Edwin V. Bonilla

100 papers A* 28A 15Misc 1Journal 47Unranked 8

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Active Flow Matching. CoRR Yashvir S. Grewal, Daniel M. Steinberg, Thang D. Bui, Cheng Soon Ong, Edwin V. Bonilla
2026	J	`jnl`	Causal Preference Elicitation. CoRR Edwin V. Bonilla, He Zhao, Daniel M. Steinberg
2026	J	`jnl`	HiBaNG: Hierarchical Bayesian Nonparametric Granger Causal Discovery in Low-Data Regimes. Trans. Mach. Learn. Res. He Zhao, Vassili Kitsios, Terence J. O'Kane, Edwin V. Bonilla
2026	J	`jnl`	Multi-Scale Wavelet Transformers for Operator Learning of Dynamical Systems. CoRR Xuesong Wang, Michael Groom, Rafael Oliveira, He Zhao, Terence J. O'Kane, Edwin V. Bonilla
2026	J	`jnl`	Ordering-based Causal Discovery via Generalized Score Matching. CoRR Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Phung
2026	J	`jnl`	Variational Learning of Fractional Posteriors. CoRR Kian Ming A. Chai, Edwin V. Bonilla
2025	J	`jnl`	Amortized Active Generation of Pareto Sets. CoRR Daniel M. Steinberg, Asiri Wijesinghe, Rafael Oliveira, Piotr Koniusz, Cheng Soon Ong, Edwin V. Bonilla
2025	J	`jnl`	Generative Bayesian Optimization: Generative Models as Acquisition Functions. CoRR Rafael Oliveira, Daniel M. Steinberg, Edwin V. Bonilla
2025	A*	`conf`	Rényi Neural Processes. ICML Xuesong Wang, He Zhao, Edwin V. Bonilla
2025	J	`jnl`	Thompson Sampling in Function Spaces via Neural Operators. CoRR Rafael Oliveira, Xuesong Wang, Kian Ming Adam Chai, Edwin V. Bonilla
2025	A*	`conf`	Variational Learning of Fractional Posteriors. ICML Kian Ming A. Chai, Edwin V. Bonilla
2025	A*	`conf`	Variational Search Distributions. ICLR Daniel M. Steinberg, Rafael Oliveira, Cheng Soon Ong, Edwin V. Bonilla
2024	A*	`conf`	Bayesian Adaptive Calibration and Optimal Design. NeurIPS Rafael Oliveira, Dino Sejdinovic, David Howard, Edwin V. Bonilla
2024	J	`jnl`	Bayesian Adaptive Calibration and Optimal Design. CoRR Rafael Oliveira, Dino Sejdinovic, David Howard, Edwin V. Bonilla
2024	J	`jnl`	Bayesian Factorised Granger-Causal Graphs For Multivariate Time-series Data. CoRR He Zhao, Edwin V. Bonilla
2024	A	`conf`	Contextual Directed Acyclic Graphs. AISTATS Ryan Thompson, Edwin V. Bonilla, Robert Kohn
2024	J	`jnl`	Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings. CoRR Yashvir S. Grewal, Edwin V. Bonilla, Thang D. Bui
2024	A*	`conf`	Optimal Transport for Structure Learning Under Missing Data. ICML Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Phung
2024	J	`jnl`	Optimal Transport for Structure Learning Under Missing Data. CoRR Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Q. Phung
2024	A*	`conf`	Parameter Estimation in DAGs from Incomplete Data via Optimal Transport. ICML Vy Vo, Trung Le, Long Tung Vuong, He Zhao, Edwin V. Bonilla, Dinh Phung
2024	J	`jnl`	ProDAG: Projection-induced variational inference for directed acyclic graphs. CoRR Ryan Thompson, Edwin V. Bonilla, Robert Kohn
2024	J	`jnl`	Rényi Neural Processes. CoRR Xuesong Wang, He Zhao, Edwin V. Bonilla
2024	J	`jnl`	Variational DAG Estimation via State Augmentation With Stochastic Permutations. CoRR Edwin V. Bonilla, Pantelis Elinas, He Zhao, Maurizio Filippone, Vassili Kitsios, Terry O'Kane
2024	J	`jnl`	Variational Search Distributions. CoRR Daniel M. Steinberg, Rafael Oliveira, Cheng Soon Ong, Edwin V. Bonilla
2023	J	`jnl`	Contextual directed acyclic graphs. CoRR Ryan Thompson, Edwin V. Bonilla, Robert Kohn
2023	J	`jnl`	Cross-Entropy Estimators for Sequential Experiment Design with Reinforcement Learning. CoRR Tom Blau, Edwin V. Bonilla, Iadine Chades, Amir Dezfouli
