7,547 research outputs found
Kernels for sequentially ordered data
We present a novel framework for learning with sequential data of any kind, such as multivariate time series, strings, or sequences of graphs. The main result is a ”sequentialization”
that transforms any kernel on a given domain into a kernel for sequences in that domain.
This procedure preserves properties such as positive definiteness, the associated kernel feature map is an ordered variant of sample (cross-)moments, and this sequentialized kernel
is consistent in the sense that it converges to a kernel for paths if sequences converge to
paths (by discretization). Further, classical kernels for sequences arise as special cases of
this method. We use dynamic programming and low-rank techniques for tensors to provide
efficient algorithms to compute this sequentialized kernel
The Signature Kernel is the solution of a Goursat PDE
Recently, there has been an increased interest in the development of kernel
methods for learning with sequential data. The signature kernel is a learning
tool with potential to handle irregularly sampled, multivariate time series. In
"Kernels for sequentially ordered data" the authors introduced a kernel trick
for the truncated version of this kernel avoiding the exponential complexity
that would have been involved in a direct computation. Here we show that for
continuously differentiable paths, the signature kernel solves a hyperbolic PDE
and recognize the connection with a well known class of differential equations
known in the literature as Goursat problems. This Goursat PDE only depends on
the increments of the input sequences, does not require the explicit
computation of signatures and can be solved efficiently using
state-of-the-arthyperbolic PDE numerical solvers, giving a kernel trick for the
untruncated signature kernel, with the same raw complexity as the method from
"Kernels for sequentially ordered data", but with the advantage that the PDE
numerical scheme is well suited for GPU parallelization, which effectively
reduces the complexity by a full order of magnitude in the length of the input
sequences. In addition, we extend the previous analysis to the space of
geometric rough paths and establish, using classical results from rough path
theory, that the rough version of the signature kernel solves a rough integral
equation analogous to the aforementioned Goursat PDE. Finally, we empirically
demonstrate the effectiveness of our PDE kernel as a machine learning tool in
various machine learning applications dealing with sequential data. We release
the library sigkernel publicly available at
https://github.com/crispitagorico/sigkernel
Run Time Approximation of Non-blocking Service Rates for Streaming Systems
Stream processing is a compute paradigm that promises safe and efficient
parallelism. Modern big-data problems are often well suited for stream
processing's throughput-oriented nature. Realization of efficient stream
processing requires monitoring and optimization of multiple communications
links. Most techniques to optimize these links use queueing network models or
network flow models, which require some idea of the actual execution rate of
each independent compute kernel within the system. What we want to know is how
fast can each kernel process data independent of other communicating kernels.
This is known as the "service rate" of the kernel within the queueing
literature. Current approaches to divining service rates are static. Modern
workloads, however, are often dynamic. Shared cloud systems also present
applications with highly dynamic execution environments (multiple users,
hardware migration, etc.). It is therefore desirable to continuously re-tune an
application during run time (online) in response to changing conditions. Our
approach enables online service rate monitoring under most conditions,
obviating the need for reliance on steady state predictions for what are
probably non-steady state phenomena. First, some of the difficulties associated
with online service rate determination are examined. Second, the algorithm to
approximate the online non-blocking service rate is described. Lastly, the
algorithm is implemented within the open source RaftLib framework for
validation using a simple microbenchmark as well as two full streaming
applications.Comment: technical repor
Learning to Race through Coordinate Descent Bayesian Optimisation
In the automation of many kinds of processes, the observable outcome can
often be described as the combined effect of an entire sequence of actions, or
controls, applied throughout its execution. In these cases, strategies to
optimise control policies for individual stages of the process might not be
applicable, and instead the whole policy might have to be optimised at once. On
the other hand, the cost to evaluate the policy's performance might also be
high, being desirable that a solution can be found with as few interactions as
possible with the real system. We consider the problem of optimising control
policies to allow a robot to complete a given race track within a minimum
amount of time. We assume that the robot has no prior information about the
track or its own dynamical model, just an initial valid driving example.
Localisation is only applied to monitor the robot and to provide an indication
of its position along the track's centre axis. We propose a method for finding
a policy that minimises the time per lap while keeping the vehicle on the track
using a Bayesian optimisation (BO) approach over a reproducing kernel Hilbert
space. We apply an algorithm to search more efficiently over high-dimensional
policy-parameter spaces with BO, by iterating over each dimension individually,
in a sequential coordinate descent-like scheme. Experiments demonstrate the
performance of the algorithm against other methods in a simulated car racing
environment.Comment: Accepted as conference paper for the 2018 IEEE International
Conference on Robotics and Automation (ICRA
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