publications

publications by categories in reversed chronological order. generated by jekyll-scholar. A comprehensive list of publications can be found in my Google scholar profile.

2024

  1. Contextual directed acyclic graphs
    Ryan Thompson, Edwin V Bonilla, and Robert Kohn
    In International Conference on Artificial Intelligence and Statistics (AISTATS) , 2024
  2. Variational DAG Estimation via State Augmentation With Stochastic Permutations
    Edwin V Bonilla, Pantelis Elinas, He Zhao, Maurizio Filippone, Vassili Kitsios, and Terry O’Kane
    arXiv preprint arXiv:2402.02644, 2024
  3. Bayesian Factorised Granger-Causal Graphs For Multivariate Time-series Data
    He Zhao, and Edwin V Bonilla
    arXiv preprint arXiv:2402.03614, 2024
  4. Optimal Transport for Structure Learning Under Missing Data
    Vy Vo, He Zhao, Trung Le, Edwin V Bonilla, and Dinh Phung
    Accepted for publication at International Conference on Machine Learning (ICML), 2024
  5. Bayesian Adaptive Calibration and Optimal Design
    Rafael Oliveira, Dino Sejdinovic, David Howard, and Edwin Bonilla
    arXiv preprint arXiv:2405.14440, 2024
  6. ProDAG: Projection-induced variational inference for directed acyclic graphs
    Ryan Thompson, Edwin V Bonilla, and Robert Kohn
    arXiv preprint arXiv:2405.15167, 2024
  7. Renyi Neural Processes
    Xuesong Wang, He Zhao, and Edwin V Bonilla
    arXiv preprint arXiv:2405.15991, 2024
  8. Parameter Estimation in DAGs from Incomplete Data via Optimal Transport
    Vy Vo, Trung Le, Long-Tung Vuong, He Zhao, Edwin Bonilla, and Dinh Phung
    In Accepted for publication at International Conference on Machine Learning (ICML) , 2024

2023

  1. Recurrent Neural Networks and Universal Approximation of Bayesian Filters
    Adrian N Bishop, and Edwin V Bonilla
    In International Conference on Artificial Intelligence and Statistics (AISTATS) , 2023
  2. Learning to Counter: Stochastic Feature-based Learning for Diverse Counterfactual Explanations
    Vy Vo, Trung Le, Van Nguyen, He Zhao, Edwin Bonilla, Gholamreza Haffari, and Dinh Phung
    In SIGKDD Conference on Knowledge Discovery and Data Mining - Research Track (KDD) , 2023
  3. Free-Form Variational Inference for Gaussian Process State-Space Models
    Xuhui Fan, Edwin V Bonilla, Terence J O’Kane, and Scott A Sisson
    In International Conference on Machine Learning (ICML) , 2023
  4. Transformed Distribution Matching for Missing Value Imputation
    He Zhao, Ke Sun, Amir Dezfouli, and Edwin Bonilla
    In International Conference on Machine Learning (ICML) , 2023
  5. Cross-Entropy Estimators for Sequential Experiment Design with Reinforcement Learning
    Tom Blau, Edwin Bonilla, Iadine Chades, and Amir Dezfouli
    arXiv preprint arXiv:2305.18435, 2023
  6. Feature-based learning for diverse and privacy-preserving counterfactual explanations
    Vy Vo, Trung Le, Van Nguyen, He Zhao, Edwin V Bonilla, Gholamreza Haffari, and Dinh Phung
    In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2023

2022

  1. Optimizing Sequential Experimental Design with Deep Reinforcement Learning
    Tom Blau, Edwin Bonilla, Amir Dezfouli, and Iadine Chades
    In International Conference on Machine Learning (ICML) , 2022
  2. Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks
    Weiming Zhi, Tin Lai, Lionel Ott, Edwin V Bonilla, and Fabio Ramos
    In International Conference on Machine Learning (ICML) , 2022
  3. Addressing over-smoothing in graph neural networks via deep supervision
    Pantelis Elinas, and Edwin V Bonilla
    arXiv preprint arXiv:2202.12508, 2022
  4. Revisiting Over-smoothing in Graph Neural Networks
    Pantelis Elinas, and Edwin V Bonilla
    Technical report, 2022

