heterogeneous multi output gaussian process prediction
play

Heterogeneous Multi-output Gaussian Process Prediction Pablo - PowerPoint PPT Presentation

Jun 12, 2023 •235 likes •472 views

Heterogeneous Multi-output Gaussian Process Prediction Pablo Moreno-Muoz Antonio Arts-Rodrguez Mauricio A. lvarez Poster #14 1 Motivation Output observations are a mix of continuous, binary, categorical or discrete variables

  1. Heterogeneous Multi-output Gaussian Process Prediction Pablo Moreno-Muñoz Antonio Artés-Rodríguez Mauricio A. Álvarez Poster #14 1

  2. Motivation Output observations are a mix of continuous, binary, categorical or discrete variables Problem Multi-output Gaussian process models usually focus on all-regression or all-classification tasks Provide an extension of multi-output Gaussian processes Goal for prediction in arbitrary heterogeneous datasets 2

  3. Motivation Medical Intensive Care Units (ICU) Electronic Health Records Spatio-temporal Applications Demographic, sociological or economic analysis of cities Bayesian Optimization Flat Other Functions with multiple outputs from different nature 2

  4. <latexit sha1_base64="CJBuvCDKLyR1MrDv7b+NdJQsObM=">ACWXicbVFbS8MwGE3rbc7bdI+BIewvYxWBH0RH3wcYJTYZ0lTdMtLG1C8lUctX/SB0H8Kz6YziHePg5nPdchIpwQ143qvjLiwuLa/UVutr6xubW43tnRsjc01Zn0oh9V1EDBM8Y3gINid0oykWC30eS80m8fmDZcZtcwVWyYklHGE04JWCpsKNUOUgLjKCm5VMQSRGbaWqvIoAxA1J+yY9lp4NPcKC0jMiPvHL+Ki/F4exv81COMfLcJGy+t6s8B/gT8HLTSPXth4DmJ85RlQAUxZuB7CoYF0cCpYGU9yA1ThE7IiA0szEjKzLCYOVPifcvEOJHangzwjP1eUZDUVOvazGpH81uryP+0Q7J8bDgmcqBZfRzUJILDBJXNuOYa0ZBTC0gVHO7K6ZjogkF+xl1a4L/+8l/wc1B1/e6/tVh6/RsbkcN7aI91EY+OkKn6BL1UB9R9ILenSVn2XlzHbfm1j9TXWde0Q/wm1+AEN3tvg=</latexit> <latexit