Edwin V. Bonilla

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Sparse Grouped Gaussian Processes for Solar Power Forecasting pdf

Astrid Dahl, Edwin V. Bonilla, 2019, ArXiv.

Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations

Astrid Dahl, Edwin V. Bonilla, 2019 .

Milepost GCC: Machine Learning Enabled Self-tuning Compiler

Michael F. P. O'Boyle, Olivier Temam, Ayal Zaks, 2011, International Journal of Parallel Programming.

Dynamic microarchitectural adaptation using machine learning

Edwin V. Bonilla, Timothy M. Jones, Christophe Dubach, 2013, ACM Trans. Archit. Code Optim..

Compilers that learn to optimise : a probabilistic machine learning approach

Edwin V. Bonilla, 2008 .

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, AISTATS 2007, San Juan, Puerto Rico, March 21-24, 2007

Edwin V. Bonilla, Christopher K. I. Williams, Felix Agakov, 2007, International Conference on Artificial Intelligence and Statistics.

Multi-task Gaussian Process Prediction

Edwin V. Bonilla, Christopher K. I. Williams, Kian Ming Adam Chai, 2007, NIPS.

Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations pdf

Maurizio Filippone, Edwin V. Bonilla, Markus Heinonen, 2020, ArXiv.

Portable compiler optimisation across embedded programs and microarchitectures using machine learning

Michael F. P. O'Boyle, Grigori Fursin, Edwin V. Bonilla, 2009, 2009 42nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

Gray-box Inference for Structured Gaussian Process Models

Pietro Galliani, Edwin V. Bonilla, Novi Quadrianto, 2017, AISTATS.

Calibrating Deep Convolutional Gaussian Processes pdf

Maurizio Filippone, Pietro Michiardi, Edwin V. Bonilla, 2018, AISTATS.

Kernel Multi-task Learning using Task-specific Features

Edwin V. Bonilla, Christopher K. I. Williams, Felix V. Agakov, 2007, AISTATS.

Using machine learning to focus iterative optimization

Michael F. P. O'Boyle, Grigori Fursin, Edwin V. Bonilla, 2006, International Symposium on Code Generation and Optimization (CGO'06).

Scalable Inference for Gaussian Process Models with Black-Box Likelihoods

Edwin V. Bonilla, Amir Dezfouli, A. Dezfouli, 2015, NIPS.

Sparse Gaussian Processes for Learning Preferences

Scott Sanner, Edwin V. Bonilla, Ehsan Abbasnejad, 2011, NIPS 2011.

Extended and Unscented Kitchen Sinks

Edwin V. Bonilla, Alistair Reid, Daniel M. Steinberg, 2016, ICML.

Log Gaussian Cox Process Networks pdf

Edwin V. Bonilla, Theodoros Damoulas, Virginia Aglietti, 2018, ArXiv.

A Predictive Model for Dynamic Microarchitectural Adaptivity Control

Michael F. P. O'Boyle, Edwin V. Bonilla, Timothy M. Jones, 2010, 2010 43rd Annual IEEE/ACM International Symposium on Microarchitecture.

Random Feature Expansions for Deep Gaussian Processes

Maurizio Filippone, Pietro Michiardi, Edwin V. Bonilla, 2016, ICML.

New objective functions for social collaborative filtering

Scott Sanner, Peter Christen, Lexing Xie, 2012, WWW.

Efficient Variational Inference for Gaussian Process Regression Networks

Edwin V. Bonilla, Trung V. Nguyen, T. Nguyen, 2013, AISTATS.

AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models

Maurizio Filippone, Edwin V. Bonilla, Karl Krauth, 2016, UAI.

Variational Network Inference: Strong and Stable with Concrete Support

Richard Nock, Edwin V. Bonilla, Amir Dezfouli, 2018, ICML.

Variational Spectral Graph Convolutional Networks pdf

Edwin V. Bonilla, Pantelis Elinas, Harrison Nguyen, 2019, ArXiv.

Grouped Gaussian Processes for Solar Forecasting

Astrid Dahl, Edwin V. Bonilla, 2018 .

Collaborative Multi-output Gaussian Processes

Edwin V. Bonilla, Trung V. Nguyen, T. Nguyen, 2014, UAI.

MILEPOST GCC: machine learning based research compiler

Michael F. P. O'Boyle, Ayal Zaks, Grigori Fursin, 2008 .

Grouped Gaussian processes for solar power prediction

Astrid Dahl, Edwin V. Bonilla, 2019, Machine Learning.