2021

  1. BORE: Bayesian Optimization by Density-Ratio Estimation
    Louis C Tiao, Aaron Klein, Matthias Seeger, Edwin V Bonilla, Cedric Archambeau, and Fabio Ramos
    In International Conference on Machine Learning (ICML) , 2021
  2. Sparse Gaussian processes revisited: Bayesian approaches to inducing-variable approximations
    Simone Rossi, Markus Heinonen, Edwin Bonilla, Zheyang Shen, and Maurizio Filippone
    In International Conference on Artificial Intelligence and Statistics (AISTATS) , 2021
  3. Distribution regression for sequential data
    Maud Lemercier, Cristopher Salvi, Theodoros Damoulas, Edwin Bonilla, and Terry Lyons
    In International Conference on Artificial Intelligence and Statistics (AISTATS) , 2021
  4. SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data
    Maud Lemercier, Cristopher Salvi, Thomas Cass, Edwin V Bonilla, Theodoros Damoulas, and Terry Lyons
    In International Conference on Machine Learning (ICML) , 2021
  5. Model Selection for Bayesian Autoencoders
    Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V Bonilla, and Maurizio Filippone
    In Advances in Neural Information Processing Systems (NeurIPS) , 2021
  6. Learning odes via diffeomorphisms for fast and robust integration
    Weiming Zhi, Tin Lai, Lionel Ott, Edwin V Bonilla, and Fabio Ramos
    arXiv preprint arXiv:2107.01650, 2021

2020

  1. Quantile Propagation for Wasserstein-Approximate Gaussian Processes
    Rui Zhang, Christian J Walder, Edwin V Bonilla, Marian-Andrei Rizoiu, and Lexing Xie
    In Advances in Neural Information Processing Systems (NeurIPS) , 2020
  2. Rethinking sparse gaussian processes: Bayesian approaches to inducing-variable approximations
    Simone Rossi, Markus Heinonen, Edwin Bonilla, Zheyang Shen, and Maurizio Filippone
    arXiv preprint arXiv:2003.03080, 2020
  3. Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
    Pantelis Elinas, Edwin V Bonilla, and Louis Tiao
    In Advances in Neural Information Processing Systems (NeurIPS) , 2020

2019

  1. Calibrating Deep Convolutional Gaussian Processes
    Gia-Lac Tran, Edwin V Bonilla, John P Cunningham, Pietro Michiardi, and Maurizio Filippone
    In International Conference on Artificial Intelligence and Statistics (AISTATS) , 2019
  2. Sparse Grouped Gaussian Processes for Solar Power Forecasting.
    Astrid Dahl, and Edwin V Bonilla
    CoRR, 2019
  3. Efficient Inference in Multi-task Cox Process Models
    Virginia Aglietti, Theodoros Damoulas, and Edwin Bonilla
    In International Conference on Artificial Intelligence and Statistics (AISTATS) , 2019
  4. Variational Graph Convolutional Networks
    Louis Tiao, Pantelis Elinas, Harrison Nguyen, and Edwin V Bonilla
    In NeurIPS Workshop on Graph Representational Learning , 2019
  5. Structured Variational Inference in Continuous Cox Process Models
    Virginia Aglietti, Edwin V Bonilla, Theodoros Damoulas, and Sally Cripps
    In Advances in Neural Information Processing Systems (NeurIPS) , 2019
  6. Generic Inference in Latent Gaussian Process Models.
    Edwin V Bonilla, Karl Krauth, and Amir Dezfouli
    Journal of Machine Learning Research (JMLR), 2019
  7. Scalable grouped Gaussian processes via direct Cholesky functional representations
    Astrid Dahl, and Edwin V Bonilla
    arXiv preprint arXiv:1903.03986, 2019
  8. Grouped Gaussian processes for solar power prediction
    Astrid Dahl, and Edwin V Bonilla
    Machine Learning, 2019

2018

  1. Cycle-consistent adversarial learning as approximate bayesian inference
    Louis C Tiao, Edwin V Bonilla, and Fabio Ramos
    arXiv preprint arXiv:1806.01771, 2018
  2. Variational Network Inference: Strong and Stable with Concrete Support
    Amir Dezfouli, Edwin Bonilla, and Richard Nock
    In International Conference on Machine Learning (ICML) , 2018

