in order to protect the privacy...
Towards robustness in query auditing (2006)
Shubha U. Nabar, Bhaskara Marthi, Krishnaram Kenthapadi, Nina Mishra, Rajeev Motwani
We consider the online query auditing problem for statistical databases. Given a stream of aggregate queries posed over sensitive data, when should queries be denied in order to protect the privacy...
Towards robustness in query auditing (2006)
Shubha U. Nabar, Bhaskara Marthi, Krishnaram Kenthapadi, Nina Mishra, Rajeev Motwani
We consider the online query auditing problem for statistical databases. Given a stream of aggregate queries posed over sensitive data, when should queries be denied in order to protect the privacy...
A Compact, Hierarchically Optimal Q-function Decomposition (2006)
Bhaskara Marthi, Stuart Russell
Previous work in hierarchical reinforcement learning has faced a dilemma: either ignore the values of different possible exit states from a subroutine, thereby risking suboptimal behavior, or...
Towards robustness in query auditing (2006)
Shubha U. Nabar, Bhaskara Marthi, Krishnaram Kenthapadi, Nina Mishra, Rajeev Motwani
We consider the online query auditing problem for statistical databases. Given a stream of aggregate queries posed over sensitive data, when should queries be denied in order to protect the privacy...
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...
Blog: Relational modeling with unknown objects (2004)
Brian Milch, Bhaskara Marthi, Stuart Russell
In many real-world probabilistic reasoning problems, one of the questions we want to answer is: how many objects are out there? Examples of such problems range from multitarget tracking to extracting...
Segmenting Documents by Stylistic Character (2004)
Neil Graham, Graeme Hirst, Bhaskara Marthi
As part of a larger project to develop an aid for writers that would help to eliminate stylistic inconsistencies within a document, we experimented with neural networks to find the points in a text...
Blog: Relational modeling with unknown objects (2004)
Brian Milch, Bhaskara Marthi, Stuart Russell
In many real-world probabilistic reasoning problems, one of the questions we want to answer is: how many objects are out there? Examples of such problems range from multitarget tracking to extracting...
Identity uncertainty and citation matching (2003)
Hanna Pasula, Bhaskara Marthi, Brian Milch, Stuart Russell, Ilya Shpitser
Identity uncertainty is a pervasive problem in real-world data analysis. It arises whenever objects are not labeled with unique identifiers or when those identifiers may not be perceived perfectly....
First-Order Probabilistic Models for Information Extraction (2003)
Information extraction (IE) is the problem of constructing a knowledge base from a corpus of text documents. In this paper, we argue that firstorder probabilistic models (FOPMs) are a promising...
First-Order Probabilistic Models for Information Extraction (2003)
Information extraction (IE) is the problem of constructing a knowledge base from a corpus of text documents. In this paper, we argue that firstorder probabilistic models (FOPMs) are a promising...
Identity uncertainty and citation matching (2003)
Hanna Pasula, Bhaskara Marthi, Brian Milch, Stuart Russell, Ilya Shpitser
Identity uncertainty is a pervasive problem in real-world data analysis. It arises whenever objects are not labeled with unique identifiers or when those identifiers may not be perceived perfectly....
First-Order Probabilistic Models for Information Extraction (2003)
Bhaskara Marthi, Brian Milch, Stuart Russell
Information extraction (IE) is the problem of constructing a knowledge base from a corpus of text documents. In this paper, we argue that firstorder probabilistic models (FOPMs) are a promising...
Bhaskara Marthi, Hanna Pasula, Stuart Russell, Yuval Peres
Filtering---estimating the state of a partially observable Markov process from a sequence of observations---is one of the most widely studied problems in control theory, AI, and computational...