9,139 research outputs found
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
We present a tutorial on Bayesian optimization, a method of finding the
maximum of expensive cost functions. Bayesian optimization employs the Bayesian
technique of setting a prior over the objective function and combining it with
evidence to get a posterior function. This permits a utility-based selection of
the next observation to make on the objective function, which must take into
account both exploration (sampling from areas of high uncertainty) and
exploitation (sampling areas likely to offer improvement over the current best
observation). We also present two detailed extensions of Bayesian optimization,
with experiments---active user modelling with preferences, and hierarchical
reinforcement learning---and a discussion of the pros and cons of Bayesian
optimization based on our experiences
NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation
Complex computational models are often designed to simulate real-world
physical phenomena in many scientific disciplines. However, these simulation
models tend to be computationally very expensive and involve a large number of
simulation input parameters which need to be analyzed and properly calibrated
before the models can be applied for real scientific studies. We propose a
visual analysis system to facilitate interactive exploratory analysis of
high-dimensional input parameter space for a complex yeast cell polarization
simulation. The proposed system can assist the computational biologists, who
designed the simulation model, to visually calibrate the input parameters by
modifying the parameter values and immediately visualizing the predicted
simulation outcome without having the need to run the original expensive
simulation for every instance. Our proposed visual analysis system is driven by
a trained neural network-based surrogate model as the backend analysis
framework. Surrogate models are widely used in the field of simulation sciences
to efficiently analyze computationally expensive simulation models. In this
work, we demonstrate the advantage of using neural networks as surrogate models
for visual analysis by incorporating some of the recent advances in the field
of uncertainty quantification, interpretability and explainability of neural
network-based models. We utilize the trained network to perform interactive
parameter sensitivity analysis of the original simulation at multiple
levels-of-detail as well as recommend optimal parameter configurations using
the activation maximization framework of neural networks. We also facilitate
detail analysis of the trained network to extract useful insights about the
simulation model, learned by the network, during the training process.Comment: Published at IEEE Transactions on Visualization and Computer Graphic
Interpretable statistics for complex modelling: quantile and topological learning
As the complexity of our data increased exponentially in the last decades, so has our
need for interpretable features. This thesis revolves around two paradigms to approach
this quest for insights.
In the first part we focus on parametric models, where the problem of interpretability
can be seen as a “parametrization selection”. We introduce a quantile-centric
parametrization and we show the advantages of our proposal in the context of regression,
where it allows to bridge the gap between classical generalized linear (mixed)
models and increasingly popular quantile methods.
The second part of the thesis, concerned with topological learning, tackles the
problem from a non-parametric perspective. As topology can be thought of as a way
of characterizing data in terms of their connectivity structure, it allows to represent
complex and possibly high dimensional through few features, such as the number of
connected components, loops and voids. We illustrate how the emerging branch of
statistics devoted to recovering topological structures in the data, Topological Data
Analysis, can be exploited both for exploratory and inferential purposes with a special
emphasis on kernels that preserve the topological information in the data.
Finally, we show with an application how these two approaches can borrow strength
from one another in the identification and description of brain activity through fMRI
data from the ABIDE project
Improving Automated Driving through Planning with Human Internal States
This work examines the hypothesis that partially observable Markov decision
process (POMDP) planning with human driver internal states can significantly
improve both safety and efficiency in autonomous freeway driving. We evaluate
this hypothesis in a simulated scenario where an autonomous car must safely
perform three lane changes in rapid succession. Approximate POMDP solutions are
obtained through the partially observable Monte Carlo planning with observation
widening (POMCPOW) algorithm. This approach outperforms over-confident and
conservative MDP baselines and matches or outperforms QMDP. Relative to the MDP
baselines, POMCPOW typically cuts the rate of unsafe situations in half or
increases the success rate by 50%.Comment: Preprint before submission to IEEE Transactions on Intelligent
Transportation Systems. arXiv admin note: text overlap with arXiv:1702.0085
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