Learning Community-Based Preferences via Dirichlet Process Mixtures of Gaussian Processes

Scott Sanner, Pascal Poupart, Edwin V. Bonilla, 2013, IJCAI.

Proceedings of the GCC Developers' Summit

Michael F. P. O'Boyle, Ayal Zaks, Grigori Fursin, 2008 .

Structured Variational Inference in Continuous Cox Process Models

Edwin V. Bonilla, Theodoros Damoulas, Sally Cripps, 2019, NeurIPS.

Automated Variational Inference for Gaussian Process Models

Edwin V. Bonilla, Trung V. Nguyen, 2014, NIPS.

Quantile Propagation for Wasserstein-Approximate Gaussian Processes pdf

Lexing Xie, Edwin V. Bonilla, Marian-Andrei Rizoiu, 2019, NeurIPS.

Automatic feature generation for machine learning-based optimising compilation

Michael F. P. O'Boyle, Edwin V. Bonilla, Hugh Leather, 2014, ACM Trans. Archit. Code Optim..

Fast Allocation of Gaussian Process Experts

Edwin V. Bonilla, Trung V. Nguyen, T. Nguyen, 2014, ICML.

Predictive search distributions

Michael F. P. O'Boyle, Edwin V. Bonilla, Christopher K. I. Williams, 2006, ICML '06.

Gaussian Process Preference Elicitation

Scott Sanner, Edwin V. Bonilla, Shengbo Guo, 2010, NIPS.

Bayesian Joint Inversions for the Exploration of Earth Resources

Fabio Tozeto Ramos, Edwin V. Bonilla, Tim Rawling, 2013, IJCAI.

Extended and Unscented Gaussian Processes

Edwin V. Bonilla, Daniel M. Steinberg, D. Steinberg, 2014, NIPS.

Automatic performance model construction for the fast software exploration of new hardware designs

Michael F. P. O'Boyle, Olivier Temam, Grigori Fursin, 2006, CASES '06.

Scalable Gaussian Process Models for Solar Power Forecasting

Astrid Dahl, Edwin V. Bonilla, A. Dahl, 2017, DARE@PKDD/ECML.

Generic Inference in Latent Gaussian Process Models

Edwin V. Bonilla, Amir Dezfouli, Karl Krauth, 2016, J. Mach. Learn. Res..

Rapidly Selecting Good Compiler Optimizations using Performance Counters

Michael F. P. O'Boyle, Olivier Temam, Grigori Fursin, 2007, International Symposium on Code Generation and Optimization (CGO'07).

Distribution Regression for Sequential Data

Edwin V. Bonilla, Theodoros Damoulas, Terry Lyons, 2020, AISTATS.

Automatic Feature Generation for Machine Learning Based Optimizing Compilation

Michael F. P. O'Boyle, Edwin V. Bonilla, Hugh Leather, 2009, 2009 International Symposium on Code Generation and Optimization.

Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations

Maurizio Filippone, Edwin V. Bonilla, Markus Heinonen, 2020, AISTATS.

Predicting Good Compiler Transformations Using Machine Learning

Edwin V. Bonilla, 2004 .

Variational Spectral Graph Convolutional Networks

Louis C. Tiao, Edwin V. Bonilla, Pantelis Elinas, 2019, ArXiv.

Discriminative Probabilistic Prototype Learning pdf

Antonio Robles-Kelly, Edwin V. Bonilla, 2012, ICML.

Improving Topic Coherence with Regularized Topic Models

Edwin V. Bonilla, Wray L. Buntine, David Newman, 2011, NIPS.

Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference pdf

Fabio Tozeto Ramos, Louis C. Tiao, Edwin V. Bonilla, 2018, ArXiv.

Decision-Theoretic Sparsification for Gaussian Process Preference Learning

Scott Sanner, Edwin V. Bonilla, Ehsan Abbasnejad, 2013, ECML/PKDD.

Predicting best design trade-offs: A case study in processor customization

Edwin V. Bonilla, Nigel P. Topham, Marcela Zuluaga, 2012, 2012 Design, Automation & Test in Europe Conference & Exhibition (DATE).

Semi-parametric Network Structure Discovery Models pdf

Richard Nock, Edwin V. Bonilla, Amir Dezfouli, 2017, ArXiv.

BORE: Bayesian Optimization by Density-Ratio Estimation

Aaron Klein, Louis C. Tiao, Cedric Archambeau, 2021, ICML.