sha1_base64="CJBuvCDKLyR1MrDv7b+NdJQsObM=">ACWXicbVFbS8MwGE3rbc7bdI+BIewvYxWBH0RH3wcYJTYZ0lTdMtLG1C8lUctX/SB0H8Kz6YziHePg5nPdchIpwQ143qvjLiwuLa/UVutr6xubW43tnRsjc01Zn0oh9V1EDBM8Y3gINid0oykWC30eS80m8fmDZcZtcwVWyYklHGE04JWCpsKNUOUgLjKCm5VMQSRGbaWqvIoAxA1J+yY9lp4NPcKC0jMiPvHL+Ki/F4exv81COMfLcJGy+t6s8B/gT8HLTSPXth4DmJ85RlQAUxZuB7CoYF0cCpYGU9yA1ThE7IiA0szEjKzLCYOVPifcvEOJHangzwjP1eUZDUVOvazGpH81uryP+0Q7J8bDgmcqBZfRzUJILDBJXNuOYa0ZBTC0gVHO7K6ZjogkF+xl1a4L/+8l/wc1B1/e6/tVh6/RsbkcN7aI91EY+OkKn6BL1UB9R9ILenSVn2XlzHbfm1j9TXWde0Q/wm1+AEN3tvg=</latexit> <latexit sha1_base64="CJBuvCDKLyR1MrDv7b+NdJQsObM=">ACWXicbVFbS8MwGE3rbc7bdI+BIewvYxWBH0RH3wcYJTYZ0lTdMtLG1C8lUctX/SB0H8Kz6YziHePg5nPdchIpwQ143qvjLiwuLa/UVutr6xubW43tnRsjc01Zn0oh9V1EDBM8Y3gINid0oykWC30eS80m8fmDZcZtcwVWyYklHGE04JWCpsKNUOUgLjKCm5VMQSRGbaWqvIoAxA1J+yY9lp4NPcKC0jMiPvHL+Ki/F4exv81COMfLcJGy+t6s8B/gT8HLTSPXth4DmJ85RlQAUxZuB7CoYF0cCpYGU9yA1ThE7IiA0szEjKzLCYOVPifcvEOJHangzwjP1eUZDUVOvazGpH81uryP+0Q7J8bDgmcqBZfRzUJILDBJXNuOYa0ZBTC0gVHO7K6ZjogkF+xl1a4L/+8l/wc1B1/e6/tVh6/RsbkcN7aI91EY+OkKn6BL1UB9R9ILenSVn2XlzHbfm1j9TXWde0Q/wm1+AEN3tvg=</latexit> <latexit sha1_base64="CJBuvCDKLyR1MrDv7b+NdJQsObM=">ACWXicbVFbS8MwGE3rbc7bdI+BIewvYxWBH0RH3wcYJTYZ0lTdMtLG1C8lUctX/SB0H8Kz6YziHePg5nPdchIpwQ143qvjLiwuLa/UVutr6xubW43tnRsjc01Zn0oh9V1EDBM8Y3gINid0oykWC30eS80m8fmDZcZtcwVWyYklHGE04JWCpsKNUOUgLjKCm5VMQSRGbaWqvIoAxA1J+yY9lp4NPcKC0jMiPvHL+Ki/F4exv81COMfLcJGy+t6s8B/gT8HLTSPXth4DmJ85RlQAUxZuB7CoYF0cCpYGU9yA1ThE7IiA0szEjKzLCYOVPifcvEOJHangzwjP1eUZDUVOvazGpH81uryP+0Q7J8bDgmcqBZfRzUJILDBJXNuOYa0ZBTC0gVHO7K6ZjogkF+xl1a4L/+8l/wc1B1/e6/tVh6/RsbkcN7aI91EY+OkKn6BL1UB9R9ILenSVn2XlzHbfm1j9TXWde0Q/wm1+AEN3tvg=</latexit> Heterogeneous Likelihoods D Y p ( y | θ ( x )) = p ( y d | θ d ( x )) d =1 3

