default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDEdwin V. Bonilla
Person information
- affiliation: Australian National University, Acton, USA
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
Books and Theses
- 2008
[b1]Edwin V. Bonilla:
Compilers that learn to optimise : a probabilistic machine learning approach. University of Edinburgh, UK, 2008
Journal Articles
- 2019
- [j5]Edwin V. Bonilla, Karl Krauth, Amir Dezfouli:
Generic Inference in Latent Gaussian Process Models. J. Mach. Learn. Res. 20: 117:1-117:63 (2019) - [j4]Astrid Dahl
, Edwin V. Bonilla:
Grouped Gaussian processes for solar power prediction. Mach. Learn. 108(8-9): 1287-1306 (2019) - 2014
- [j3]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): 14:1-14:32 (2014) - 2013
- [j2]Christophe Dubach, Timothy M. Jones, Edwin V. Bonilla:
Dynamic microarchitectural adaptation using machine learning. ACM Trans. Archit. Code Optim. 10(4): 31:1-31:28 (2013) - 2011
- [j1]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): 296-327 (2011)
Conference and Workshop Papers
- 2024
- [c48]Ryan Thompson, Edwin V. Bonilla, Robert Kohn:
Contextual Directed Acyclic Graphs. AISTATS 2024: 2872-2880 - [c47]Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Phung:
Optimal Transport for Structure Learning Under Missing Data. ICML 2024 - [c46]Vy Vo, Trung Le, Long Tung Vuong, He Zhao, Edwin V. Bonilla, Dinh Phung:
Parameter Estimation in DAGs from Incomplete Data via Optimal Transport. ICML 2024 - 2023
- [c45]Adrian N. Bishop, Edwin V. Bonilla:
Recurrent Neural Networks and Universal Approximation of Bayesian Filters. AISTATS 2023: 6956-6967 - [c44]Xuhui Fan, Edwin V. Bonilla, Terence J. O'Kane, Scott A. Sisson:
Free-Form Variational Inference for Gaussian Process State-Space Models. ICML 2023: 9603-9622 - [c43]He Zhao, Ke Sun, Amir Dezfouli, Edwin V. Bonilla:
Transformed Distribution Matching for Missing Value Imputation. ICML 2023: 42159-42186 - [c42]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: 2211-2222 - 2022
- [c41]Tom Blau, Edwin V. Bonilla, Iadine Chades, Amir Dezfouli:
Optimizing Sequential Experimental Design with Deep Reinforcement Learning. ICML 2022: 2107-2128 - [c40]Weiming Zhi, Tin Lai, Lionel Ott, Edwin V. Bonilla, Fabio Ramos:
Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks. ICML 2022: 27060-27074 - 2021
- [c39]Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone:
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations. AISTATS 2021: 1837-1845 - [c38]Maud Lemercier, Cristopher Salvi, Theodoros Damoulas, Edwin V. Bonilla, Terry J. Lyons:
Distribution Regression for Sequential Data. AISTATS 2021: 3754-3762 - [c37]Maud Lemercier, Cristopher Salvi, Thomas Cass, Edwin V. Bonilla, Theodoros Damoulas, Terry J. Lyons:
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data. ICML 2021: 6233-6242 - [c36]Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos:
BORE: Bayesian Optimization by Density-Ratio Estimation. ICML 2021: 10289-10300 - [c35]Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone:
Model Selection for Bayesian Autoencoders. NeurIPS 2021: 19730-19742 - 2020
- [c34]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 - [c33]Rui Zhang, Christian J. Walder, Edwin V. Bonilla, Marian-Andrei Rizoiu, Lexing Xie:
Quantile Propagation for Wasserstein-Approximate Gaussian Processes. NeurIPS 2020 - 2019
- [c32]Virginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla:
Efficient Inference in Multi-task Cox Process Models. AISTATS 2019: 537-546 - [c31]Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone:
Calibrating Deep Convolutional Gaussian Processes. AISTATS 2019: 1554-1563 - [c30]Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas, Sally Cripps:
Structured Variational Inference in Continuous Cox Process Models. NeurIPS 2019: 12437-12447 - 2018
- [c29]Amir Dezfouli, Edwin V. Bonilla, Richard Nock:
Variational Network Inference: Strong and Stable with Concrete Support. ICML 2018: 1212-1221 - 2017
- [c28]Pietro Galliani, Amir Dezfouli, Edwin V. Bonilla, Novi Quadrianto:
Gray-box Inference for Structured Gaussian Process Models. AISTATS 2017: 353-361 - [c27]Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi, Maurizio Filippone:
