Scalable Gaussian Process Models for Solar Power Forecasting
A. Dahl and E. V. Bonilla
Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy. DARE 2017. Lecture Notes in Computer       (2017)
https://link.springer.com/chapter/10.1007%2F978-3-319-71643-5_9
Semi-parametric Network Structure Discovery Models
A. Dezfouli and E. V. Bonilla and R. Nock
  ArXiv    (2017)
https://arxiv.org/abs/1702.08530
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models
K. Krauth and E. V. Bonilla and K. Cutajar and M. Filippone
UAI      (2017)
krauth-et-al-uai-2017.pdf
krauth-et-al-uai-2017-supplemental.pdf
https://github.com/ebonilla/AutoGP
Random Feature Expansions for Deep Gaussian Processes
K. Cutajar and E. V. Bonilla and P. Michiardi and M. Filippone
ICML      (2017)
cutajar-et-al-icml-2017.pdf
cutajar-et-al-icml-2017-supplemental.pdf
https://github.com/mauriziofilippone/deep_gp_random_features
Gray-box Inference for Structured Gaussian Process Models
P. Galliani and A. Dezfouli and E. V. Bonilla and N. Quadrianto
  AISTATS    (2017)
galliani-et-al-aistats-2016.pdf
galliani-et-al-aistats-2016-supplemental.pdf
Generic Inference in Latent Gaussian Process Models
E. V. Bonilla and K. Krauth and A. Dezfouli
  ArXiv    (2016)
http://arxiv.org/abs/1609.00577
https://github.com/Karl-Krauth/Sparse-GP
Extended and Unscented Kitchen Sinks
E. V. Bonilla and D. Steinberg and A. Reid
ICML      (2016)
bonilla-et-al-icml-2016.pdf
bonilla-et-al-icml-2016-supplemental.pdf
https://ebonilla.github.io/gp-kitchen-sinks/
Scalable Inference for Gaussian Process Models with Black-Box Likelihoods
A. Dezfouli and E. V. Bonilla
NIPS      (2015)
dezfouli-bonilla-nips-2015.pdf
dezfouli-bonilla-nips-2015-supplemental.pdf
https://github.com/adezfouli/savigp
Automated Variational Inference for Gaussian Process Models
T. V. Nguyen and E. V. Bonilla
NIPS      (2014)
nguyen-bonilla-nips-2014.pdf
nguyen-bonilla-nips-2014-supplemental.pdf
http://trungngv.github.io/agp/
Extended and Unscented Gaussian Processes
D. Steinberg and E. V. Bonilla
NIPS      (2014)
steinberg-bonilla-nips-2014.pdf
https://github.com/NICTA/linearizedGP
Collaborative Multi-output Gaussian Processes
T. V. Nguyen and E. V. Bonilla
UAI      (2014)
nguyen-bonilla-uai-2014.pdf
nguyen-bonilla-uai-2014-supplemental.pdf
http://trungngv.github.io/cogp/
Fast Allocation of Gaussian Process Experts
T. V. Nguyen and E. V. Bonilla
ICML      (2014)
nguyen-bonilla-icml-2014.pdf
http://trungngv.github.io/fgp/
Distributed Bayesian Geophysical Inversions
L. McCalman and S. T. O'Callaghan and A. Reid and D. Shen and S. Carter and L. Krieger and G. Beardsmore and E. V. Bonilla and F. T. Ramos
Thirty-Ninth Workshop on Geothermal Reservoir Engineering      (2014)
Automatic Feature Generation for Machine Learning--based Optimising Compilation
H. Leather and E. Bonilla and M. O'Boyle
  TACO  11  (2014)
Dynamic Microarchitectural Adaptation Using Machine Learning
C. Dubach and T. M. Jones and E. V. Bonilla
  TACO  10  (2013)
Efficient Variational Inference for Gaussian Process Regression Networks
T. Nguyen and E. V. Bonilla
AISTATS      (2013)
nguyen-bonilla-aistats-2013.pdf
nguyen-bonilla-aistats-2013-supplemental.pdf
http://trungngv.github.io/gprn/
Bayesian Joint Inversions for the Exploration of Earth Resources
A. Reid and S. O'Callaghan and E. V. Bonilla and L. McCalman and T. Rawling and F. Ramos
IJCAI      (2013)
reid-et-al-ijcai-2013.pdf
Decision-theoretic Sparsification for Gaussian Process Preference Learning
E. Abbasnejad and E. V. Bonilla and S. Sanner
ECML      (2013)
abbasnejad-et-al-ecml-2013.pdf
http://users.cecs.anu.edu.au/~u4940058/gp_ep.zip
http://users.cecs.anu.edu.au/~u4940058/CarPreferences.html
Learning Community-based Preferences via Dirichlet Process Mixtures of Gaussian Processes
E. Abbasnejad and S. Sanner and E. V. Bonilla and P. Poupart
IJCAI      (2013)
abbasnejad-et-al-ijcai-2013.pdf
abbasnejad-et-al-ijcai-2013-appendix.pdf
http://users.cecs.anu.edu.au/~u4940058/mogp_pref.zip
http://users.cecs.anu.edu.au/~u4940058/CarPreferences.html
Predicting Best Design Trade-offs: A Case Study in Processor Customization
M. Zuluaga and E. V. Bonilla and N. Topham
DATE      (2012)
zuluaga-et-al-date-2012.pdf
New Objective Functions for Social Collaborative Filtering
J. Noel and S. Sanner and K.-N. Tran and P. Christen and L. Xie and E. V. Bonilla and E. Abbasnejad and N. D. Penna