  5. <latexit sha1_base64="CJBuvCDKLyR1MrDv7b+NdJQsObM=">ACWXicbVFbS8MwGE3rbc7bdI+BIewvYxWBH0RH3wcYJTYZ0lTdMtLG1C8lUctX/SB0H8Kz6YziHePg5nPdchIpwQ143qvjLiwuLa/UVutr6xubW43tnRsjc01Zn0oh9V1EDBM8Y3gINid0oykWC30eS80m8fmDZcZtcwVWyYklHGE04JWCpsKNUOUgLjKCm5VMQSRGbaWqvIoAxA1J+yY9lp4NPcKC0jMiPvHL+Ki/F4exv81COMfLcJGy+t6s8B/gT8HLTSPXth4DmJ85RlQAUxZuB7CoYF0cCpYGU9yA1ThE7IiA0szEjKzLCYOVPifcvEOJHangzwjP1eUZDUVOvazGpH81uryP+0Q7J8bDgmcqBZfRzUJILDBJXNuOYa0ZBTC0gVHO7K6ZjogkF+xl1a4L/+8l/wc1B1/e6/tVh6/RsbkcN7aI91EY+OkKn6BL1UB9R9ILenSVn2XlzHbfm1j9TXWde0Q/wm1+AEN3tvg=</latexit> <latexit sha1_base64="CJBuvCDKLyR1MrDv7b+NdJQsObM=">ACWXicbVFbS8MwGE3rbc7bdI+BIewvYxWBH0RH3wcYJTYZ0lTdMtLG1C8lUctX/SB0H8Kz6YziHePg5nPdchIpwQ143qvjLiwuLa/UVutr6xubW43tnRsjc01Zn0oh9V1EDBM8Y3gINid0oykWC30eS80m8fmDZcZtcwVWyYklHGE04JWCpsKNUOUgLjKCm5VMQSRGbaWqvIoAxA1J+yY9lp4NPcKC0jMiPvHL+Ki/F4exv81COMfLcJGy+t6s8B/gT8HLTSPXth4DmJ85RlQAUxZuB7CoYF0cCpYGU9yA1ThE7IiA0szEjKzLCYOVPifcvEOJHangzwjP1eUZDUVOvazGpH81uryP+0Q7J8bDgmcqBZfRzUJILDBJXNuOYa0ZBTC0gVHO7K6ZjogkF+xl1a4L/+8l/wc1B1/e6/tVh6/RsbkcN7aI91EY+OkKn6BL1UB9R9ILenSVn2XlzHbfm1j9TXWde0Q/wm1+AEN3tvg=</latexit> <latexit sha1_base64="CJBuvCDKLyR1MrDv7b+NdJQsObM=">ACWXicbVFbS8MwGE3rbc7bdI+BIewvYxWBH0RH3wcYJTYZ0lTdMtLG1C8lUctX/SB0H8Kz6YziHePg5nPdchIpwQ143qvjLiwuLa/UVutr6xubW43tnRsjc01Zn0oh9V1EDBM8Y3gINid0oykWC30eS80m8fmDZcZtcwVWyYklHGE04JWCpsKNUOUgLjKCm5VMQSRGbaWqvIoAxA1J+yY9lp4NPcKC0jMiPvHL+Ki/F4exv81COMfLcJGy+t6s8B/gT8HLTSPXth4DmJ85RlQAUxZuB7CoYF0cCpYGU9yA1ThE7IiA0szEjKzLCYOVPifcvEOJHangzwjP1eUZDUVOvazGpH81uryP+0Q7J8bDgmcqBZfRzUJILDBJXNuOYa0ZBTC0gVHO7K6ZjogkF+xl1a4L/+8l/wc1B1/e6/tVh6/RsbkcN7aI91EY+OkKn6BL1UB9R9ILenSVn2XlzHbfm1j9TXWde0Q/wm1+AEN3tvg=</latexit> <latexit sha1_base64="CJBuvCDKLyR1MrDv7b+NdJQsObM=">ACWXicbVFbS8MwGE3rbc7bdI+BIewvYxWBH0RH3wcYJTYZ0lTdMtLG1C8lUctX/SB0H8Kz6YziHePg5nPdchIpwQ143qvjLiwuLa/UVutr6xubW43tnRsjc01Zn0oh9V1EDBM8Y3gINid0oykWC30eS80m8fmDZcZtcwVWyYklHGE04JWCpsKNUOUgLjKCm5VMQSRGbaWqvIoAxA1J+yY9lp4NPcKC0jMiPvHL+Ki/F4exv81COMfLcJGy+t6s8B/gT8HLTSPXth4DmJ85RlQAUxZuB7CoYF0cCpYGU9yA1ThE7IiA0szEjKzLCYOVPifcvEOJHangzwjP1eUZDUVOvazGpH81uryP+0Q7J8bDgmcqBZfRzUJILDBJXNuOYa0ZBTC0gVHO7K6ZjogkF+xl1a4L/+8l/wc1B1/e6/tVh6/RsbkcN7aI91EY+OkKn6BL1UB9R9ILenSVn2XlzHbfm1j9TXWde0Q/wm1+AEN3tvg=</latexit> <latexit