2017

  1. Gray-box inference for structured Gaussian process models
    Pietro Galliani, Amir Dezfouli, Edwin V Bonilla, and Novi Quadrianto
    In International Conference on Artificial Intelligence and Statistics (AISTATS) , 2017
  2. AutoGP: Exploring the capabilities and limitations of Gaussian process models
    Karl Krauth, Edwin V Bonilla, Kurt Cutajar, and Maurizio Filippone
    In Uncertainty in Artificial Intelligence (UAI) , 2017
  3. Random feature expansions for deep Gaussian processes
    Kurt Cutajar, Edwin V Bonilla, Pietro Michiardi, and Maurizio Filippone
    In International Conference on Machine Learning (ICML) , 2017
  4. Scalable Gaussian process models for solar power forecasting
    Astrid Dahl, and Edwin Bonilla
    In Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy: 5th ECML PKDD Workshop, DARE 2017, Skopje, Macedonia, September 22, 2017, Revised Selected Papers 5 , 2017

2016

  1. Extended and unscented kitchen sinks
    Edwin Bonilla, Daniel Steinberg, and Alistair Reid
    In International Conference on Machine Learning (ICML) , 2016
  2. Accelerating Deep Gaussian Processes Inference with Arc-Cosine Kernels
    Kurt Cutajar, Edwin V Bonilla, Pietro Michiardi, and Maurizio Filippone
    In NIPS Workshop on Bayesian Deep Learning , 2016

2015

  1. Scalable inference for Gaussian process models with black-box likelihoods
    Amir Dezfouli, and Edwin V Bonilla
    Advances in Neural Information Processing Systems (NeurIPS), 2015

2014

  1. Fast allocation of Gaussian process experts
    Trung Nguyen, and Edwin Bonilla
    In International Conference on Machine Learning (ICML) , 2014
  2. Distributed Bayesian geophysical inversions
    Lachlan McCalman, Simon T O’Callaghan, Alistair Reid, Darren Shen, Simon Carter, Lars Krieger, GR Beardsmore, Edwin V Bonilla, and Fabio T Ramos
    In Thirty-Ninth Stanford geothermal workshop , 2014
  3. Automatic feature generation for machine learning–based optimising compilation
    Hugh Leather, Edwin Bonilla, and Michael O’boyle
    ACM Transactions on Architecture and Code Optimization, 2014
  4. Inference Engine Final Report to ARENA
    Edwin Bonilla, Lachlan McCalman, Simon O’Callaghan, Fabio Ramos, Alistair Reid, and Marie Connett
    2014
  5. Collaborative Multi-output Gaussian Processes.
    Trung V Nguyen, and Edwin V Bonilla
    In Uncertainty in Artificial Intelligence (UAI) , 2014
  6. Extended and unscented Gaussian processes
    Daniel M Steinberg, and Edwin V Bonilla
    In Advances in Neural Information Processing Systems (NeurIPS) , 2014
  7. Automated variational inference for Gaussian process models
    Trung V Nguyen, and Edwin V Bonilla
    In Advances in Neural Information Processing Systems (NeurIPS) , 2014

2013

  1. Learning community-based preferences via dirichlet process mixtures of gaussian processes
    Ehsan Abbasnejad, Scott Sanner, Edwin V Bonilla, and Pascal Poupart
    In International joint conference on artificial intelligence , 2013
  2. Efficient Variational Inference for Gaussian Process Regression Networks
    Trung Nguyen, and Edwin Bonilla
    In International Conference on Artificial Intelligence and Statistics (AISTATS) , 2013
  3. Decision-theoretic sparsification for Gaussian process preference learning
    M Ehsan Abbasnejad, Edwin V Bonilla, and Scott Sanner
    In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II 13 , 2013
  4. Bayesian joint inversions for the exploration of earth resources
    Alistair Smyth Reid, Simon O’Callaghan, Edwin Bonilla, Lachlan McCalman, Tim Rawling, and Fabio Ramos
    In Twenty-Third International Joint Conference on Artificial Intelligence , 2013
  5. Dynamic microarchitectural adaptation using machine learning
    Christophe Dubach, Timothy M Jones, and Edwin V Bonilla
    ACM Transactions on Architecture and Code Optimization (TACO), 2013

