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Robust and Efficient Kernel Hyperparameter Paths with Guarantees
2014
International Conference on Machine Learning
Preserved Fulltext
Here we address this problem by devising a robust and efficient path tracking algorithm that can also handle kernel hyperparameter paths. The algorithm has asymptotically optimal complexity. ...
kernel hyperparameter path. ...
The kernel hyperparameter path for the IONOSPHERE data set with fixed bias updates (top, left) and dynamic bias updates (top, right), and the kernel hyperparameter path for the A1A data set with fixed ...
dblp:conf/icml/GiesenLW14
fatcat:3we3mx6ll5g5pblwwpgllr4x7m
Robust Policy Search for Robot Navigation with Stochastic Meta-Policies
[article]
2020
arXiv
pre-print
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Then, to deal with mismodeling errors and improve exploration we use stochastic meta-policies for query selection and an adaptive kernel. ...
We compare the proposed algorithm with previous results in several optimization benchmarks and robot tasks, such as pushing objects with a robot arm, or path finding with a rover. ...
For the remainder of the paper we consider a GP with zero mean and kernel k : X × X → R with hyperparameters θ. ...
arXiv:2003.01000v1
fatcat:o7gs2bdenfaxlpja4rulbtuhee
Uncertainty-driven view planning for underwater inspection
2012
2012 IEEE International Conference on Robotics and Automation
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We discuss the problem of inspecting an underwater structure, such as a submerged ship hull, with an autonomous underwater vehicle (AUV). ...
Uncertainty modeling provides novel cost functions for planning the path of the AUV to minimize a metric of inspection performance. ...
The authors gratefully acknowledge Jonathan Binney, Jnaneshwar Das, Arvind Pereira, and Hordur Heidarsson at the University of Southern California for their insightful comments. ...
doi:10.1109/icra.2012.6224726
dblp:conf/icra/HollingerEHMS12
fatcat:4fgujww4enarhmoprcqsgxa6qi
Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties
[article]
2020
arXiv
pre-print
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Robots operating in real world settings must navigate and maintain safety while interacting with many heterogeneous agents and obstacles. ...
We verify via simulation results that the nominal multi-agent CBF is often violated during agent interactions, whereas our robust formulation maintains safety with a much higher probability and adapts ...
Fig. 2 : 2 Sample path of a multi-agent system based on the nominal CBF (cf. [6] ) and our proposed Robust CBF. ...
arXiv:2004.05273v2
fatcat:lqmwx54fd5hprkdihv5gf7s5qy
Funneled Bayesian Optimization for Design, Tuning and Control of Autonomous Systems
[article]
2019
arXiv
pre-print
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Bayesian optimization has become a fundamental global optimization algorithm in many problems where sample efficiency is of paramount importance. ...
In order to generalize to unknown functions in a black-box fashion, the common assumption is that the underlying function can be modeled with a stationary process. ...
ACKNOWLEDGMENT The authors would like to thank Javier García-Barcos for his help on the CFD simulator and Eduardo Montijano for his valuable comments. ...
arXiv:1610.00366v2
fatcat:3iwxcni4hvdgjl2pupx3jyjksm
Active planning for underwater inspection and the benefit of adaptivity
2012
The international journal of robotics research
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Such analysis allows the use of robust, non-adaptive planning algorithms that perform competitively with adaptive algorithms. ...
We formulate the inspection planning problem as an extension to Bayesian active learning, and we show connections to recent theoretical guarantees in this area. ...
Acknowledgments The authors gratefully acknowledge Jonathan Binney, Jnaneshwar Das, Arvind Pereira and Hordur Heidarsson at the University of Southern California for their insightful comments. ...
doi:10.1177/0278364912467485
fatcat:ksznirv22nd5dazct4blb7fzqu
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications
[article]
2022
arXiv
pre-print
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To mitigate this, we introduce robust Gaussian process uniform error bounds in settings with unknown hyperparameters. ...
However, state-of-the-art techniques for safety-critical settings hinge on the assumption that the kernel hyperparameters are known, which does not apply in general. ...
We thank Christian Fiedler for the useful comments and constructive feedback on the manuscript. ...
arXiv:2109.02606v2
fatcat:zknjqzsnsvfavlcbfkg4nthw5a
A Gaussian sampling heuristic estimation model for developing synthetic trip sets
2021
Computer-Aided Civil and Infrastructure Engineering
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We show that the presented model is more robust and computationally efficient than the benchmark method. ...