sha1_base64="NsLos3laW3WlkybC0/eUHSYSJS0=">AB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0GNRDx5bsB/QhrLZTtq1m03Y3Qgl9Bd48aCIV3+SN/+N2zYHbX0w8Hhvhpl5QSK4Nq7RTW1jc2t4rbpZ3dvf2D8uFRS8epYthksYhVJ6AaBZfYNwI7CQKaRQIbAfj25nfkKleSwfzCRBP6JDyUPOqLFS465frhVdw6ySrycVCBHvV/+6g1ilkYoDRNU67nJsbPqDKcCZyWeqnGhLIxHWLXUkj1H42P3RKzqwyIGsbElD5urviYxGWk+iwHZG1Iz0sjcT/O6qQmv/YzLJDUo2WJRmApiYjL7mgy4QmbExBLKFLe3EjaijJjsynZELzl1dJ6LquVWvcVmp3eRxFOETuEcPLiCGtxDHZrAOEZXuHNeXRenHfnY9FacPKZY/gD5/MHl2mMyA=</latexit> <latexit sha1_base64="NsLos3laW3WlkybC0/eUHSYSJS0=">AB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0GNRDx5bsB/QhrLZTtq1m03Y3Qgl9Bd48aCIV3+SN/+N2zYHbX0w8Hhvhpl5QSK4Nq7RTW1jc2t4rbpZ3dvf2D8uFRS8epYthksYhVJ6AaBZfYNwI7CQKaRQIbAfj25nfkKleSwfzCRBP6JDyUPOqLFS465frhVdw6ySrycVCBHvV/+6g1ilkYoDRNU67nJsbPqDKcCZyWeqnGhLIxHWLXUkj1H42P3RKzqwyIGsbElD5urviYxGWk+iwHZG1Iz0sjcT/O6qQmv/YzLJDUo2WJRmApiYjL7mgy4QmbExBLKFLe3EjaijJjsynZELzl1dJ6LquVWvcVmp3eRxFOETuEcPLiCGtxDHZrAOEZXuHNeXRenHfnY9FacPKZY/gD5/MHl2mMyA=</latexit> <latexit sha1_base64="NsLos3laW3WlkybC0/eUHSYSJS0=">AB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0GNRDx5bsB/QhrLZTtq1m03Y3Qgl9Bd48aCIV3+SN/+N2zYHbX0w8Hhvhpl5QSK4Nq7RTW1jc2t4rbpZ3dvf2D8uFRS8epYthksYhVJ6AaBZfYNwI7CQKaRQIbAfj25nfkKleSwfzCRBP6JDyUPOqLFS465frhVdw6ySrycVCBHvV/+6g1ilkYoDRNU67nJsbPqDKcCZyWeqnGhLIxHWLXUkj1H42P3RKzqwyIGsbElD5urviYxGWk+iwHZG1Iz0sjcT/O6qQmv/YzLJDUo2WJRmApiYjL7mgy4QmbExBLKFLe3EjaijJjsynZELzl1dJ6LquVWvcVmp3eRxFOETuEcPLiCGtxDHZrAOEZXuHNeXRenHfnY9FacPKZY/gD5/MHl2mMyA=</latexit> <latexit sha1_base64="NsLos3laW3WlkybC0/eUHSYSJS0=">AB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0GNRDx5bsB/QhrLZTtq1m03Y3Qgl9Bd48aCIV3+SN/+N2zYHbX0w8Hhvhpl5QSK4Nq7RTW1jc2t4rbpZ3dvf2D8uFRS8epYthksYhVJ6AaBZfYNwI7CQKaRQIbAfj25nfkKleSwfzCRBP6JDyUPOqLFS465frhVdw6ySrycVCBHvV/+6g1ilkYoDRNU67nJsbPqDKcCZyWeqnGhLIxHWLXUkj1H42P3RKzqwyIGsbElD5urviYxGWk+iwHZG1Iz0sjcT/O6qQmv/YzLJDUo2WJRmApiYjL7mgy4QmbExBLKFLe3EjaijJjsynZELzl1dJ6LquVWvcVmp3eRxFOETuEcPLiCGtxDHZrAOEZXuHNeXRenHfnY9FacPKZY/gD5/MHl2mMyA=</latexit> Heterogeneous Likelihoods D Y p ( y | θ ( x )) = p ( y d | θ d ( x )) d =1 different statistical data types D 3

