21,986 research outputs found
Cover Tree Bayesian Reinforcement Learning
This paper proposes an online tree-based Bayesian approach for reinforcement
learning. For inference, we employ a generalised context tree model. This
defines a distribution on multivariate Gaussian piecewise-linear models, which
can be updated in closed form. The tree structure itself is constructed using
the cover tree method, which remains efficient in high dimensional spaces. We
combine the model with Thompson sampling and approximate dynamic programming to
obtain effective exploration policies in unknown environments. The flexibility
and computational simplicity of the model render it suitable for many
reinforcement learning problems in continuous state spaces. We demonstrate this
in an experimental comparison with least squares policy iteration
Calibration of semi-analytic models of galaxy formation using Particle Swarm Optimization
We present a fast and accurate method to select an optimal set of parameters
in semi-analytic models of galaxy formation and evolution (SAMs). Our approach
compares the results of a model against a set of observables applying a
stochastic technique called Particle Swarm Optimization (PSO), a self-learning
algorithm for localizing regions of maximum likelihood in multidimensional
spaces that outperforms traditional sampling methods in terms of computational
cost. We apply the PSO technique to the SAG semi-analytic model combined with
merger trees extracted from a standard CDM N-body simulation. The
calibration is performed using a combination of observed galaxy properties as
constraints, including the local stellar mass function and the black hole to
bulge mass relation. We test the ability of the PSO algorithm to find the best
set of free parameters of the model by comparing the results with those
obtained using a MCMC exploration. Both methods find the same maximum
likelihood region, however the PSO method requires one order of magnitude less
evaluations. This new approach allows a fast estimation of the best-fitting
parameter set in multidimensional spaces, providing a practical tool to test
the consequences of including other astrophysical processes in SAMs.Comment: 11 pages, 4 figures, 1 table. Accepted for publication in ApJ.
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Approximated and User Steerable tSNE for Progressive Visual Analytics
Progressive Visual Analytics aims at improving the interactivity in existing
analytics techniques by means of visualization as well as interaction with
intermediate results. One key method for data analysis is dimensionality
reduction, for example, to produce 2D embeddings that can be visualized and
analyzed efficiently. t-Distributed Stochastic Neighbor Embedding (tSNE) is a
well-suited technique for the visualization of several high-dimensional data.
tSNE can create meaningful intermediate results but suffers from a slow
initialization that constrains its application in Progressive Visual Analytics.
We introduce a controllable tSNE approximation (A-tSNE), which trades off speed
and accuracy, to enable interactive data exploration. We offer real-time
visualization techniques, including a density-based solution and a Magic Lens
to inspect the degree of approximation. With this feedback, the user can decide
on local refinements and steer the approximation level during the analysis. We
demonstrate our technique with several datasets, in a real-world research
scenario and for the real-time analysis of high-dimensional streams to
illustrate its effectiveness for interactive data analysis
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