[auton-users] Fwd: Thesis Oral: Sajid M. Siddiqi

[Sajid Siddiqi]

My thesis defense is on Thursday at noon in the room around the corner.
Come cheer me on / throw rotten eggs, as required :) I hear that there
will be food.

Sajid

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Date: 15 October 2009
Time: 12:00 p.m.
Place: Newell Simon Hall 3305
Type: Thesis Oral
Who: Sajid M. Siddiqi
Topic: Learning Latent Variable and Predictive Models of Dynamical Systems

Abstract:

A variety of learning problems in robotics, computer vision and other
areas of artificial intelligence can be construed as problems of
learning statistical models for dynamical systems from sequential
observations. Good dynamical system models allow us to represent and
predict observations in these systems, which in turn enables
applications such as classification, planning, control, simulation,
anomaly detection and forecasting. One class of dynamical system models
assumes the existence of an underlying hidden random variable that
evolves over time and emits the observations we see. Past observations
are summarized into the belief distribution over this random variable,
which represents the state of the system. This assumption leads to
`latent variable models' which are used heavily in practice. However,
learning algorithms for these models still face a variety of issues such
as model selection, local optima and instability. The representational
ability of these models also differs significantly based on whether the
underlying latent variable is assumed to be discrete as in Hidden Markov
Models (HMMs), or real-valued as in Linear Dynamical Systems (LDSs).
Another recently introduced class of models represents state as a set of
predictions about future observations rather than as a latent variable
summarizing the past. These `predictive models', such as Predictive
State Representations (PSRs), are provably more powerful than latent
variable models and hold the promise of allowing more accurate,
efficient learning algorithms since no hidden quantities are involved.
However, this promise has not been realized.

In this thesis we propose novel learning algorithms that address the
issues of model selection, local minima and instability in learning
latent variable models. We show that certain 'predictive' latent
variable model learning methods bridge the gap between latent variable
and predictive models. We also propose a novel latent variable model,
the Reduced-Rank HMM (RR-HMM), that combines desirable properties of
discrete and real-valued latent-variable models. We show that
reparameterizing the class of RR-HMMs  yields a subset of PSRs, and
propose an asymptotically unbiased predictive learning algorithm for
RR-HMMs and PSRs along with finite-sample error bounds for the RR-HMM
case. In terms of efficiency and accuracy, our methods outperform
alternatives on dynamic texture videos, mobile robot visual sensing
data, and other domains.


Thesis Committee Members:
Geoffrey J. Gordon, Chair
Andrew Moore
Jeff Schneider
Zoubin Ghahramani, University of Cambridge
David Wingate, Massachusetts Institute of Technology

A copy of the thesis document is available at:
http://www.cs.cmu.edu/~siddiqi/docs/sajid_thesis_draft.pdf