Recommend

Multi-task Gaussian Process Prediction Chris Williams Joint Work with Edwin Bonilla, Kian Ming A.

Multi-task Gaussian Process Prediction Chris Williams Joint Work with Edwin Bonilla, Kian Ming A.

Multi-task Gaussian Process Prediction Chris Williams Joint Work with Edwin Bonilla, Kian Ming A. Chai, Stefan Klanke and Sethu Vijayakumar Institute for Adaptive and Neural Computation School of Informatics University of Edinburgh, UK

880 views • 34 slides
Overview Prediction with Gaussian Processes: Basic Ideas Bayesian Prediction Chris Williams

Overview Prediction with Gaussian Processes: Basic Ideas Bayesian Prediction Chris Williams

Overview Prediction with Gaussian Processes: Basic Ideas Bayesian Prediction Chris Williams Gaussian Process Priors over Functions I V N E U R S E I H T GP regression Y T O H F G R E U GP classification D I

351 views • 5 slides
Decentralized Dynamic Scheduling across Heterogeneous Multi core across Heterogeneous Multi

Decentralized Dynamic Scheduling across Heterogeneous Multi core across Heterogeneous Multi

Decentralized Dynamic Scheduling across Heterogeneous Multi core across Heterogeneous Multi core Desktop Grids J Jaehwan Lee , Pete Keleher, Alan Sussman h L P t K l h Al S Department of Computer Science University of Maryland Multi

357 views • 25 slides
Scalable Exact Inference in Multi-Output Gaussian Processes Wessel P. Bruinsma 1 , 2 , Eric Perim

Scalable Exact Inference in Multi-Output Gaussian Processes Wessel P. Bruinsma 1 , 2 , Eric Perim

Scalable Exact Inference in Multi-Output Gaussian Processes Wessel P. Bruinsma 1 , 2 , Eric Perim 2 , Will Tebbutt 1 , J. Scott Hosking 3 , 4 , Arno Solin 5 , Richard E. Turner 1 , 6 1 University of Cambridge, 2 Invenia Labs, 3 British Antarctic

340 views • 23 slides
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation Carlos

Scalable Multi-Class Gaussian Process Classification using Expectation Propagation Carlos

Scalable Multi-Class Gaussian Process Classification using Expectation Propagation Carlos Villacampa-Calvo and Daniel Hern andezLobato Computer Science Department Universidad Aut onoma de Madrid http://dhnzl.org ,

963 views • 69 slides
The challenge of bounded, non-Gaussian, non- linear and multi-scale variables Craig H. Bishop

The challenge of bounded, non-Gaussian, non- linear and multi-scale variables Craig H. Bishop

The challenge of bounded, non-Gaussian, non- linear and multi-scale variables Craig H. Bishop University of Melbourne, Parkville, Victoria, Australia Some Motivation Major source of model error in weather and climate prediction models is

937 views • 69 slides
Joint Emotion Analysis via Multi-task Gaussian Processes Daniel Beck, Trevor Cohn, Lucia Specia

Joint Emotion Analysis via Multi-task Gaussian Processes Daniel Beck, Trevor Cohn, Lucia Specia

Joint Emotion Analysis via Multi-task Gaussian Processes Daniel Beck, Trevor Cohn, Lucia Specia October 28, 2014 Introduction 1 Multi-task Gaussian Process Regression 2 Experiments and Discussion 3 Conclusions and Future Work 4 2 / 23

