Edwin V. Bonilla

According to our database, Edwin V. Bonilla authored at least 73 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings.

Yashvir S. Grewal, Edwin V. Bonilla, Thang D. Bui

CoRR, 2024

Variational Search Distributions.

Daniel M. Steinberg, Rafael Oliveira, Cheng Soon Ong, Edwin V. Bonilla

CoRR, 2024

Rényi Neural Processes.

Xuesong Wang, He Zhao, Edwin V. Bonilla

CoRR, 2024

ProDAG: Projection-induced variational inference for directed acyclic graphs.

Ryan Thompson, Edwin V. Bonilla, Robert Kohn

CoRR, 2024

Bayesian Adaptive Calibration and Optimal Design.

Rafael Oliveira, Dino Sejdinovic, David Howard, Edwin V. Bonilla

CoRR, 2024

Bayesian Factorised Granger-Causal Graphs For Multivariate Time-series Data.

He Zhao, Edwin V. Bonilla

CoRR, 2024

Variational DAG Estimation via State Augmentation With Stochastic Permutations.

Edwin V. Bonilla, Pantelis Elinas, He Zhao, Maurizio Filippone, Vassili Kitsios, Terry O'Kane

CoRR, 2024

Parameter Estimation in DAGs from Incomplete Data via Optimal Transport.

Vy Vo, Trung Le, Long Tung Vuong, He Zhao, Edwin V. Bonilla, Dinh Phung

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Optimal Transport for Structure Learning Under Missing Data.

Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Phung

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Contextual Directed Acyclic Graphs.

Ryan Thompson, Edwin V. Bonilla, Robert Kohn

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Cross-Entropy Estimators for Sequential Experiment Design with Reinforcement Learning.

Tom Blau, Edwin V. Bonilla, Iadine Chades, Amir Dezfouli

CoRR, 2023

Learning Directed Graphical Models with Optimal Transport.

Vy Vo, Trung Le, Long Tung Vuong, He Zhao, Edwin V. Bonilla, Dinh Q. Phung

CoRR, 2023

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

Vy Vo, Trung Le, Van Nguyen, He Zhao, Edwin V. Bonilla, Gholamreza Haffari, Dinh Q. Phung

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

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

Xuhui Fan, Edwin V. Bonilla, Terence J. O'Kane, Scott A. Sisson

Proceedings of the International Conference on Machine Learning, 2023

Transformed Distribution Matching for Missing Value Imputation.

He Zhao, Ke Sun, Amir Dezfouli, Edwin V. Bonilla

Proceedings of the International Conference on Machine Learning, 2023

Recurrent Neural Networks and Universal Approximation of Bayesian Filters.

Adrian N. Bishop, Edwin V. Bonilla

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

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

Vy Vo, Trung Le, Van Nguyen, He Zhao, Edwin V. Bonilla, Gholamreza Haffari, Dinh Q. Phung

CoRR, 2022

Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision.

Pantelis Elinas, Edwin V. Bonilla

CoRR, 2022

Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks.

Weiming Zhi, Tin Lai, Lionel Ott, Edwin V. Bonilla, Fabio Ramos

Proceedings of the International Conference on Machine Learning, 2022

Optimizing Sequential Experimental Design with Deep Reinforcement Learning.

Tom Blau, Edwin V. Bonilla, Iadine Chades, Amir Dezfouli

Proceedings of the International Conference on Machine Learning, 2022

2021

Learning ODEs via Diffeomorphisms for Fast and Robust Integration.

Weiming Zhi, Tin Lai, Lionel Ott, Edwin V. Bonilla, Fabio Ramos

CoRR, 2021

Model Selection for Bayesian Autoencoders.

Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

BORE: Bayesian Optimization by Density-Ratio Estimation.

Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos

Proceedings of the 38th International Conference on Machine Learning, 2021

SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data.

Maud Lemercier, Cristopher Salvi, Thomas Cass, Edwin V. Bonilla, Theodoros Damoulas, Terry J. Lyons

Proceedings of the 38th International Conference on Machine Learning, 2021

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

Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Distribution Regression for Sequential Data.

Maud Lemercier, Cristopher Salvi, Theodoros Damoulas, Edwin V. Bonilla, Terry J. Lyons

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

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

Maud Lemercier, Cristopher Salvi, Theodoros Damoulas, Edwin V. Bonilla, Terry J. Lyons

CoRR, 2020

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

Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone

CoRR, 2020

Quantile Propagation for Wasserstein-Approximate Gaussian Processes.

Rui Zhang, Christian J. Walder, Edwin V. Bonilla, Marian-Andrei Rizoiu, Lexing Xie

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Grouped Gaussian processes for solar power prediction.

Astrid Dahl, Edwin V. Bonilla

Mach. Learn., 2019

Generic Inference in Latent Gaussian Process Models.

Edwin V. Bonilla, Karl Krauth, Amir Dezfouli

J. Mach. Learn. Res., 2019

Variational Spectral Graph Convolutional Networks.

Louis C. Tiao, Pantelis Elinas, Harrison Nguyen, Edwin V. Bonilla

CoRR, 2019

Sparse Grouped Gaussian Processes for Solar Power Forecasting.

Astrid Dahl, Edwin V. Bonilla

CoRR, 2019

Structured Variational Inference in Continuous Cox Process Models.

Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas, Sally Cripps

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Calibrating Deep Convolutional Gaussian Processes.

Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Efficient Inference in Multi-task Cox Process Models.

Virginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference.

Louis C. Tiao, Edwin V. Bonilla, Fabio Ramos

CoRR, 2018

Log Gaussian Cox Process Networks.

Virginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla

CoRR, 2018

Variational Network Inference: Strong and Stable with Concrete Support.

Amir Dezfouli, Edwin V. Bonilla, Richard Nock

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Semi-parametric Network Structure Discovery Models.

Amir Dezfouli, Edwin V. Bonilla, Richard Nock

CoRR, 2017

AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models.

Karl Krauth, Edwin V. Bonilla, Kurt Cutajar, Maurizio Filippone

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Scalable Gaussian Process Models for Solar Power Forecasting.

Astrid Dahl, Edwin V. Bonilla

Proceedings of the Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy, 2017

Random Feature Expansions for Deep Gaussian Processes.

Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi, Maurizio Filippone

Proceedings of the 34th International Conference on Machine Learning, 2017

Gray-box Inference for Structured Gaussian Process Models.

Pietro Galliani, Amir Dezfouli, Edwin V. Bonilla, Novi Quadrianto

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Extended and Unscented Kitchen Sinks.

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

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Scalable Inference for Gaussian Process Models with Black-Box Likelihoods.

Amir Dezfouli, Edwin V. Bonilla

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Automatic feature generation for machine learning-based optimising compilation.

Hugh Leather, Edwin V. Bonilla, Michael F. P. O'Boyle

ACM Trans. Archit. Code Optim., 2014

Collaborative Multi-output Gaussian Processes.

Trung V. Nguyen, Edwin V. Bonilla

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Extended and Unscented Gaussian Processes.

Daniel M. Steinberg, Edwin V. Bonilla

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Automated Variational Inference for Gaussian Process Models.

Trung V. Nguyen, Edwin V. Bonilla

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Fast Allocation of Gaussian Process Experts.

Trung V. Nguyen, Edwin V. Bonilla

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Dynamic microarchitectural adaptation using machine learning.

Christophe Dubach, Timothy M. Jones, Edwin V. Bonilla

ACM Trans. Archit. Code Optim., 2013

Decision-Theoretic Sparsification for Gaussian Process Preference Learning.

M. Ehsan Abbasnejad, Edwin V. Bonilla, Scott Sanner

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

Bayesian Joint Inversions for the Exploration of Earth Resources.

Alistair Reid, Simon Timothy O'Callaghan, Edwin V. Bonilla, Lachlan McCalman, Tim Rawling, Fabio Ramos

Proceedings of the IJCAI 2013, 2013

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

Ehsan Abbasnejad, Scott Sanner, Edwin V. Bonilla, Pascal Poupart

Proceedings of the IJCAI 2013, 2013

Efficient Variational Inference for Gaussian Process Regression Networks.

Trung V. Nguyen, Edwin V. Bonilla

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012

New objective functions for social collaborative filtering.

Joseph Noel, Scott Sanner, Khoi-Nguyen Tran, Peter Christen, Lexing Xie, Edwin V. Bonilla, Ehsan Abbasnejad, Nicolás Della Penna

Proceedings of the 21st World Wide Web Conference 2012, 2012

Discriminative Probabilistic Prototype Learning.

Edwin V. Bonilla, Antonio Robles-Kelly

Proceedings of the 29th International Conference on Machine Learning, 2012

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

Marcela Zuluaga, Edwin V. Bonilla, Nigel P. Topham

Proceedings of the 2012 Design, Automation & Test in Europe Conference & Exhibition, 2012

2011

Milepost GCC: Machine Learning Enabled Self-tuning Compiler.

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

Int. J. Parallel Program., 2011

Improving Topic Coherence with Regularized Topic Models.

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

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

Gaussian Process Preference Elicitation.

Edwin V. Bonilla, Shengbo Guo, Scott Sanner

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

A Predictive Model for Dynamic Microarchitectural Adaptivity Control.

Christophe Dubach, Timothy M. Jones, Edwin V. Bonilla, Michael F. P. O'Boyle

Proceedings of the 43rd Annual IEEE/ACM International Symposium on Microarchitecture, 2010

2009

Portable compiler optimisation across embedded programs and microarchitectures using machine learning.

Christophe Dubach, Timothy M. Jones, Edwin V. Bonilla, Grigori Fursin, Michael F. P. O'Boyle

Proceedings of the 42st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO-42 2009), 2009

Automatic Feature Generation for Machine Learning Based Optimizing Compilation.

Hugh Leather, Edwin V. Bonilla, Michael F. P. O'Boyle

Proceedings of the CGO 2009, 2009

2008

Compilers that learn to optimise : a probabilistic machine learning approach.

Edwin V. Bonilla

PhD thesis, 2008

2007

Kernel Multi-task Learning using Task-specific Features.

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

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Multi-task Gaussian Process Prediction.

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

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Rapidly Selecting Good Compiler Optimizations using Performance Counters.

John Cavazos, Grigori Fursin, Felix V. Agakov, Edwin V. Bonilla, Michael F. P. O'Boyle, Olivier Temam

Proceedings of the Fifth International Symposium on Code Generation and Optimization (CGO 2007), 2007

2006

Predictive search distributions.

Edwin V. Bonilla, Christopher K. I. Williams, Felix V. Agakov, John Cavazos, John Thomson, Michael F. P. O'Boyle

Proceedings of the Machine Learning, 2006

Using Machine Learning to Focus Iterative Optimization.

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

Proceedings of the Fourth IEEE/ACM International Symposium on Code Generation and Optimization (CGO 2006), 2006

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

John Cavazos, Christophe Dubach, Felix V. Agakov, Edwin V. Bonilla, Michael F. P. O'Boyle, Grigori Fursin, Olivier Temam

Proceedings of the 2006 International Conference on Compilers, 2006