Geoffrey Gordon

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Carnegie Mellon University

Associate Department Head for Education and Professor, Machine Learning Department at Carnegie Mellon University

Graph Adversarial Networks: Protecting Information against Adversarial Attacks

We study the problem of protecting information when learning with graph ...
09/28/2020 ∙ by Peiyuan Liao, et al. ∙ 20 ∙ share

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Towards modular and programmable architecture search

Neural architecture search methods are able to find high performance dee...
09/30/2019 ∙ by Renato Negrinho, et al. ∙ 0 ∙ share

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Recurrent Predictive State Policy Networks

We introduce Recurrent Predictive State Policy (RPSP) networks, a recurr...
03/05/2018 ∙ by Ahmed Hefny, et al. ∙ 0 ∙ share

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Learning General Latent-Variable Graphical Models with Predictive Belief Propagation and Hilbert Space Embeddings

In this paper, we propose a new algorithm for learning general latent-va...
12/06/2017 ∙ by Borui Wang, et al. ∙ 0 ∙ share

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Predictive State Recurrent Neural Networks

We present a new model, Predictive State Recurrent Neural Networks (PSRN...
05/25/2017 ∙ by Carlton Downey, et al. ∙ 0 ∙ share

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Practical Learning of Predictive State Representations

Over the past decade there has been considerable interest in spectral al...
02/14/2017 ∙ by Carlton Downey, et al. ∙ 0 ∙ share

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Supervised Learning for Dynamical System Learning

Recently there has been substantial interest in spectral methods for lea...
05/20/2015 ∙ by Ahmed Hefny, et al. ∙ 0 ∙ share

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Hilbert Space Embeddings of Predictive State Representations

Predictive State Representations (PSRs) are an expressive class of model...
09/26/2013 ∙ by Byron Boots, et al. ∙ 0 ∙ share

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Distributed Planning in Hierarchical Factored MDPs

We present a principled and efficient planning algorithm for collaborati...
12/12/2012 ∙ by Carlos E. Guestrin, et al. ∙ 0 ∙ share

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Decentralized Sensor Fusion With Distributed Particle Filters

This paper presents a scalable Bayesian technique for decentralized stat...
10/19/2012 ∙ by Matthew Rosencrantz, et al. ∙ 0 ∙ share

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Policy-contingent abstraction for robust robot control

This paper presents a scalable control algorithm that enables a deployed...
10/19/2012 ∙ by Joelle Pineau, et al. ∙ 0 ∙ share

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First-Order Mixed Integer Linear Programming

Mixed integer linear programming (MILP) is a powerful representation oft...
05/09/2012 ∙ by Geoffrey Gordon, et al. ∙ 0 ∙ share

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A Bayesian Matrix Factorization Model for Relational Data

Relational learning can be used to augment one data source with other co...
03/15/2012 ∙ by Ajit P. Singh, et al. ∙ 0 ∙ share

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Geoffrey Gordon

Featured Co-authors

Ruslan Salakhutdinov

Joelle Pineau

Byron Boots

Arthur Gretton

Stefanie Jegelka

Tommi Jaakkola

Han Zhao

Wen Sun

Siddhartha Srinivasa

Miroslav Dudík

Sebastian Thrun

Graph Adversarial Networks: Protecting Information against Adversarial Attacks

Towards modular and programmable architecture search

Recurrent Predictive State Policy Networks

Learning General Latent-Variable Graphical Models with Predictive Belief Propagation and Hilbert Space Embeddings

Predictive State Recurrent Neural Networks

Practical Learning of Predictive State Representations

Supervised Learning for Dynamical System Learning

Hilbert Space Embeddings of Predictive State Representations

Distributed Planning in Hierarchical Factored MDPs

Decentralized Sensor Fusion With Distributed Particle Filters

Policy-contingent abstraction for robust robot control

First-Order Mixed Integer Linear Programming

A Bayesian Matrix Factorization Model for Relational Data