Learning ODEs via Diffeomorphisms for Fast and Robust Integration pdf

Edwin V. Bonilla, Lionel Ott, Fabio Ramos, 2021, ArXiv.

SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data pdf

Edwin V. Bonilla, Theodoros Damoulas, Terry Lyons, 2021, ICML.

Distribution Regression for Sequential Data

Edwin V. Bonilla, Theodoros Damoulas, Terry Lyons, 2020, AISTATS.

Model Selection for Bayesian Autoencoders pdf

Maurizio Filippone, Pietro Michiardi, Edwin V. Bonilla, 2021, NeurIPS.

Explorer Multitask Gaussian Process Prediction

Edwin V. Bonilla, Christopher K. I. Williams, K. M. A. Chai, 2008 .

A note on noise-free Gaussian process prediction with separable covariance functions and grid designs

Edwin V. Bonilla, Christopher K. I. Williams, K. M. A. Chai, 2007 .

DISTRIBUTED BAYESIAN GEOPHYSICAL INVERSIONS

Edwin V. Bonilla, F. Ramos, S. O'Callaghan, 2014 .

Optimizing Sequential Experimental Design with Deep Reinforcement Learning

Edwin V. Bonilla, I. Chades, A. Dezfouli, 2022, ICML.

Gaussian processes

Edwin V. Bonilla, 2020, Dynamical Theories of Brownian Motion.

Recurrent Neural Networks and Universal Approximation of Bayesian Filters

Edwin V. Bonilla, A. Bishop, 2022, ArXiv.

Learning to Counter: Stochastic Feature-based Learning for Diverse Counterfactual Explanations

Edwin V. Bonilla, Dinh Q. Phung, Gholamreza Haffari, 2022, ArXiv.

Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks

Edwin V. Bonilla, Weiming Zhi, Tin Lai, 2022, ICML.

Bayesian Optimization by Density Ratio Estimation

Louis C. Tiao, Edwin V. Bonilla, C. Archambeau, 2020 .

Distribution Regression for Continuous-Time Processes via the Expected Signature pdf

Edwin V. Bonilla, M. Lemercier, C. Salvi, 2020, ArXiv.

Supplementary Material BORE: Bayesian Optimization by Density-Ratio Estimation

Louis C. Tiao, Edwin V. Bonilla, C. Archambeau, 2021 .

Recurrent Neural Networks and Universal Approximation of Bayesian Filters

Edwin V. Bonilla, A. Bishop, 2022, International Conference on Artificial Intelligence and Statistics.

Transformed Distribution Matching for Missing Value Imputation

Edwin V. Bonilla, Ke Sun, A. Dezfouli, 2023, ICML.

Learning Directed Graphical Models with Optimal Transport

Edwin V. Bonilla, Vy A. Vo, Dinh Q. Phung, 2023, 2305.15927.

Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision

Edwin V. Bonilla, P. Elinas, 2022, ArXiv.

Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings

Louis C. Tiao, Edwin V. Bonilla, P. Elinas, 2019, Neural Information Processing Systems.

Cross-Entropy Estimators for Sequential Experiment Design with Reinforcement Learning

Edwin V. Bonilla, I. Chades, A. Dezfouli, 2023, ArXiv.

Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations

Edwin V. Bonilla, Vy A. Vo, Dinh Q. Phung, 2022, KDD.

S TOCHASTIC O PTIMIZATION FOR C OUNTERFACTUAL E XPLANATIONS

Edwin V. Bonilla, Dinh Q. Phung, Gholamreza Haffari, 2023 .

Free-Form Variational Inference for Gaussian Process State-Space Models

Edwin V. Bonilla, Xuhui Fan, S. Sisson, 2023, ICML.

Efficient Inference in Multi-task Cox Process Models

Edwin V. Bonilla, Theodoros Damoulas, Virginia Aglietti, 2018, AISTATS.

Proceedings of the GCC Developers' Summit

Edwin V. Bonilla, Christopher K. I. Williams, G. Fursin, 2008 .

Learning Directed Graphical Models with Optimal Transport

Edwin V. Bonilla, Vy A. Vo, Dinh Q. Phung, 2023, ArXiv.

Parameter Estimation in DAGs from Incomplete Data via Optimal Transport

Edwin V. Bonilla, Vy A. Vo, Dinh Q. Phung, 2023, 2305.15927.

Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations

Edwin V. Bonilla, M. Filippone, Zheyan Shen, 2020, ArXiv.