Edwin V. Bonilla

ORCID

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 51

Top Venues

University of Edinburgh, UK (PHD)

Education History

University of Edinburgh, UK

PHD

Publications

He Zhao, Edwin V. Bonilla
Bayesian Factorised Granger-Causal Graphs For Multivariate Time-series Data. CoRR (2024)
Ryan Thompson, Edwin V. Bonilla, Robert Kohn
ProDAG: Projection-induced variational inference for directed acyclic graphs. CoRR (2024)
Edwin V. Bonilla, Pantelis Elinas, He Zhao, Maurizio Filippone, Vassili Kitsios, Terry O'Kane
Variational DAG Estimation via State Augmentation With Stochastic Permutations. CoRR (2024)
Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Q. Phung
Optimal Transport for Structure Learning Under Missing Data. CoRR (2024)
Xuesong Wang, He Zhao, Edwin V. Bonilla
Rényi Neural Processes. CoRR (2024)
Ryan Thompson, Edwin V. Bonilla, Robert Kohn
Contextual Directed Acyclic Graphs. AISTATS (2024)
He Zhao, Ke Sun, Amir Dezfouli, Edwin V. Bonilla
Transformed Distribution Matching for Missing Value Imputation. CoRR (2023)
He Zhao, Ke Sun, Amir Dezfouli, Edwin V. Bonilla
Transformed Distribution Matching for Missing Value Imputation. ICML (2023)
Adrian N. Bishop, Edwin V. Bonilla
Recurrent Neural Networks and Universal Approximation of Bayesian Filters. AISTATS (2023)
Ryan Thompson, Edwin V. Bonilla, Robert Kohn
Contextual directed acyclic graphs. CoRR (2023)
Xuhui Fan, Edwin V. Bonilla, Terence J. O'Kane, Scott A. Sisson
Free-Form Variational Inference for Gaussian Process State-Space Models. ICML (2023)
Xuhui Fan, Edwin V. Bonilla, Terence J. O'Kane, Scott A. Sisson
Free-Form Variational Inference for Gaussian Process State-Space Models. CoRR (2023)
Vy Vo, Trung Le, Van Nguyen, He Zhao, Edwin V. Bonilla, Gholamreza Haffari, Dinh Q. Phung
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations. KDD (2023)
Tom Blau, Edwin V. Bonilla, Amir Dezfouli, Iadine Chades
Optimizing Sequential Experimental Design with Deep Reinforcement Learning. CoRR (2022)
Weiming Zhi, Tin Lai, Lionel Ott, Edwin V. Bonilla, Fabio Ramos
Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks. ICML (2022)
Adrian N. Bishop, Edwin V. Bonilla
Recurrent Neural Networks and Universal Approximation of Bayesian Filters. CoRR (2022)
Pantelis Elinas, Edwin V. Bonilla
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision. CoRR (2022)
Tom Blau, Edwin V. Bonilla, Iadine Chades, Amir Dezfouli
Optimizing Sequential Experimental Design with Deep Reinforcement Learning. ICML (2022)
Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone
Model Selection for Bayesian Autoencoders. CoRR (2021)
Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone
Model Selection for Bayesian Autoencoders. NeurIPS (2021)
Maud Lemercier, Cristopher Salvi, Thomas Cass, Edwin V. Bonilla, Theodoros Damoulas, Terry J. Lyons
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data. ICML (2021)
Maud Lemercier, Cristopher Salvi, Thomas Cass, Edwin V. Bonilla, Theodoros Damoulas, Terry J. Lyons
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data. CoRR (2021)
Weiming Zhi, Tin Lai, Lionel Ott, Edwin V. Bonilla, Fabio Ramos
Learning ODEs via Diffeomorphisms for Fast and Robust Integration. CoRR (2021)
Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos
BORE: Bayesian Optimization by Density-Ratio Estimation. CoRR (2021)
Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos
BORE: Bayesian Optimization by Density-Ratio Estimation. ICML (2021)
Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations. AISTATS (2021)
Maud Lemercier, Cristopher Salvi, Theodoros Damoulas, Edwin V. Bonilla, Terry J. Lyons
Distribution Regression for Sequential Data. AISTATS (2021)
