3,034 research outputs found
Gaussian process hyper-parameter estimation using parallel asymptotically independent Markov sampling
Gaussian process emulators of computationally expensive computer codes
provide fast statistical approximations to model physical processes. The
training of these surrogates depends on the set of design points chosen to run
the simulator. Due to computational cost, such training set is bound to be
limited and quantifying the resulting uncertainty in the hyper-parameters of
the emulator by uni-modal distributions is likely to induce bias. In order to
quantify this uncertainty, this paper proposes a computationally efficient
sampler based on an extension of Asymptotically Independent Markov Sampling, a
recently developed algorithm for Bayesian inference. Structural uncertainty of
the emulator is obtained as a by-product of the Bayesian treatment of the
hyper-parameters. Additionally, the user can choose to perform stochastic
optimisation to sample from a neighbourhood of the Maximum a Posteriori
estimate, even in the presence of multimodality. Model uncertainty is also
acknowledged through numerical stabilisation measures by including a nugget
term in the formulation of the probability model. The efficiency of the
proposed sampler is illustrated in examples where multi-modal distributions are
encountered. For the purpose of reproducibility, further development, and use
in other applications the code used to generate the examples is freely
available for download at https://github.com/agarbuno/paims_codesComment: Computational Statistics \& Data Analysis, Volume 103, November 201
Unconventional machine learning of genome-wide human cancer data
Recent advances in high-throughput genomic technologies coupled with
exponential increases in computer processing and memory have allowed us to
interrogate the complex aberrant molecular underpinnings of human disease from
a genome-wide perspective. While the deluge of genomic information is expected
to increase, a bottleneck in conventional high-performance computing is rapidly
approaching. Inspired in part by recent advances in physical quantum
processors, we evaluated several unconventional machine learning (ML)
strategies on actual human tumor data. Here we show for the first time the
efficacy of multiple annealing-based ML algorithms for classification of
high-dimensional, multi-omics human cancer data from the Cancer Genome Atlas.
To assess algorithm performance, we compared these classifiers to a variety of
standard ML methods. Our results indicate the feasibility of using
annealing-based ML to provide competitive classification of human cancer types
and associated molecular subtypes and superior performance with smaller
training datasets, thus providing compelling empirical evidence for the
potential future application of unconventional computing architectures in the
biomedical sciences
From Parameter Tuning to Dynamic Heuristic Selection
The importance of balance between exploration and exploitation plays a crucial role while solving combinatorial optimization problems. This balance is reached by two general techniques: by using an appropriate problem solver and by setting its proper parameters. Both problems were widely studied in the past and the research process continues up until now. The latest studies in the field of automated machine learning propose merging both problems, solving them at design time, and later strengthening the results at runtime. To the best of our knowledge, the generalized approach for solving the parameter setting problem in heuristic solvers has not yet been proposed. Therefore, the concept of merging heuristic selection and parameter control have not been introduced.
In this thesis, we propose an approach for generic parameter control in meta-heuristics by means of reinforcement learning (RL). Making a step further, we suggest a technique for merging the heuristic selection and parameter control problems and solving them at runtime using RL-based hyper-heuristic. The evaluation of the proposed parameter control technique on a symmetric traveling salesman problem (TSP) revealed its applicability by reaching the performance of tuned in online and used in isolation underlying meta-heuristic. Our approach provides the results on par with the best underlying heuristics with tuned parameters.:1 Introduction 1
1.1 Motivation 1
1.2 Research objective 2
1.3 Solution overview 2
2 Background and RelatedWork Analysis 3
2.1 Optimization Problems and their Solvers 3
2.2 Heuristic Solvers for Optimization Problems 9
2.3 Setting Algorithm Parameters 19
2.4 Combined Algorithm Selection and Hyper-Parameter Tuning Problem 27
2.5 Conclusion on Background and Related Work Analysis 28
3 Online Selection Hyper-Heuristic with Generic Parameter Control 31
3.1 Combined Parameter Control and Algorithm Selection Problem 31
3.2 Search Space Structure 32
3.3 Parameter Prediction Process 34
3.4 Low-Level Heuristics 35
3.5 Conclusion of Concept 36
4 Implementation Details 37
4.2 Search Space 40
4.3 Prediction Process 43
4.4 Low Level Heuristics 48
4.5 Conclusion 52
5 Evaluation 55
5.1 Optimization Problem 55
5.2 Environment Setup 56
5.3 Meta-heuristics Tuning 56
5.4 Concept Evaluation 60
5.5 Analysis of HH-PC Settings 74
5.6 Conclusion 79
6 Conclusion 81
7 FutureWork 83
7.1 Prediction Process 83
7.2 Search Space 84
7.3 Evaluations and Benchmarks 84
Bibliography 87
A Evaluation Results 99
A.1 Results in Figures 99
A.2 Results in numbers 10
Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes
We apply numerical methods in combination with finite-difference-time-domain
(FDTD) simulations to optimize transmission properties of plasmonic mirror
color filters using a multi-objective figure of merit over a five-dimensional
parameter space by utilizing novel multi-fidelity Gaussian processes approach.
We compare these results with conventional derivative-free global search
algorithms, such as (single-fidelity) Gaussian Processes optimization scheme,
and Particle Swarm Optimization---a commonly used method in nanophotonics
community, which is implemented in Lumerical commercial photonics software. We
demonstrate the performance of various numerical optimization approaches on
several pre-collected real-world datasets and show that by properly trading off
expensive information sources with cheap simulations, one can more effectively
optimize the transmission properties with a fixed budget.Comment: NIPS 2018 Workshop on Machine Learning for Molecules and Materials.
arXiv admin note: substantial text overlap with arXiv:1811.0075
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