2023	A*	`conf`	Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations. KDD Vy Vo, Trung Le, Van Nguyen, He Zhao, Edwin V. Bonilla, Gholamreza Haffari, Dinh Q. Phung
2023	A*	`conf`	Free-Form Variational Inference for Gaussian Process State-Space Models. ICML Xuhui Fan, Edwin V. Bonilla, Terence J. O'Kane, Scott A. Sisson
2023	J	`jnl`	Free-Form Variational Inference for Gaussian Process State-Space Models. CoRR Xuhui Fan, Edwin V. Bonilla, Terence J. O'Kane, Scott A. Sisson
2023	J	`jnl`	Learning Directed Graphical Models with Optimal Transport. CoRR Vy Vo, Trung Le, Long Tung Vuong, He Zhao, Edwin V. Bonilla, Dinh Q. Phung
2023	A	`conf`	Recurrent Neural Networks and Universal Approximation of Bayesian Filters. AISTATS Adrian N. Bishop, Edwin V. Bonilla
2023	A*	`conf`	Transformed Distribution Matching for Missing Value Imputation. ICML He Zhao, Ke Sun, Amir Dezfouli, Edwin V. Bonilla
2023	J	`jnl`	Transformed Distribution Matching for Missing Value Imputation. CoRR He Zhao, Ke Sun, Amir Dezfouli, Edwin V. Bonilla
2022	J	`jnl`	Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision. CoRR Pantelis Elinas, Edwin V. Bonilla
2022	A*	`conf`	Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks. ICML Weiming Zhi, Tin Lai, Lionel Ott, Edwin V. Bonilla, Fabio Ramos
2022	J	`jnl`	Learning to Counter: Stochastic Feature-based Learning for Diverse Counterfactual Explanations. CoRR Vy Vo, Trung Le, Van Nguyen, He Zhao, Edwin V. Bonilla, Gholamreza Haffari, Dinh Q. Phung
2022	A*	`conf`	Optimizing Sequential Experimental Design with Deep Reinforcement Learning. ICML Tom Blau, Edwin V. Bonilla, Iadine Chades, Amir Dezfouli
2022	J	`jnl`	Optimizing Sequential Experimental Design with Deep Reinforcement Learning. CoRR Tom Blau, Edwin V. Bonilla, Amir Dezfouli, Iadine Chades
2022	J	`jnl`	Recurrent Neural Networks and Universal Approximation of Bayesian Filters. CoRR Adrian N. Bishop, Edwin V. Bonilla
2021	A*	`conf`	BORE: Bayesian Optimization by Density-Ratio Estimation. ICML Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos
2021	J	`jnl`	BORE: Bayesian Optimization by Density-Ratio Estimation. CoRR Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos
2021	A	`conf`	Distribution Regression for Sequential Data. AISTATS Maud Lemercier, Cristopher Salvi, Theodoros Damoulas, Edwin V. Bonilla, Terry J. Lyons
2021	J	`jnl`	Learning ODEs via Diffeomorphisms for Fast and Robust Integration. CoRR Weiming Zhi, Tin Lai, Lionel Ott, Edwin V. Bonilla, Fabio Ramos
2021	A*	`conf`	Model Selection for Bayesian Autoencoders. NeurIPS Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone
2021	J	`jnl`	Model Selection for Bayesian Autoencoders. CoRR Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone
2021	A*	`conf`	SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data. ICML Maud Lemercier, Cristopher Salvi, Thomas Cass, Edwin V. Bonilla, Theodoros Damoulas, Terry J. Lyons
2021	J	`jnl`	SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data. CoRR Maud Lemercier, Cristopher Salvi, Thomas Cass, Edwin V. Bonilla, Theodoros Damoulas, Terry J. Lyons
2021	A	`conf`	Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations. AISTATS Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone
2020	J	`jnl`	Distribution Regression for Continuous-Time Processes via the Expected Signature. CoRR Maud Lemercier, Cristopher Salvi, Theodoros Damoulas, Edwin V. Bonilla, Terry J. Lyons
2020	A*	`conf`	Quantile Propagation for Wasserstein-Approximate Gaussian Processes. NeurIPS Rui Zhang, Christian J. Walder, Edwin V. Bonilla, Marian-Andrei Rizoiu, Lexing Xie
