3,822 research outputs found
Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic Likelihood-Free Inference
In likelihood-free settings where likelihood evaluations are intractable,
approximate Bayesian computation (ABC) addresses the formidable inference task
to discover plausible parameters of simulation programs that explain the
observations. However, they demand large quantities of simulation calls.
Critically, hyperparameters that determine measures of simulation discrepancy
crucially balance inference accuracy and sample efficiency, yet are difficult
to tune. In this paper, we present kernel embedding likelihood-free inference
(KELFI), a holistic framework that automatically learns model hyperparameters
to improve inference accuracy given limited simulation budget. By leveraging
likelihood smoothness with conditional mean embeddings, we nonparametrically
approximate likelihoods and posteriors as surrogate densities and sample from
closed-form posterior mean embeddings, whose hyperparameters are learned under
its approximate marginal likelihood. Our modular framework demonstrates
improved accuracy and efficiency on challenging inference problems in ecology.Comment: To appear in the Proceedings of the 22nd International Conference on
Artificial Intelligence and Statistics (AISTATS) 2019, Naha, Okinawa, Japa
Transductive Learning for Multi-Task Copula Processes
We tackle the problem of multi-task learning with copula process.
Multivariable prediction in spatial and spatial-temporal processes such as
natural resource estimation and pollution monitoring have been typically
addressed using techniques based on Gaussian processes and co-Kriging. While
the Gaussian prior assumption is convenient from analytical and computational
perspectives, nature is dominated by non-Gaussian likelihoods. Copula processes
are an elegant and flexible solution to handle various non-Gaussian likelihoods
by capturing the dependence structure of random variables with cumulative
distribution functions rather than their marginals. We show how multi-task
learning for copula processes can be used to improve multivariable prediction
for problems where the simple Gaussianity prior assumption does not hold. Then,
we present a transductive approximation for multi-task learning and derive
analytical expressions for the copula process model. The approach is evaluated
and compared to other techniques in one artificial dataset and two publicly
available datasets for natural resource estimation and concrete slump
prediction
Directional grid maps: modeling multimodal angular uncertainty in dynamic environments
Robots often have to deal with the challenges of operating in dynamic and
sometimes unpredictable environments. Although an occupancy map of the
environment is sufficient for navigation of a mobile robot or manipulation
tasks with a robotic arm in static environments, robots operating in dynamic
environments demand richer information to improve robustness, efficiency, and
safety. For instance, in path planning, it is important to know the direction
of motion of dynamic objects at various locations of the environment for safer
navigation or human-robot interaction. In this paper, we introduce directional
statistics into robotic mapping to model circular data. Primarily, in
collateral to occupancy grid maps, we propose directional grid maps to
represent the location-wide long-term angular motion of the environment. Being
highly representative, this defines a probability measure-field over the
longitude-latitude space rather than a scalar-field or a vector field. Withal,
we further demonstrate how the same theory can be used to model angular
variations in the spatial domain, temporal domain, and spatiotemporal domain.
We carried out a series of experiments to validate the proposed models using a
variety of robots having different sensors such as RGB cameras and LiDARs on
simulated and real-world settings in both indoor and outdoor environments.Comment: To appear in the proceedings of the IEEE/RSJ International Conference
on Intelligent Robots and Systems (IROS) 201
Index Set Fourier Series Features for Approximating Multi-dimensional Periodic Kernels
Periodicity is often studied in timeseries modelling with autoregressive
methods but is less popular in the kernel literature, particularly for higher
dimensional problems such as in textures, crystallography, and quantum
mechanics. Large datasets often make modelling periodicity untenable for
otherwise powerful non-parametric methods like Gaussian Processes (GPs) which
typically incur an computational burden and, consequently,
are unable to scale to larger datasets. To this end we introduce a method
termed \emph{Index Set Fourier Series Features} to tractably exploit
multivariate Fourier series and efficiently decompose periodic kernels on
higher-dimensional data into a series of basis functions. We show that our
approximation produces significantly less predictive error than alternative
approaches such as those based on random Fourier features and achieves better
generalisation on regression problems with periodic data
Invariant measures for the 3D Navier-Stokes-Voigt equations and their Navier-Stokes limit
The Navier-Stokes-Voigt model of viscoelastic incompressible fluid has been
recently proposed as a regularization of the three-dimensional Navier-Stokes
equations for the purpose of direct numerical simulations. Besides the
kinematic viscosity parameter, , this model possesses a regularizing
parameter, , a given length scale parameter, so that
is the relaxation time of the viscoelastic fluid. In
this work, we derive several statistical properties of the invariant measures
associated with the solutions of the three-dimensional Navier-Stokes-Voigt
equations. Moreover, we prove that, for fixed viscosity, , as the
regularizing parameter tends to zero, there exists a subsequence of