861 views • 61 slides
Self-reflective Multi-task Gaussian Process Kohei Hayashi 1 , Takashi Takenouchi 1 , Ryota Tomioka

Self-reflective Multi-task Gaussian Process Kohei Hayashi 1 , Takashi Takenouchi 1 , Ryota Tomioka

Self-reflective Multi-task Gaussian Process Kohei Hayashi 1 , Takashi Takenouchi 1 , Ryota Tomioka 2 , Hisashi Kashima 2 1 Graduate School of Information Science Nara Institute of Science and Technology 2 Department of Mathematical Informatics

742 views • 22 slides
Gaussian Process Lei Tang Arizona State University Jul. 31th, 2007 Lei Tang (ASU) Gaussian

Gaussian Process Lei Tang Arizona State University Jul. 31th, 2007 Lei Tang (ASU) Gaussian

Gaussian Process Lei Tang Arizona State University Jul. 31th, 2007 Lei Tang (ASU) Gaussian Process Jul. 31th, 2007 1 / 22 Gaussian Process, (kriging in geostatistics) Autoregressive moving average model, Kalman filters, and radial basis

340 views • 31 slides
Gaussian Processes Seung-Hoon Na Chonbuk National University Gaussian Process Regression

Gaussian Processes Seung-Hoon Na Chonbuk National University Gaussian Process Regression

Gaussian Processes Seung-Hoon Na Chonbuk National University Gaussian Process Regression Predictions using noisy observations The case of a single test input: Gaussian Process Regression Computational and numerical issues It is

444 views • 29 slides
Training Strategies CS 6355: Structured Prediction 1 So far we saw What is structured output

Training Strategies CS 6355: Structured Prediction 1 So far we saw What is structured output

Training Strategies CS 6355: Structured Prediction 1 So far we saw What is structured output prediction? Different ways for modeling structured prediction Conditional random fields, factor graphs, constraints What we only

986 views • 60 slides
Course Information CS 6355: Structured Prediction Building up structured output prediction

Course Information CS 6355: Structured Prediction Building up structured output prediction

Course Information CS 6355: Structured Prediction Building up structured output prediction Refresher of binary classification Inference: Predicting structures, and introduction to multiclass complexity of inference and

213 views • 9 slides
Prediction in kernelized output spaces: output kernel trees and ensemble methods Pierre Geurts

Prediction in kernelized output spaces: output kernel trees and ensemble methods Pierre Geurts

Prediction in kernelized output spaces: output kernel trees and ensemble methods Pierre Geurts Florence dAlchBuc IBISC CNRS, Universit dEvry, GENOPOLE, Evry, France Department of EE and CS, University of Lige, Belgium 25 janvier

631 views • 52 slides
Wafer Fabrication Facility Based on Gaussian Process Regression Zhihong Min Li Li Presenter

Wafer Fabrication Facility Based on Gaussian Process Regression Zhihong Min Li Li Presenter

Prediction Model for Long-term Performance Indicators of Semiconductor Wafer Fabrication Facility Based on Gaussian Process Regression Zhihong Min Li Li Presenter Zhihong Min OUTLINE MOTIVATION &amp; FORMULATION I MODEL &amp;

401 views • 22 slides
Heterogeneous Multi-Computer System A New Platform for Multi-Paradigm Scientific Simulation

Heterogeneous Multi-Computer System A New Platform for Multi-Paradigm Scientific Simulation

Heterogeneous Multi-Computer System A New Platform for Multi-Paradigm Scientific Simulation Taisuke Boku, Hajime Susa, Masayuki Umemura, Akira Ukawa Center for Computational Physics, University of Tsukuba Junichiro Makino, Toshiyuki

531 views • 29 slides
Time-aware API Popularity Prediction via Heterogeneous Features Yao

Time-aware API Popularity Prediction via Heterogeneous Features Yao

ICWS 2015 New York, USA June 27- July 2 2015 Time-aware API Popularity Prediction via Heterogeneous Features Yao Wan 1 , Liang Chen 1 , Jian Wu 1 and Qi Yu 2 1 Zhejiang University,