2020	J	`jnl`	Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations. CoRR Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone
2020	A*	`conf`	Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings. NeurIPS Pantelis Elinas, Edwin V. Bonilla, Louis C. Tiao
2019	A	`conf`	Calibrating Deep Convolutional Gaussian Processes. AISTATS Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone
2019	A	`conf`	Efficient Inference in Multi-task Cox Process Models. AISTATS Virginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla
2019	J	`jnl`	Generic Inference in Latent Gaussian Process Models. J. Mach. Learn. Res. Edwin V. Bonilla, Karl Krauth, Amir Dezfouli
2019	J	`jnl`	Grouped Gaussian processes for solar power prediction. Mach. Learn. Astrid Dahl, Edwin V. Bonilla
2019	J	`jnl`	Quantile Propagation for Wasserstein-Approximate Gaussian Processes. CoRR Rui Zhang, Christian J. Walder, Edwin V. Bonilla, Marian-Andrei Rizoiu, Lexing Xie
2019	J	`jnl`	Sparse Grouped Gaussian Processes for Solar Power Forecasting. CoRR Astrid Dahl, Edwin V. Bonilla
2019	A*	`conf`	Structured Variational Inference in Continuous Cox Process Models. NeurIPS Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas, Sally Cripps
2019	J	`jnl`	Structured Variational Inference in Continuous Cox Process Models. CoRR Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas, Sally Cripps
2019	J	`jnl`	Variational Spectral Graph Convolutional Networks. CoRR Louis C. Tiao, Pantelis Elinas, Harrison Nguyen, Edwin V. Bonilla
2018	J	`jnl`	Calibrating Deep Convolutional Gaussian Processes. CoRR Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone
2018	J	`jnl`	Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference. CoRR Louis C. Tiao, Edwin V. Bonilla, Fabio Ramos
2018	J	`jnl`	Grouped Gaussian Processes for Solar Power Prediction. CoRR Astrid Dahl, Edwin V. Bonilla
2018	J	`jnl`	Log Gaussian Cox Process Networks. CoRR Virginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla
2018	A*	`conf`	Variational Network Inference: Strong and Stable with Concrete Support. ICML Amir Dezfouli, Edwin V. Bonilla, Richard Nock
2017	A	`conf`	AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models. UAI Karl Krauth, Edwin V. Bonilla, Kurt Cutajar, Maurizio Filippone
2017	A	`conf`	Gray-box Inference for Structured Gaussian Process Models. AISTATS Pietro Galliani, Amir Dezfouli, Edwin V. Bonilla, Novi Quadrianto
2017	A*	`conf`	Random Feature Expansions for Deep Gaussian Processes. ICML Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi, Maurizio Filippone
2017	—	`conf`	Scalable Gaussian Process Models for Solar Power Forecasting. DARE@PKDD/ECML Astrid Dahl, Edwin V. Bonilla
2017	J	`jnl`	Semi-parametric Network Structure Discovery Models. CoRR Amir Dezfouli, Edwin V. Bonilla, Richard Nock
2016	A*	`conf`	Extended and Unscented Kitchen Sinks. ICML Edwin V. Bonilla, Daniel M. Steinberg, Alistair Reid
2015	—	`conf`	Scalable Inference for Gaussian Process Models with Black-Box Likelihoods. NIPS Amir Dezfouli, Edwin V. Bonilla
2014	—	`conf`	Automated Variational Inference for Gaussian Process Models. NIPS Trung V. Nguyen, Edwin V. Bonilla
2014	J	`jnl`	Automatic feature generation for machine learning-based optimising compilation. ACM Trans. Archit. Code Optim. Hugh Leather, Edwin V. Bonilla, Michael F. P. O'Boyle
2014	A	`conf`	Collaborative Multi-output Gaussian Processes. UAI Trung V. Nguyen, Edwin V. Bonilla
2014	—	`conf`	Extended and Unscented Gaussian Processes. NIPS Daniel M. Steinberg, Edwin V. Bonilla