probability invariant measures converging, in a suitable sense, to a strong
stationary statistical solution of the three-dimensional Navier-Stokes
equations, which is a regularized version of the notion of stationary
statistical solutions - a generalization of the concept of invariant measure
introduced and investigated by Foias. This fact supports earlier numerical
observations, and provides an additional evidence that, for small values of the
regularization parameter , the Navier-Stokes-Voigt model can indeed be
considered as a model to study the statistical properties of the
three-dimensional Navier-Stokes equations and turbulent flows via direct
numerical simulations
Quantum bit commitment protocol without quantum memory
Quantum protocols for bit commitment have been proposed and it is largely
accepted that unconditionally secure quantum bit commitment is not possible;
however, it can be more secure than classical bit commitment. In despite of its
usefulness, quantum bit commitment protocols have not been experimentally
implemented. The main reason is the fact that all proposed quantum bit
commitment protocols require quantum memory. In this work, we show a quantum
bit commitment protocol that does not require quantum memory and can be
implemented with present technology.Comment: 12 pages, 2 figure
Stochastic Functional Gradient Path Planning in Occupancy Maps
Planning safe paths is a major building block in robot autonomy. It has been
an active field of research for several decades, with a plethora of planning
methods. Planners can be generally categorised as either trajectory optimisers
or sampling-based planners. The latter is the predominant planning paradigm for
occupancy maps. Trajectory optimisation entails major algorithmic changes to
tackle contextual information gaps caused by incomplete sensor coverage of the
map. However, the benefits are substantial, as trajectory optimisers can reason
on the trade-off between path safety and efficiency.
In this work, we improve our previous work on stochastic functional gradient
planners. We introduce a novel expressive path representation based on kernel
approximation, that allows cost effective model updates based on stochastic
samples. The main drawback of the previous stochastic functional gradient
planner was the cubic cost, stemming from its non-parametric path
representation. Our novel approximate kernel based model, on the other hand,
has a fixed linear cost that depends solely on the number of features used to
represent the path. We show that the stochasticity of the samples is crucial
for the planner and present comparisons to other state-of-the-art planning
methods in both simulation and with real occupancy data. The experiments
demonstrate the advantages of the stochastic approximate kernel method for path
planning in occupancy maps
Hybrid Parallel Quantum Key Distribution Protocol
The practical realizations of BB84 quantum key distribution protocol using
single-photon or weak coherent states have normally presented low efficiency,
in the meaning that most bits sent by Alice are not useful for the final key.
In this work, we show an optical setup to improve the transmission rate of
useful bits putting together two ideas, parallel quantum key distribution and
physical encryption using mesoscopic coherent states. The final result is a
four time faster quantum key distribution setup.Comment: 16 pages, 4 figure
Learning to Navigate by Growing Deep Networks
Adaptability is central to autonomy. Intuitively, for high-dimensional
learning problems such as navigating based on vision, internal models with
higher complexity allow to accurately encode the information available.
However, most learning methods rely on models with a fixed structure and
complexity. In this paper, we present a self-supervised framework for robots to
learn to navigate, without any prior knowledge of the environment, by
incrementally building the structure of a deep network as new data becomes
available. Our framework captures images from a monocular camera and self
labels the images to continuously train and predict actions from a
computationally efficient adaptive deep architecture based on Autoencoders
(AE), in a self-supervised fashion. The deep architecture, named Reinforced
Adaptive Denoising Autoencoders (RA-DAE), uses reinforcement learning to
dynamically change the network structure by adding or removing neurons.
Experiments were conducted in simulation and real-world indoor and outdoor
environments to assess the potential of self-supervised navigation. RA-DAE
demonstrates better performance than equivalent non-adaptive deep learning
alternatives and can continue to expand its knowledge, trading-off past and
present information.Comment: 10 pages, Australasian Conference on Robotics and Automation, 201
Learning Non-Stationary Space-Time Models for Environmental Monitoring
One of the primary aspects of sustainable development involves accurate
understanding and modeling of environmental phenomena. Many of these phenomena
exhibit variations in both space and time and it is imperative to develop a
deeper understanding of techniques that can model space-time dynamics
accurately. In this paper we propose NOSTILL-GP - NOn-stationary Space TIme
variable Latent Length scale GP, a generic non-stationary, spatio-temporal
Gaussian Process (GP) model. We present several strategies, for efficient
training of our model, necessary for real-world applicability. Extensive
empirical validation is performed using three real-world environmental
monitoring datasets, with diverse dynamics across space and time. Results from
the experiments clearly demonstrate general applicability and effectiveness of
our approach for applications in environmental monitoring.Comment: AAAI-1
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