Rui Zhang, Christian J. Walder, Edwin V. Bonilla, Marian-Andrei Rizoiu, Lexing Xie
Quantile Propagation for Wasserstein-Approximate Gaussian Processes. NeurIPS (2020)
Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone
Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations. CoRR (2020)
Maud Lemercier, Cristopher Salvi, Theodoros Damoulas, Edwin V. Bonilla, Terry J. Lyons
Distribution Regression for Continuous-Time Processes via the Expected Signature. CoRR (2020)
Pantelis Elinas, Edwin V. Bonilla, Louis C. Tiao
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings. NeurIPS (2020)
Astrid Dahl, Edwin V. Bonilla
Sparse Grouped Gaussian Processes for Solar Power Forecasting. CoRR (2019)
Virginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla
Efficient Inference in Multi-task Cox Process Models. AISTATS (2019)
Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas, Sally Cripps
Structured Variational Inference in Continuous Cox Process Models. CoRR (2019)
Edwin V. Bonilla, Karl Krauth, Amir Dezfouli
Generic Inference in Latent Gaussian Process Models. J. Mach. Learn. Res. 20 (2019)
Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone
Calibrating Deep Convolutional Gaussian Processes. AISTATS (2019)
Rui Zhang, Christian J. Walder, Edwin V. Bonilla, Marian-Andrei Rizoiu, Lexing Xie
Quantile Propagation for Wasserstein-Approximate Gaussian Processes. CoRR (2019)
Louis C. Tiao, Pantelis Elinas, Harrison Nguyen, Edwin V. Bonilla
Variational Spectral Graph Convolutional Networks. CoRR (2019)
Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas, Sally Cripps
Structured Variational Inference in Continuous Cox Process Models. NeurIPS (2019)
Astrid Dahl, Edwin V. Bonilla
Grouped Gaussian processes for solar power prediction. Mach. Learn. 108 (8-9) (2019)
Louis C. Tiao, Edwin V. Bonilla, Fabio Ramos
Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference. CoRR (2018)
Amir Dezfouli, Edwin V. Bonilla, Richard Nock
Variational Network Inference: Strong and Stable with Concrete Support. ICML (2018)
Virginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla
Log Gaussian Cox Process Networks. CoRR (2018)
Astrid Dahl, Edwin V. Bonilla
Grouped Gaussian Processes for Solar Power Prediction. CoRR (2018)
Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone
Calibrating Deep Convolutional Gaussian Processes. CoRR (2018)
Karl Krauth, Edwin V. Bonilla, Kurt Cutajar, Maurizio Filippone
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models. UAI (2017)
Pietro Galliani, Amir Dezfouli, Edwin V. Bonilla, Novi Quadrianto
Gray-box Inference for Structured Gaussian Process Models. AISTATS (2017)
Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi, Maurizio Filippone
Random Feature Expansions for Deep Gaussian Processes. ICML (2017)
Astrid Dahl, Edwin V. Bonilla
Scalable Gaussian Process Models for Solar Power Forecasting. DARE@PKDD/ECML (2017)
Amir Dezfouli, Edwin V. Bonilla, Richard Nock
Semi-parametric Network Structure Discovery Models. CoRR (2017)
Edwin V. Bonilla, Daniel M. Steinberg, Alistair Reid
Extended and Unscented Kitchen Sinks. ICML (2016)
Amir Dezfouli, Edwin V. Bonilla
Scalable Inference for Gaussian Process Models with Black-Box Likelihoods. NIPS (2015)
Daniel M. Steinberg, Edwin V. Bonilla
Extended and Unscented Gaussian Processes. NIPS (2014)
Trung V. Nguyen, Edwin V. Bonilla
Automated Variational Inference for Gaussian Process Models. NIPS (2014)
Hugh Leather, Edwin V. Bonilla, Michael F. P. O'Boyle
Automatic feature generation for machine learning-based optimising compilation. ACM Trans. Archit. Code Optim. 11 (1) (2014)
Trung V. Nguyen, Edwin V. Bonilla
Fast Allocation of Gaussian Process Experts. ICML (2014)