543 views • 23 slides
Complex Prediction Problems A novel approach to multiple Structured Output Prediction Yasemin

Complex Prediction Problems A novel approach to multiple Structured Output Prediction Yasemin

Complex Prediction Problems A novel approach to multiple Structured Output Prediction Yasemin Altun Max-Planck Institute ECML HLIE08 Yasemin Altun Complex Prediction Information Extraction Extract structured information from unstructured

826 views • 27 slides
Bottom-up Estimation and Top-down Prediction for Multi-level Models: Solar Energy Prediction

Bottom-up Estimation and Top-down Prediction for Multi-level Models: Solar Energy Prediction

Bottom-up Estimation and Top-down Prediction for Multi-level Models: Solar Energy Prediction Combining Information from Multiple Sources Jae-Kwang Kim Department of Statistics, Iowa State University Ross-Royall Symposium: Johns Hopkins

857 views • 37 slides
Kriging a.k.a. Gaussian Process Regression(GPR) Yubo Paul Yang, Algorithm Interest Group,

Kriging a.k.a. Gaussian Process Regression(GPR) Yubo Paul Yang, Algorithm Interest Group,

Kriging a.k.a. Gaussian Process Regression(GPR) Yubo Paul Yang, Algorithm Interest Group, Jan. 17 2019 What is kriging? Kriging is an interpolation method. Kriging minimizes the variance of prediction error at data points. Kriging

164 views • 14 slides
Lecture 13 Gaussian Process Models - Part 2 3/06/2018 1 EDA and GPs 2 Variogram When fitting

Lecture 13 Gaussian Process Models - Part 2 3/06/2018 1 EDA and GPs 2 Variogram When fitting

Lecture 13 Gaussian Process Models - Part 2 3/06/2018 1 EDA and GPs 2 Variogram When fitting a Gaussian process model, it is often difficult to fit the covariance parameters (hard to identify). Today we will discuss some EDA approaches for

1.15k views • 49 slides
Bayesian Neural Networks from a Gaussian Process Perspective Andrew Gordon Wilson

Bayesian Neural Networks from a Gaussian Process Perspective Andrew Gordon Wilson

Bayesian Neural Networks from a Gaussian Process Perspective Andrew Gordon Wilson https://cims.nyu.edu/~andrewgw Courant Institute of Mathematical Sciences Center for Data Science New York University Gaussian Process Summer School September

508 views • 47 slides
Outline 1. Motivation 2. Gaussian process introduction 3. Change surface model 4. Analysis of

Outline 1. Motivation 2. Gaussian process introduction 3. Change surface model 4. Analysis of

5/22/2016 Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces April 18, 2016 William Herlands Committee: Daniel Neill, Alex Smola, Wilbert Van Panhuis Chair: Dave Choi Outline 1. Motivation 2. Gaussian process

1k views • 21 slides
Ruin problem for integrated stationary Gaussian process Kobelkov S. G. 1 Consider a random process

Ruin problem for integrated stationary Gaussian process Kobelkov S. G. 1 Consider a random process

Ruin problem for integrated stationary Gaussian process Kobelkov S. G. 1 Consider a random process t 0 X s ds ct , Y t = where X t , t 0 is a stationary real-valued zero-mean Gaussian process with continuous trajectories and twice

343 views • 5 slides
Regularization for Multi-Output Learning Lorenzo Rosasco 9.520 L. Rosasco Regularization for

Regularization for Multi-Output Learning Lorenzo Rosasco 9.520 L. Rosasco Regularization for

Regularization for Multi-Output Learning Lorenzo Rosasco 9.520 L. Rosasco Regularization for Multi-Output Learning About this class Goal In many practical problems, it is convenient to model the object of interest as a function with multiple

507 views • 47 slides

More recommend