Bonilla, Edwin V.

44 publications

NeurIPS 2025 Amortized Active Generation of Pareto Sets Daniel M. Steinberg, Asiri Wijesinghe, Rafael Oliveira, Piotr Koniusz, Cheng Soon Ong, Edwin V. Bonilla

NeurIPS 2025 ProDAG: Projected Variational Inference for Directed Acyclic Graphs Ryan Thompson, Edwin V. Bonilla, Robert Kohn

ICML 2025 Rényi Neural Processes Xuesong Wang, He Zhao, Edwin V. Bonilla

NeurIPS 2025 Thompson Sampling in Function Spaces via Neural Operators Rafael Oliveira, Xuesong Wang, Kian Ming A. Chai, Edwin V. Bonilla

ICML 2025 Variational Learning of Fractional Posteriors Kian Ming A. Chai, Edwin V. Bonilla

ICLR 2025 Variational Search Distributions Daniel M. Steinberg, Rafael Oliveira, Cheng Soon Ong, Edwin V. Bonilla

NeurIPS 2024 Bayesian Adaptive Calibration and Optimal Design Rafael Oliveira, Dino Sejdinovic, David Howard, Edwin V. Bonilla

AISTATS 2024 Contextual Directed Acyclic Graphs Ryan Thompson, Edwin V. Bonilla, Robert Kohn

ICML 2024 Optimal Transport for Structure Learning Under Missing Data Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Phung

ICML 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

NeurIPSW 2024 Variational Search Distributions Daniel M. Steinberg, Rafael Oliveira, Cheng Soon Ong, Edwin V. Bonilla

NeurIPSW 2023 Cross-Entropy Estimators for Sequential Experiment Design with Reinforcement Learning Tom Blau, Iadine Chades, Amir Dezfouli, Daniel M Steinberg, Edwin V. Bonilla

ICML 2023 Free-Form Variational Inference for Gaussian Process State-Space Models Xuhui Fan, Edwin V. Bonilla, Terence O’Kane, Scott A Sisson

AISTATS 2023 Recurrent Neural Networks and Universal Approximation of Bayesian Filters Adrian N. Bishop, Edwin V. Bonilla

ICML 2023 Transformed Distribution Matching for Missing Value Imputation He Zhao, Ke Sun, Amir Dezfouli, Edwin V. Bonilla

ICML 2022 Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks Weiming Zhi, Tin Lai, Lionel Ott, Edwin V. Bonilla, Fabio Ramos

ICML 2022 Optimizing Sequential Experimental Design with Deep Reinforcement Learning Tom Blau, Edwin V. Bonilla, Iadine Chades, Amir Dezfouli

ICML 2021 BORE: Bayesian Optimization by Density-Ratio Estimation Louis C Tiao, Aaron Klein, Matthias W Seeger, Edwin V. Bonilla, Cedric Archambeau, Fabio Ramos

NeurIPS 2021 Model Selection for Bayesian Autoencoders Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone

ICML 2021 SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data Maud Lemercier, Cristopher Salvi, Thomas Cass, Edwin V. Bonilla, Theodoros Damoulas, Terry J Lyons

NeurIPS 2020 Quantile Propagation for Wasserstein-Approximate Gaussian Processes Rui Zhang, Christian Walder, Edwin V. Bonilla, Marian-Andrei Rizoiu, Lexing Xie

NeurIPS 2020 Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings Pantelis Elinas, Edwin V. Bonilla, Louis Tiao

AISTATS 2019 Calibrating Deep Convolutional Gaussian Processes Gia-Lac Tran, Edwin V. Bonilla, John Cunningham, Pietro Michiardi, Maurizio Filippone

AISTATS 2019 Efficient Inference in Multi-Task Cox Process Models Virginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla

JMLR 2019 Generic Inference in Latent Gaussian Process Models Edwin V. Bonilla, Karl Krauth, Amir Dezfouli

MLJ 2019 Grouped Gaussian Processes for Solar Power Prediction Astrid Dahl, Edwin V. Bonilla

NeurIPS 2019 Structured Variational Inference in Continuous Cox Process Models Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas, Sally Cripps

UAI 2017 AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models Karl Krauth, Edwin V. Bonilla, Kurt Cutajar, Maurizio Filippone

AISTATS 2017 Gray-Box Inference for Structured Gaussian Process Models Pietro Galliani, Amir Dezfouli, Edwin V. Bonilla, Novi Quadrianto

ICML 2017 Random Feature Expansions for Deep Gaussian Processes Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi, Maurizio Filippone

NeurIPS 2015 Scalable Inference for Gaussian Process Models with Black-Box Likelihoods Amir Dezfouli, Edwin V. Bonilla

NeurIPS 2014 Automated Variational Inference for Gaussian Process Models Trung V Nguyen, Edwin V. Bonilla

UAI 2014 Collaborative Multi-Output Gaussian Processes Trung V. Nguyen, Edwin V. Bonilla

NeurIPS 2014 Extended and Unscented Gaussian Processes Daniel M Steinberg, Edwin V. Bonilla

IJCAI 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

ECML-PKDD 2013 Decision-Theoretic Sparsification for Gaussian Process Preference Learning M. Ehsan Abbasnejad, Edwin V. Bonilla, Scott Sanner

AISTATS 2013 Efficient Variational Inference for Gaussian Process Regression Networks Trung V. Nguyen, Edwin V. Bonilla

IJCAI 2013 Learning Community-Based Preferences via Dirichlet Process Mixtures of Gaussian Processes Ehsan Abbasnejad, Scott Sanner, Edwin V. Bonilla, Pascal Poupart

ICML 2012 Discriminative Probabilistic Prototype Learning Edwin V. Bonilla, Antonio Robles-Kelly

NeurIPS 2011 Improving Topic Coherence with Regularized Topic Models David Newman, Edwin V. Bonilla, Wray Buntine

NeurIPS 2010 Gaussian Process Preference Elicitation Shengbo Guo, Scott Sanner, Edwin V. Bonilla

AISTATS 2007 Kernel Multi-Task Learning Using Task-Specific Features Edwin V. Bonilla, Felix V. Agakov, Christopher K. I. Williams

NeurIPS 2007 Multi-Task Gaussian Process Prediction Edwin V. Bonilla, Kian M. Chai, Christopher Williams

ICML 2006 Predictive Search Distributions Edwin V. Bonilla, Christopher K. I. Williams, Felix V. Agakov, John Cavazos, John Thomson, Michael F. P. O'Boyle