Random Feature Expansions for Deep Gaussian Processes. ICML 2017: 884-893 - [c26]Astrid Dahl, Edwin V. Bonilla:
Scalable Gaussian Process Models for Solar Power Forecasting. DARE@PKDD/ECML 2017: 94-106 - [c25]Karl Krauth, Edwin V. Bonilla, Kurt Cutajar, Maurizio Filippone:
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models. UAI 2017 - 2016
- [c24]Edwin V. Bonilla, Daniel M. Steinberg, Alistair Reid:
Extended and Unscented Kitchen Sinks. ICML 2016: 1651-1659 - 2015
- [c23]Amir Dezfouli, Edwin V. Bonilla:
Scalable Inference for Gaussian Process Models with Black-Box Likelihoods. NIPS 2015: 1414-1422 - 2014
- [c22]Trung V. Nguyen, Edwin V. Bonilla:
Fast Allocation of Gaussian Process Experts. ICML 2014: 145-153 - [c21]Daniel M. Steinberg, Edwin V. Bonilla:
Extended and Unscented Gaussian Processes. NIPS 2014: 1251-1259 - [c20]Trung V. Nguyen, Edwin V. Bonilla:
Automated Variational Inference for Gaussian Process Models. NIPS 2014: 1404-1412 - [c19]Trung V. Nguyen, Edwin V. Bonilla:
Collaborative Multi-output Gaussian Processes. UAI 2014: 643-652 - 2013
- [c18]Trung V. Nguyen, Edwin V. Bonilla:
Efficient Variational Inference for Gaussian Process Regression Networks. AISTATS 2013: 472-480 - [c17]Ehsan Abbasnejad, Scott Sanner, Edwin V. Bonilla, Pascal Poupart:
Learning Community-Based Preferences via Dirichlet Process Mixtures of Gaussian Processes. IJCAI 2013: 1213-1219 - [c16]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: 2877-2884 - [c15]M. Ehsan Abbasnejad, Edwin V. Bonilla, Scott Sanner:
Decision-Theoretic Sparsification for Gaussian Process Preference Learning. ECML/PKDD (2) 2013: 515-530 - 2012
- [c14]Marcela Zuluaga, Edwin V. Bonilla, Nigel P. Topham:
Predicting best design trade-offs: A case study in processor customization. DATE 2012: 1030-1035 - [c13]Edwin V. Bonilla, Antonio Robles-Kelly:
Discriminative Probabilistic Prototype Learning. ICML 2012 - [c12]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: 859-868 - 2011
- [c11]David Newman, Edwin V. Bonilla, Wray L. Buntine:
Improving Topic Coherence with Regularized Topic Models. NIPS 2011: 496-504 - 2010
- [c10]Christophe Dubach, Timothy M. Jones, Edwin V. Bonilla, Michael F. P. O'Boyle:
A Predictive Model for Dynamic Microarchitectural Adaptivity Control. MICRO 2010: 485-496 - [c9]Edwin V. Bonilla, Shengbo Guo, Scott Sanner:
Gaussian Process Preference Elicitation. NIPS 2010: 262-270 - 2009
- [c8]Hugh Leather, Edwin V. Bonilla, Michael F. P. O'Boyle:
Automatic Feature Generation for Machine Learning Based Optimizing Compilation. CGO 2009: 81-91 - [c7]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: 78-88 - 2007
- [c6]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: 185-197 - [c5]Edwin V. Bonilla, Kian Ming Adam Chai, Christopher K. I. Williams:
Multi-task Gaussian Process Prediction. NIPS 2007: 153-160 - [c4]Edwin V. Bonilla, Felix V. Agakov, Christopher K. I. Williams:
Kernel Multi-task Learning using Task-specific Features. AISTATS 2007: 43-50 - 2006
- [c3]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: 24-34 - [c2]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: 295-305 - [c1]Edwin V. Bonilla, Christopher K. I. Williams, Felix V. Agakov, John Cavazos, John Thomson, Michael F. P. O'Boyle:
Predictive search distributions. ICML 2006: 121-128
Informal and Other Publications
- 2024
- [i32]Edwin V. Bonilla, Pantelis Elinas, He Zhao, Maurizio Filippone, Vassili Kitsios, Terry O'Kane:
Variational DAG Estimation via State Augmentation With Stochastic Permutations. CoRR abs/2402.02644 (2024) - [i31]He Zhao, Edwin V. Bonilla:
Bayesian Factorised Granger-Causal Graphs For Multivariate Time-series Data. CoRR abs/2402.03614 (2024) - [i30]Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Q. Phung:
Optimal Transport for Structure Learning Under Missing Data. CoRR abs/2402.15255 (2024) - [i29]Rafael Oliveira, Dino Sejdinovic, David Howard, Edwin V. Bonilla:
Bayesian Adaptive Calibration and Optimal Design. CoRR abs/2405.14440 (2024) - [i28]Ryan Thompson, Edwin V. Bonilla, Robert Kohn:
ProDAG: Projection-induced variational inference for directed acyclic graphs. CoRR abs/2405.15167 (2024) - [i27]Xuesong Wang, He Zhao, Edwin V. Bonilla:
Rényi Neural Processes. CoRR abs/2405.15991 (2024) - [i26]Daniel M. Steinberg, Rafael Oliveira, Cheng Soon Ong, Edwin V. Bonilla:
Variational Search Distributions. CoRR abs/2409.06142 (2024) - 2023
- [i25]Xuhui Fan, Edwin V. Bonilla, Terence J. O'Kane, Scott A. Sisson:
Free-Form Variational Inference for Gaussian Process State-Space Models. CoRR abs/2302.09921 (2023) - [i24]He Zhao, Ke Sun, Amir Dezfouli, Edwin V. Bonilla:
Transformed Distribution Matching for Missing Value Imputation. CoRR abs/2302.10363 (2023) - [i23]Vy Vo, Trung Le, Long Tung Vuong, He Zhao, Edwin V. Bonilla, Dinh Q. Phung:
Learning Directed Graphical Models with Optimal Transport. CoRR abs/2305.15927 (2023) - [i22]Tom Blau, Edwin V. Bonilla, Iadine Chades, Amir Dezfouli:
Cross-Entropy Estimators for Sequential Experiment Design with Reinforcement Learning. CoRR abs/2305.18435 (2023) - [i21]Ryan Thompson, Edwin V. Bonilla, Robert Kohn:
Contextual directed acyclic graphs. CoRR abs/2310.15627 (2023) - 2022
- [i20]Tom Blau, Edwin V. Bonilla, Amir Dezfouli, Iadine Chades:
Optimizing Sequential Experimental Design with Deep Reinforcement Learning. CoRR abs/2202.00821 (2022) - [i19]Pantelis Elinas, Edwin V. Bonilla:
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision. CoRR abs/2202.12508 (2022) - [i18]Vy Vo, Trung Le, Van Nguyen, He Zhao, Edwin V. Bonilla, Gholamreza Haffari, Dinh Q. Phung:
Learning to Counter: Stochastic Feature-based Learning for Diverse Counterfactual Explanations. CoRR abs/2209.13446 (2022) - [i17]Adrian N. Bishop, Edwin V. Bonilla:
Recurrent Neural Networks and Universal Approximation of Bayesian Filters. CoRR abs/2211.00335 (2022) - 2021
- [i16]Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos:
BORE: Bayesian Optimization by Density-Ratio Estimation. CoRR abs/2102.09009 (2021) - [i15]Maud Lemercier, Cristopher Salvi, Thomas Cass, Edwin V. Bonilla, Theodoros Damoulas, Terry J. Lyons:
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data. CoRR abs/2105.04211 (2021) - [i14]Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone:
Model Selection for Bayesian Autoencoders. CoRR abs/2106.06245 (2021) - [i13]Weiming Zhi, Tin Lai, Lionel Ott, Edwin V. Bonilla, Fabio Ramos:
Learning ODEs via Diffeomorphisms for Fast and Robust Integration. CoRR abs/2107.01650 (2021) - 2020
- [i12]Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone:
Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations. CoRR abs/2003.03080 (2020) - [i11]Maud Lemercier, Cristopher Salvi, Theodoros Damoulas, Edwin V. Bonilla, Terry J. Lyons:
Distribution Regression for Continuous-Time Processes via the Expected Signature. CoRR abs/2006.05805 (2020) - 2019
- [i10]Astrid Dahl, Edwin V. Bonilla:
Sparse Grouped Gaussian Processes for Solar Power Forecasting. CoRR abs/1903.03986 (2019) - [i9]Louis C. Tiao, Pantelis Elinas, Harrison Nguyen, Edwin V. Bonilla:
Variational Spectral Graph Convolutional Networks. CoRR abs/1906.01852 (2019) - [i8]Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas, Sally Cripps:
Structured Variational Inference in Continuous Cox Process Models. CoRR abs/1906.03161 (2019) - [i7]Rui Zhang, Christian J. Walder, Edwin V. Bonilla, Marian-Andrei Rizoiu, Lexing Xie:
Quantile Propagation for Wasserstein-Approximate Gaussian Processes. CoRR abs/1912.10200 (2019) - 2018
- [i6]Virginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla:
Log Gaussian Cox Process Networks. CoRR abs/1805.09781 (2018) - [i5]Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone:
Calibrating Deep Convolutional Gaussian Processes. CoRR abs/1805.10522 (2018) - [i4]Louis C. Tiao, Edwin V. Bonilla, Fabio Ramos:
Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference. CoRR abs/1806.01771 (2018) - [i3]Astrid Dahl, Edwin V. Bonilla:
Grouped Gaussian Processes for Solar Power Prediction. CoRR abs/1806.02543 (2018) - 2017
- [i2]Amir Dezfouli, Edwin V. Bonilla, Richard Nock:
Semi-parametric Network Structure Discovery Models. CoRR abs/1702.08530 (2017) - 2012
- [i1]Edwin V. Bonilla, Antonio Robles-Kelly:
Discriminative Probabilistic Prototype Learning. CoRR abs/1206.4686 (2012)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-22 21:18 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