WWW      (2012)
noel-et-al-www-2012.pdf
https://code.google.com/p/social-recommendation/
Discriminative Probabilistic Prototype Learning
E. V. Bonilla and A. Robles-Kelly
ICML      (2012)
bonilla-robles-icml-2012.pdf
http://ebonilla.github.io/dppl
Bayesian data fusion for geothermal exploration
F. Ramos and E. V. Bonilla and S. O'Callaghan and A. Reid and W. Uther and M. Sambridge and T. Rawling
AGEC      (2012)
ramos-et-al-agec-2012.pdf
Sparse Gaussian Processes for Learning Preferences
E. Abbasnejad and E. V. Bonilla and S. Sanner
NIPS WS on Choice Models and Preference Learning      (2011)
abbasnejad-et-al-nips-cmpl-2011.pdf
Improving Topic Coherence with Regularized Topic Models
D. Newman and E. V. Bonilla and W. Buntine
NIPS      (2011)
newman-et-al-nips-2011.pdf
http://ebonilla.github.io/rtm
MilePost GCC: A machine learning enabled self-tuning compiler
G. Fursin and Y. Kashnikov and A. W. Memon and Z. Chamski and O. Temam and M. Namolaru and E. Yom-Tov and B. Mendelson and A. Zaks and E. Courtois and F. Bodin and P. Barnard and E. Ashton and E. Bonilla and J. Thomson and C. Williams and M. O'Boyle
  IJPP    (2011)
fursin-et-al-ijpp-2011.pdf
Gaussian Process Preference Elicitation
E. V. Bonilla and S. Guo and S. Sanner
NIPS      (2010)
bonilla-et-al-nips-2010.pdf
http://ebonilla.github.io/gppe
A Predictive Model for Dynamic Microarchitectural Adaptivity Control
C. Dubach and T. M. Jones and E. V. Bonilla and M. F. P. O'Boyle
MICRO      (2010)
dubach-et-al-micro-2010.pdf
Portable Compiler Optimization Across Embedded Programs and Microarchitectures using Machine Learning
C. Dubach and T. M. Jones and E. V. Bonilla and G. Fursin and M. F. P. O'Boyle
MICRO      (2009)
dubach-et-al-micro-2009.pdf
Automatic Feature Generation for Machine Learning Based Optimizing Compilation
H. Leather and E. Bonilla and M. O'Boyle
CGO      (2009)
leather-et-al-cgo-2009.pdf
Compilers that Learn to Optimise: A Probabilistic Machine Learning Approach
E. V. Bonilla
      (2008)
MILEPOST GCC: machine learning based research compiler
G. Fursin and C. Miranda and O. Temam and M. Namolaru and E. Yom-Tov and A. Zaks and B. Mendelson and P. Barnard and E. Ashton and E. Courtois and F. Bodin and E. Bonilla and J. Thomson and H. Leather and C. Williams and M. O'Boyle
GCC Developers' Summit      (2008)
fursin-et-al-gccds-2008.pdf
Multi-task Gaussian Process Prediction
E. V. Bonilla and K. M. A. Chai and C. K. I. Williams
NIPS      (2008)
bonilla-et-al-nips-2008.pdf
http://ebonilla.github.io/mtgp
A Note on Noise-free Gaussian Process Prediction with Separable Covariance Functions and Grid Designs
C. K. I. Williams and K. M. A. Chai and E. V. Bonilla
      (2007)
williams-et-al-techr-2007.pdf
Kernel Multi-task Learning using Task-specific Features
E. V. Bonilla and F. V. Agakov and C. K. I. Williams
AISTATS      (2007)
bonilla-et-al-aistats-2007.pdf
Rapidly Selecting Good Compiler Optimizations using Performance Counters
J. Cavazos and G. Fursin and F. Agakov and E. Bonilla and M. F. P. O'Boyle and O. Temam
CGO      (2007)
cavazos-et-al-cgo-2007.pdf
Predictive search distributions
E. V. Bonilla and C. K. I. Williams and F. V. Agakov and J. Cavazos and J. Thomson and M. F. P. O'Boyle
ICML      (2006)
bonilla-et-al-icml-2006.pdf
Automatic performance model construction for the fast software exploration of new hardware designs
J. Cavazos and C. Dubach and F. Agakov and E. Bonilla and M. F. P. O'Boyle and G. Fursin and O. Temam
CASES      (2006)
cavazos-et-al-cases-2006.pdf
Using Machine Learning to Focus Iterative Optimization
F. Agakov and E. Bonilla and J. Cavazos and B. Franke and G. Fursin and M. O'Boyle and J. Thomson and M. Toussaint and C. Williams
CGO      (2006)
agakov-et-al-cgo-2006.pdf
Predicting the Effect of Loop Unrolling Using Machine Learning
E. V. Bonilla
Postgraduate Research Conference in Electronics, Photonics, Communications and Software      (2005)
Predicting Good Compiler Transformations Using Machine Learning
E. V. Bonilla
      (2004)
Modificaciones de la Senal de Voz en Tiempo y Frecuencia
A. Salazar and E. Bonilla and G. Conzalez and M. A. Rodriguez
  Mundo Electrico Colombiano  16  (2002)
Codificacion de La Voz Basada En Un Modelo Sinusoidal
E. Bonilla and A. Salazar and G. Conzalez and M. A. Rodriguez
  Mundo Electrico Colombiano  16  (2001)