2014	A*	`conf`	Fast Allocation of Gaussian Process Experts. ICML Trung V. Nguyen, Edwin V. Bonilla
2013	A*	`conf`	Bayesian Joint Inversions for the Exploration of Earth Resources. IJCAI Alistair Reid, Simon Timothy O'Callaghan, Edwin V. Bonilla, Lachlan McCalman, Tim Rawling, Fabio Ramos
2013	—	`conf`	Decision-Theoretic Sparsification for Gaussian Process Preference Learning. ECML/PKDD (2) M. Ehsan Abbasnejad, Edwin V. Bonilla, Scott Sanner
2013	J	`jnl`	Dynamic microarchitectural adaptation using machine learning. ACM Trans. Archit. Code Optim. Christophe Dubach, Timothy M. Jones, Edwin V. Bonilla
2013	A	`conf`	Efficient Variational Inference for Gaussian Process Regression Networks. AISTATS Trung V. Nguyen, Edwin V. Bonilla
2013	A*	`conf`	Learning Community-Based Preferences via Dirichlet Process Mixtures of Gaussian Processes. IJCAI Ehsan Abbasnejad, Scott Sanner, Edwin V. Bonilla, Pascal Poupart
2012	J	`jnl`	Discriminative Probabilistic Prototype Learning CoRR Edwin V. Bonilla, Antonio Robles-Kelly
2012	A*	`conf`	Discriminative Probabilistic Prototype Learning. ICML Edwin V. Bonilla, Antonio Robles-Kelly
2012	A*	`conf`	New objective functions for social collaborative filtering. WWW Joseph Noel, Scott Sanner, Khoi-Nguyen Tran, Peter Christen, Lexing Xie, Edwin V. Bonilla, Ehsan Abbasnejad, Nicolás Della Penna
2012	A	`conf`	Predicting best design trade-offs: A case study in processor customization. DATE Marcela Zuluaga, Edwin V. Bonilla, Nigel P. Topham
2011	—	`conf`	Improving Topic Coherence with Regularized Topic Models. NIPS David Newman, Edwin V. Bonilla, Wray L. Buntine
2011	J	`jnl`	Milepost GCC: Machine Learning Enabled Self-tuning Compiler. Int. J. Parallel Program. 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
2010	A*	`conf`	A Predictive Model for Dynamic Microarchitectural Adaptivity Control. MICRO Christophe Dubach, Timothy M. Jones, Edwin V. Bonilla, Michael F. P. O'Boyle
2010	—	`conf`	Gaussian Process Preference Elicitation. NIPS Edwin V. Bonilla, Shengbo Guo, Scott Sanner
2009	A	`conf`	Automatic Feature Generation for Machine Learning Based Optimizing Compilation. CGO Hugh Leather, Edwin V. Bonilla, Michael F. P. O'Boyle
2009	A*	`conf`	Portable compiler optimisation across embedded programs and microarchitectures using machine learning. MICRO Christophe Dubach, Timothy M. Jones, Edwin V. Bonilla, Grigori Fursin, Michael F. P. O'Boyle
2008	—	`—`	Compilers that learn to optimise : a probabilistic machine learning approach. Edwin V. Bonilla
2007	A	`conf`	Kernel Multi-task Learning using Task-specific Features. AISTATS Edwin V. Bonilla, Felix V. Agakov, Christopher K. I. Williams
2007	—	`conf`	Multi-task Gaussian Process Prediction. NIPS Edwin V. Bonilla, Kian Ming Adam Chai, Christopher K. I. Williams
2007	A	`conf`	Rapidly Selecting Good Compiler Optimizations using Performance Counters. CGO John Cavazos, Grigori Fursin, Felix V. Agakov, Edwin V. Bonilla, Michael F. P. O'Boyle, Olivier Temam
2006	Misc	`conf`	Automatic performance model construction for the fast software exploration of new hardware designs. CASES John Cavazos, Christophe Dubach, Felix V. Agakov, Edwin V. Bonilla, Michael F. P. O'Boyle, Grigori Fursin, Olivier Temam
2006	A*	`conf`	Predictive search distributions. ICML Edwin V. Bonilla, Christopher K. I. Williams, Felix V. Agakov, John Cavazos, John Thomson, Michael F. P. O'Boyle
2006	A	`conf`	Using Machine Learning to Focus Iterative Optimization. CGO 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