Non-Stationary Spectral Kernels

Sami Remes; Markus Heinonen; Samuel Kaski

Non-Stationary Spectral Kernels

Sami Remes, Markus Heinonen, Samuel Kaski

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

63 Sitaatiot (Scopus)

Abstrakti

We propose non-stationary spectral kernels for Gaussian process regression. We propose to model the spectral density of a non-stationary kernel function as a mixture of input-dependent Gaussian process frequency density surfaces. We solve the generalised Fourier transform with such a model, and present a family of non-stationary and non-monotonic kernels that can learn input-dependent and potentially long-range, non-monotonic covariances between inputs. We derive efficient inference using model whitening and marginalized posterior, and show with case studies that these kernels are necessary when modelling even rather simple time series, image or geospatial data with non-stationary characteristics.

Alkuperäiskieli	Englanti
Otsikko	Advances in Neural Information Processing Systems 30
Alaotsikko	Proceedings of NIPS2017
Kustantaja	Curran Associates Inc.
Sivut	4645-4654
Tila	Julkaistu - 2017
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	NIPS Symposium on Interpretable Machine Learning - Long Beach, Los Angeles, Yhdysvallat Kesto: 4 jouluk. 2017 → 9 jouluk. 2017 Konferenssinumero: 31

Julkaisusarja

Nimi	Advances in Neural Information Processing Systems
Kustantaja	Curran Associates
Vuosikerta	30
ISSN (painettu)	1049-5258

Conference

Conference	NIPS Symposium on Interpretable Machine Learning
Maa/Alue	Yhdysvallat
Kaupunki	Los Angeles
Ajanjakso	04/12/2017 → 09/12/2017

Pääsy asiakirjaan

Seuraavan sukupolven tilastollinen oppiminen synteettisten entsyymien muokkaukseen
Heinonen, M. (Vastuullinen tutkija)
01/09/2016 → 31/08/2019
Projekti: Academy of Finland: Other research funding
Interaktiivinen koneoppiminen useista biodatalähteistä
Kaski, S. (Vastuullinen tutkija), Reinvall, J. (Projektin jäsen), Chen, Y. (Projektin jäsen), Daee, P. (Projektin jäsen), Qin, X. (Projektin jäsen), Jälkö, J. (Projektin jäsen), Pesonen, H. (Projektin jäsen), Blomstedt, P. (Projektin jäsen), Eranti, P. (Projektin jäsen), Hegde, P. (Projektin jäsen), Siren, J. (Projektin jäsen), Peltola, T. (Projektin jäsen), Çelikok, M. M. (Projektin jäsen), Sundin, I. (Projektin jäsen), Kangas, J.-K. (Projektin jäsen), Afrabandpey, H. (Projektin jäsen), Honkamaa, J. (Projektin jäsen), Shen, Z. (Projektin jäsen) & Aushev, A. (Projektin jäsen)
01/01/2016 → 31/12/2018
Projekti: Academy of Finland: Other research funding
Interaktiivinen koneoppiminen useista biodatalähteistä
Kaski, S. (Vastuullinen tutkija) & Filstroff, L. (Projektin jäsen)
01/01/2016 → 31/08/2021
Projekti: Academy of Finland: Other research funding

Science-IT
Hakala, M. (Manager)
Perustieteiden korkeakoulu
Laitteistot/tilat: Facility

Siteeraa tätä

@inproceedings{e29a7f5333d4498a9b65a236acc2301b,

title = "Non-Stationary Spectral Kernels",

abstract = "We propose non-stationary spectral kernels for Gaussian process regression. We propose to model the spectral density of a non-stationary kernel function as a mixture of input-dependent Gaussian process frequency density surfaces. We solve the generalised Fourier transform with such a model, and present a family of non-stationary and non-monotonic kernels that can learn input-dependent and potentially long-range, non-monotonic covariances between inputs. We derive efficient inference using model whitening and marginalized posterior, and show with case studies that these kernels are necessary when modelling even rather simple time series, image or geospatial data with non-stationary characteristics.",

author = "Sami Remes and Markus Heinonen and Samuel Kaski",

note = "Odottele, ett{\"a} julkaisu tulee ulos kes{\"a}ll{\"a} ja lis{\"a}{\"a} ISBN. Asiasta sovittu Samuel Kasken ja Markus Heinosen kanssa tammikuussa.; NIPS Symposium on Interpretable Machine Learning ; Conference date: 04-12-2017 Through 09-12-2017",

year = "2017",

language = "English",

series = "Advances in Neural Information Processing Systems",

publisher = "Curran Associates Inc.",

pages = "4645--4654",

booktitle = "Advances in Neural Information Processing Systems 30",

address = "United States",

}

Remes, S, Heinonen, M & Kaski, S 2017, Non-Stationary Spectral Kernels. julkaisussa Advances in Neural Information Processing Systems 30: Proceedings of NIPS2017. Advances in Neural Information Processing Systems, Vuosikerta 30, Curran Associates Inc., Sivut 4645-4654, NIPS Symposium on Interpretable Machine Learning, Los Angeles, California, Yhdysvallat, 04/12/2017. <https://arxiv.org/abs/1705.08736>

Non-Stationary Spectral Kernels. / Remes, Sami; Heinonen, Markus ; Kaski, Samuel.
Advances in Neural Information Processing Systems 30: Proceedings of NIPS2017. Curran Associates Inc., 2017. s. 4645-4654 (Advances in Neural Information Processing Systems; Vuosikerta 30).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Non-Stationary Spectral Kernels

AU - Remes, Sami

AU - Heinonen, Markus

AU - Kaski, Samuel

N1 - Conference code: 31

PY - 2017

Y1 - 2017

N2 - We propose non-stationary spectral kernels for Gaussian process regression. We propose to model the spectral density of a non-stationary kernel function as a mixture of input-dependent Gaussian process frequency density surfaces. We solve the generalised Fourier transform with such a model, and present a family of non-stationary and non-monotonic kernels that can learn input-dependent and potentially long-range, non-monotonic covariances between inputs. We derive efficient inference using model whitening and marginalized posterior, and show with case studies that these kernels are necessary when modelling even rather simple time series, image or geospatial data with non-stationary characteristics.

AB - We propose non-stationary spectral kernels for Gaussian process regression. We propose to model the spectral density of a non-stationary kernel function as a mixture of input-dependent Gaussian process frequency density surfaces. We solve the generalised Fourier transform with such a model, and present a family of non-stationary and non-monotonic kernels that can learn input-dependent and potentially long-range, non-monotonic covariances between inputs. We derive efficient inference using model whitening and marginalized posterior, and show with case studies that these kernels are necessary when modelling even rather simple time series, image or geospatial data with non-stationary characteristics.

M3 - Conference article in proceedings

T3 - Advances in Neural Information Processing Systems

SP - 4645

EP - 4654

BT - Advances in Neural Information Processing Systems 30

PB - Curran Associates Inc.

T2 - NIPS Symposium on Interpretable Machine Learning

Y2 - 4 December 2017 through 9 December 2017

ER -

Non-Stationary Spectral Kernels

Abstrakti

Julkaisusarja

Conference

Pääsy asiakirjaan

Sormenjälki

Projektit

Seuraavan sukupolven tilastollinen oppiminen synteettisten entsyymien muokkaukseen

Interaktiivinen koneoppiminen useista biodatalähteistä

Interaktiivinen koneoppiminen useista biodatalähteistä

Laitteet

Science-IT

Siteeraa tätä