We also discuss how the choice of the kernel function and calibration of the hyperparameters influence the performance of the presented heuristic model. ...
Second, we analyze the computational efficiency and robustness of the developed heuristic model in comparison to the benchmark method to determine synthetic sets of trips. ...
doi:10.1111/mice.12697
fatcat:xvnjlnnwtraa3jv3id4x3uho5m
Uncertainty-based Human Motion Tracking with Stable Gaussian Process State Space Models
2019
IFAC-PapersOnLine
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We exploit the model fidelity which is related to the location of the training and test data: Our approach actively strives into regions with more demonstration data and thus higher model certainty. ...
We exploit the model fidelity which is related to the location of the training and test data: Our approach actively strives into regions with more demonstration data and thus higher model certainty. ...
The hyperparameters of the SE kernel are the lengthscales l j ∈ R + , j = 1, . . . , n. ...
doi:10.1016/j.ifacol.2019.01.002
fatcat:wmtduk2pqndble2h4njiub5nle
Prediction of Atomization Energy Using Graph Kernel and Active Learning
[article]
2018
arXiv
pre-print
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2018 pre-print arXiv:1810.07310v1 fatcat:2j5ogxbgova2njd3etz2aslef4Other Versions
We then derive formulas for the efficient evaluation of the kernel. ...
In this paper, we present a kernel-based pipeline that can learn and predict the atomization energy of molecules with high accuracy. ...
Gaussian process regression (GPR) [6] is a robust regression method that features high accuracy, strong smoothness guarantee, and built-in uncertainty estimation. ...
arXiv:1810.07310v2
fatcat:b2sxpxaryjbjthbcgninjptn7i
Using Proximity Graph Cut for Fast and Robust Instance-Based Classification in Large Datasets
2021
Complexity
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This work presents the design and implementation of a classification algorithm with index data structures, which would allow us to build fast and scalable solutions for large multidimensional datasets. ...
Our results suggest that the algorithm can be used in large-scale applications for fast and robust classification, especially when the search index is already constructed for the data. ...
For implementation, we use both properties of NSW graphs to efficiently obtain a path in a graph and combine them with the Jordan curve theorem. ...
doi:10.1155/2021/2011738
doaj:2b877f8528d84394ba3f6fd5d2776363
fatcat:3pfkluar6zdvto5gsoehfwqnpq
A Neurochaos Learning Architecture for Genome Classification
[article]
2020
arXiv
pre-print
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These ChaosFEX features are then fed to a Support Vector Machine with linear kernel for classification. ...
Robustness of ChaosFEX features to additive noise is also demonstrated. ...
B. thanks "The University of Trans-Disciplinary Health Sciences and Technology (TDU)" for permitting this research as part of the PhD programme. ...
arXiv:2010.10995v1
fatcat:7dxjkbeoyfbf7ldd643fyebhqe
On-the-fly construction of surrogate constitutive models for concurrent multiscale mechanical analysis through probabilistic machine learning
2020
Journal of Computational Physics: X
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with robust probabilistic foundations. ...
Hyperparameter optimization The process variance σ 2 f and length scale that compose the kernel and the target noise σ 2 n are hyperparameters that should be learned from the dataset D. ...
doi:10.1016/j.jcpx.2020.100083
fatcat:3ap2ocyz5ncv7gpz77rykqnpam
Multi-tasking the growth of cosmological structures
[article]
2021
arXiv
pre-print
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We find that this multi-task approach outperforms the single-task approach for future surveys and will allow us to detect departures from the standard model with higher significance. ...
By contrast, the limited sensitivity of current data severely hinders the use of agnostic methods, since the Gaussian processes parameters need to be fine tuned in order to obtain robust reconstructions ...
Cosmology with Radio Telescopes, ASTRO-0945), for providing computational resources for this research project. ...
arXiv:2105.01613v2
fatcat:57jztixyajhnfftxdofomxb3ke
Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization
[article]
2020
arXiv
pre-print
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A comparison with a traditional Bayesian Optimization approach to optimize the hyperparameters of the SVM classifier on the large dataset only is reported. ...
A strategy which has been gaining recently importance to drastically reduce computational time and energy consumed is to exploit the availability of different information sources, with different computational ...
Compliance with Ethical Standards Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors. ...
arXiv:2006.14233v1
fatcat:5ivwye2n3nftdp44qj2kgzpila
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