Avi Pfeffer

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Charles River Analytics Inc.

Chief Scientist at Charles River Analytics

Learning Probabilistic Programs Using Backpropagation

Probabilistic modeling enables combining domain knowledge with learning ...
05/15/2017 ∙ by Avi Pfeffer, et al. ∙ 0 ∙ share

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Artificial Intelligence Based Malware Analysis

Artificial intelligence methods have often been applied to perform speci...
04/27/2017 ∙ by Avi Pfeffer, et al. ∙ 0 ∙ share

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Structured Factored Inference: A Framework for Automated Reasoning in Probabilistic Programming Languages

Reasoning on large and complex real-world models is a computationally di...
06/10/2016 ∙ by Avi Pfeffer, et al. ∙ 0 ∙ share

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Lazy Factored Inference for Functional Probabilistic Programming

Probabilistic programming provides the means to represent and reason abo...
09/11/2015 ∙ by Avi Pfeffer, et al. ∙ 0 ∙ share

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Decision-Making with Complex Data Structures using Probabilistic Programming

Existing decision-theoretic reasoning frameworks such as decision networ...
07/11/2014 ∙ by Brian E. Ruttenberg, et al. ∙ 0 ∙ share

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Networks of Influence Diagrams: A Formalism for Representing Agents' Beliefs and Decision-Making Processes

This paper presents Networks of Influence Diagrams (NID), a compact, nat...
01/15/2014 ∙ by Yaakov Gal, et al. ∙ 0 ∙ share

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Object-Oriented Bayesian Networks

Bayesian networks provide a modeling language and associated inference a...
02/06/2013 ∙ by Daphne Koller, et al. ∙ 0 ∙ share

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SPOOK: A System for Probabilistic Object-Oriented Knowledge Representation

In previous work, we pointed out the limitations of standard Bayesian ne...
01/23/2013 ∙ by Avi Pfeffer, et al. ∙ 0 ∙ share

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Sufficiency, Separability and Temporal Probabilistic Models

Suppose we are given the conditional probability of one variable given s...
01/10/2013 ∙ by Avi Pfeffer, et al. ∙ 0 ∙ share

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Factored Particles for Scalable Monitoring

Exact monitoring in dynamic Bayesian networks is intractable, so approxi...
12/12/2012 ∙ by Brenda Ng, et al. ∙ 0 ∙ share

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Loopy Belief Propagation as a Basis for Communication in Sensor Networks

Sensor networks are an exciting new kind of computer system. Consisting ...
10/19/2012 ∙ by Christopher Crick, et al. ∙ 0 ∙ share

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Asynchronous Dynamic Bayesian Networks

Systems such as sensor networks and teams of autonomous robots consist o...
07/04/2012 ∙ by Avi Pfeffer, et al. ∙ 0 ∙ share

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Approximate Separability for Weak Interaction in Dynamic Systems

One approach to monitoring a dynamic system relies on decomposition of t...
06/27/2012 ∙ by Avi Pfeffer, et al. ∙ 0 ∙ share

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Learning and Solving Many-Player Games through a Cluster-Based Representation

In addressing the challenge of exponential scaling with the number of ag...
06/13/2012 ∙ by Sevan G. Ficici, et al. ∙ 0 ∙ share

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Identifying reasoning patterns in games

We present an algorithm that identifies the reasoning patterns of agents...
06/13/2012 ∙ by Dimitrios Antos, et al. ∙ 0 ∙ share

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Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence (2011)

This is the Proceedings of the Twenty-Seventh Conference on Uncertainty ...
05/11/2012 ∙ by Fabio Cozman, et al. ∙ 0 ∙ share

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Temporal Action-Graph Games: A New Representation for Dynamic Games

In this paper we introduce temporal action graph games (TAGGs), a novel ...
05/09/2012 ∙ by Albert Xin Jiang, et al. ∙ 0 ∙ share

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Learning Game Representations from Data Using Rationality Constraints

While game theory is widely used to model strategic interactions, a natu...
03/15/2012 ∙ by Xi Alice Gao, et al. ∙ 0 ∙ share

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Avi Pfeffer

Featured Co-authors

Daphne Koller

David C. Parkes

Kevin Leyton-Brown

William Kretschmer

Leonid Peshkin

Brian Ruttenberg

Jason Taylor

Craig Miles

Glenn Takata

Dan Scofield

Fabio Cozman

Learning Probabilistic Programs Using Backpropagation

Artificial Intelligence Based Malware Analysis

Structured Factored Inference: A Framework for Automated Reasoning in Probabilistic Programming Languages

Lazy Factored Inference for Functional Probabilistic Programming

Decision-Making with Complex Data Structures using Probabilistic Programming

Networks of Influence Diagrams: A Formalism for Representing Agents' Beliefs and Decision-Making Processes

Object-Oriented Bayesian Networks

SPOOK: A System for Probabilistic Object-Oriented Knowledge Representation

Sufficiency, Separability and Temporal Probabilistic Models

Factored Particles for Scalable Monitoring

Loopy Belief Propagation as a Basis for Communication in Sensor Networks

Asynchronous Dynamic Bayesian Networks

Approximate Separability for Weak Interaction in Dynamic Systems

Learning and Solving Many-Player Games through a Cluster-Based Representation

Identifying reasoning patterns in games

Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence (2011)

Temporal Action-Graph Games: A New Representation for Dynamic Games

Learning Game Representations from Data Using Rationality Constraints