2012

  1. New objective functions for social collaborative filtering
    Joseph Noel, Scott Sanner, Khoi-Nguyen Tran, Peter Christen, Lexing Xie, Edwin V Bonilla, Ehsan Abbasnejad, and Nicolás Della Penna
    In Proceedings of the 21st international conference on World Wide Web , 2012
  2. Predicting best design trade-offs: A case study in processor customization
    Marcela Zuluaga, Edwin Bonilla, and Nigel Topham
    In 2012 Design, Automation & Test in Europe Conference & Exhibition (DATE) , 2012
  3. Discriminative probabilistic prototype learning
    Edwin Bonilla, and Antonio Robles-Kelly
    In International Conference on Machine Learning (ICML) , 2012
  4. Bayesian data fusion for geothermal exploration
    FT Ramos, Edwin Bonilla, L McCalman, S O’Callaghan, A Reid, William TB Uther, Malcolm Sambridge, Tim Rawling, and  others
    Australian Geothermal Energy Conference, 2012

2011

  1. 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, and  others
    International journal of parallel programming, 2011
  2. Improving Topic Coherence with Regularized Topic Models
    David Newman, Edwin V Bonilla, and Wray Buntine
    In Advances in Neural Information Processing Systems (NeurIPS) , 2011
  3. Sparse gaussian processes for learning preferences
    M Ehsan Abbasnejad, Edwin V Bonilla, Scott Sanner, and  others
    In Proceedings of Workshop on Choice Models and Preference Learning (CMPL) , 2011

2010

  1. Gaussian process preference elicitation
    Edwin V Bonilla, Shengbo Guo, and Scott Sanner
    In Advances in Neural Information Processing Systems (NeurIPS) , 2010
  2. A predictive model for dynamic microarchitectural adaptivity control
    Christophe Dubach, Timothy M Jones, Edwin V Bonilla, and Michael FP O’Boyle
    In 2010 43rd Annual IEEE/ACM International Symposium on Microarchitecture , 2010

2009

  1. Automatic feature generation for machine learning based optimizing compilation
    Hugh Leather, Edwin Bonilla, and Michael O’Boyle
    In 2009 International Symposium on Code Generation and Optimization , 2009
  2. Portable compiler optimisation across embedded programs and microarchitectures using machine learning
    Christophe Dubach, Timothy M Jones, Edwin V Bonilla, Grigori Fursin, and Michael FP O’Boyle
    In Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture , 2009

2008

  1. MILEPOST GCC: machine learning based research compiler
    Grigori Fursin, Cupertino Miranda, Olivier Temam, Mircea Namolaru, Elad Yom-Tov, Ayal Zaks, Bilha Mendelson, Edwin Bonilla, John Thomson, Hugh Leather, and  others
    In GCC Summit , 2008
  2. Compilers that learn to optimise: a probabilistic machine learning approach
    Edwin Vladimir Bonilla
    The University of Edinburgh , 2008

2007

  1. Multi-task Gaussian process prediction
    Edwin V Bonilla, Chris Williams, and Kian M Chai
    In Advances in Neural Information Processing Systems (NeurIPS) , 2007
  2. Rapidly selecting good compiler optimizations using performance counters
    John Cavazos, Grigori Fursin, Felix Agakov, Edwin Bonilla, Michael FP O’Boyle, and Olivier Temam
    In International Symposium on Code Generation and Optimization , 2007
  3. Kernel multi-task learning using task-specific features
    Edwin V Bonilla, Felix V Agakov, and Christopher KI Williams
    In International Conference on Artificial Intelligence and Statistics (AISTATS) , 2007
  4. A note on noise-free Gaussian process prediction with separable covariance functions and grid designs
    Christopher KI Williams, Kian Ming A Chai, and Edwin V Bonilla
    2007

2006

  1. Using machine learning to focus iterative optimization
    Felix Agakov, Edwin Bonilla, John Cavazos, Björn Franke, Grigori Fursin, Michael FP O’Boyle, John Thomson, Marc Toussaint, and Christopher KI Williams
    In International Symposium on Code Generation and Optimization , 2006
  2. Automatic performance model construction for the fast software exploration of new hardware designs
    John Cavazos, Christophe Dubach, Felix Agakov, Edwin Bonilla, Michael FP O’Boyle, Grigori Fursin, and Olivier Temam
    In International conference on Compilers, architecture and synthesis for embedded systems , 2006
  3. Predictive search distributions
    Edwin V Bonilla, Christopher KI Williams, Felix V Agakov, John Cavazos, John Thomson, and Michael FP O’Boyle
    In International Conference on Machine Learning (ICML) , 2006

2004

  1. Predicting Good Compiler Transformations Using Machine Learning
    Edwin V Bonilla
    The University of Edinburgh , 2004