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- Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon
- Robotics: Science and Systems
- 2010

A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of our environment,â€¦ (More)

Hidden Markov Models (HMMs) are important tools for modeling sequence data. However, they are restricted to discrete latent states, and are largely restricted to Gaussian and discrete observations.â€¦ (More)

- Sajid M. Siddiqi, Byron Boots, Geoffrey J. Gordon
- AISTATS
- 2010

We introduce the Reduced-Rank Hidden Markov Model (RR-HMM), a generalization of HMMs that can model smooth state evolution as in Linear Dynamical Systems (LDSs) as well as non-log-concave predictiveâ€¦ (More)

- Sajid M. Siddiqi, Byron Boots, Geoffrey J. Gordon
- NIPS
- 2007

Stability is a desirable characteristic for linear dynamical systems, but it is often ignored by algorithms that learn these systems from data. We propose a novel method for learning stable linearâ€¦ (More)

- Byron Boots, Geoffrey J. Gordon, Arthur Gretton
- UAI
- 2013

Predictive State Representations (PSRs) are an expressive class of models for controlled stochastic processes. PSRs represent state as a set of predictions of future observable events. Because PSRsâ€¦ (More)

- Byron Boots, Geoffrey J. Gordon
- AAAI
- 2011

Recently, a number of researchers have proposed spectral algorithms for learning models of dynamical systemsâ€”for example, Hidden Markov Models (HMMs), Partially Observable Markov Decision Processesâ€¦ (More)

- Byron Boots, Geoffrey J. Gordon
- ICML
- 2012

Recently, there has been much interest in spectral approaches to learning manifoldsâ€” so-called kernel eigenmap methods. These methods have had some successes, but their applicability is limitedâ€¦ (More)

- Byron Boots, Geoffrey J. Gordon
- NIPS
- 2010

We propose a new approach to value function approximation which combines linear temporal difference reinforcement learning with subspace identification. In practical applications, reinforcementâ€¦ (More)

- Zita Marinho, Byron Boots, Anca D. Dragan, Arunkumar Byravan, Geoffrey J. Gordon, Siddhartha S. Srinivasa
- Robotics: Science and Systems
- 2016

We introduce a functional gradient descent trajectory optimization algorithm for robot motion planning in Reproducing Kernel Hilbert Spaces (RKHSs). Functional gradient algorithms are a popularâ€¦ (More)

- Stephen M. Majercik, Byron Boots
- AAAI
- 2005

We present DC-SSAT, a sound and complete divide-andconquer algorithm for solving stochastic satisfiability (SSAT) problems that outperforms the best existing algorithm for solving such problemsâ€¦ (More)