Trung V. Nguyen, Edwin V. Bonilla
Collaborative Multi-output Gaussian Processes. UAI (2014)
Alistair Reid, Simon Timothy O'Callaghan, Edwin V. Bonilla, Lachlan McCalman, Tim Rawling, Fabio Ramos
Bayesian Joint Inversions for the Exploration of Earth Resources. IJCAI (2013)
Ehsan Abbasnejad, Scott Sanner, Edwin V. Bonilla, Pascal Poupart
Learning Community-Based Preferences via Dirichlet Process Mixtures of Gaussian Processes. IJCAI (2013)
Christophe Dubach, Timothy M. Jones, Edwin V. Bonilla
Dynamic microarchitectural adaptation using machine learning. ACM Trans. Archit. Code Optim. 10 (4) (2013)
M. Ehsan Abbasnejad, Edwin V. Bonilla, Scott Sanner
Decision-Theoretic Sparsification for Gaussian Process Preference Learning. ECML/PKDD (2) (2013)
Trung V. Nguyen, Edwin V. Bonilla
Efficient Variational Inference for Gaussian Process Regression Networks. AISTATS (2013)
Marcela Zuluaga, Edwin V. Bonilla, Nigel P. Topham
Predicting best design trade-offs: A case study in processor customization. DATE (2012)
Joseph Noel, Scott Sanner, Khoi-Nguyen Tran, Peter Christen, Lexing Xie, Edwin V. Bonilla, Ehsan Abbasnejad, Nicolás Della Penna
New objective functions for social collaborative filtering. WWW (2012)
Edwin V. Bonilla, Antonio Robles-Kelly
Discriminative Probabilistic Prototype Learning CoRR (2012)
Edwin V. Bonilla, Antonio Robles-Kelly
Discriminative Probabilistic Prototype Learning. ICML (2012)
David Newman, Edwin V. Bonilla, Wray L. Buntine
Improving Topic Coherence with Regularized Topic Models. NIPS (2011)
Grigori Fursin, Yuriy Kashnikov, Abdul Wahid Memon, Zbigniew Chamski, Olivier Temam, Mircea Namolaru, Elad Yom-Tov, Bilha Mendelson, Ayal Zaks, Eric Courtois, François Bodin, Phil Barnard, Elton Ashton, Edwin V. Bonilla, John Thomson, Christopher K. I. Williams, Michael F. P. O'Boyle
Milepost GCC: Machine Learning Enabled Self-tuning Compiler. Int. J. Parallel Program. 39 (3) (2011)
Christophe Dubach, Timothy M. Jones, Edwin V. Bonilla, Michael F. P. O'Boyle
A Predictive Model for Dynamic Microarchitectural Adaptivity Control. MICRO (2010)
Edwin V. Bonilla, Shengbo Guo, Scott Sanner
Gaussian Process Preference Elicitation. NIPS (2010)
Christophe Dubach, Timothy M. Jones, Edwin V. Bonilla, Grigori Fursin, Michael F. P. O'Boyle
Portable compiler optimisation across embedded programs and microarchitectures using machine learning. MICRO (2009)
Hugh Leather, Edwin V. Bonilla, Michael F. P. O'Boyle
Automatic Feature Generation for Machine Learning Based Optimizing Compilation. CGO (2009)
Edwin V. Bonilla, Kian Ming Adam Chai, Christopher K. I. Williams
Multi-task Gaussian Process Prediction. NIPS (2007)
John Cavazos, Grigori Fursin, Felix V. Agakov, Edwin V. Bonilla, Michael F. P. O'Boyle, Olivier Temam
Rapidly Selecting Good Compiler Optimizations using Performance Counters. CGO (2007)
Edwin V. Bonilla, Felix V. Agakov, Christopher K. I. Williams
Kernel Multi-task Learning using Task-specific Features. AISTATS (2007)
Edwin V. Bonilla, Christopher K. I. Williams, Felix V. Agakov, John Cavazos, John Thomson, Michael F. P. O'Boyle
Predictive search distributions. ICML (2006)
John Cavazos, Christophe Dubach, Felix V. Agakov, Edwin V. Bonilla, Michael F. P. O'Boyle, Grigori Fursin, Olivier Temam
Automatic performance model construction for the fast software exploration of new hardware designs. CASES (2006)
Felix V. Agakov, Edwin V. Bonilla, John Cavazos, Björn Franke, Grigori Fursin, Michael F. P. O'Boyle, John Thomson, Marc Toussaint, Christopher K. I. Williams
Using Machine Learning to Focus Iterative Optimization. CGO (2006)

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Publication Activity (10 Years)

Top Topics

Top Venues

Edwin V. Bonilla