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← Authors

Edwin V. Bonilla

30 papers · 2007–2025 · 6 conferences · across top CS/AI conferences

Achievements

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+12 more ↓ 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (20) 🐣 Hot Topic Early Bird
🐝 Cross-Pollinator (13) πŸ—ΊοΈ Taxonomy Completionist (20) 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (3) 🧬 Topic Evolution πŸ† Keyword Champion πŸ”¬ Deep Specialist (11) πŸ—ƒοΈ Keyword Collector (70) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ’Ž Century Club (30) πŸ”₯ Unstoppable (7)

Conferences

ICML (11) NIPS (11) AISTATS (4) IJCAI (2) ICLR (1) JMLR (1)

Top co-authors

He Zhao (4) Amir Dezfouli (4) Maurizio Filippone (3) Pietro Michiardi (3) Theodoros Damoulas (3) Fabio Ramos (3) Trung Le (2) Rafael Oliveira (2) Scott Sanner (2) Vy Vo (2)

Keywords

gaussian process (13) variational inference (11) bayesian inference (4) approximate inference (3) gaussian processes (3) sparse approximation (2) uncertainty quantification (2) representation learning (2) black-box inference (2) bayesian optimization (2) bayesian approach (2) latent space (2) probabilistic modeling (2) multi-task learning (2) structure learning (1) regularization (1) image classification (1) active learning (1) model calibration (1) gradient-based optimization (1)

Papers

Variational Learning of Fractional Posteriors ICML 2025 RΓ©nyi Neural Processes ICML 2025 Variational Search Distributions ICLR 2025 Contextual Directed Acyclic Graphs AISTATS 2024 Optimal Transport for Structure Learning Under Missing Data ICML 2024 Parameter Estimation in DAGs from Incomplete Data via Optimal Transport ICML 2024 Bayesian Adaptive Calibration and Optimal Design NIPS 2024 Free-Form Variational Inference for Gaussian Process State-Space Models ICML 2023 Transformed Distribution Matching for Missing Value Imputation ICML 2023 Recurrent Neural Networks and Universal Approximation of Bayesian Filters AISTATS 2023 Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks ICML 2022 Optimizing Sequential Experimental Design with Deep Reinforcement Learning ICML 2022 SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data ICML 2021 BORE: Bayesian Optimization by Density-Ratio Estimation ICML 2021 Model Selection for Bayesian Autoencoders NIPS 2021 Quantile Propagation for Wasserstein-Approximate Gaussian Processes NIPS 2020 Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings NIPS 2020 Generic Inference in Latent Gaussian Process Models JMLR 2019 Structured Variational Inference in Continuous Cox Process Models NIPS 2019 Efficient Inference in Multi-task Cox Process Models AISTATS 2019 Calibrating Deep Convolutional Gaussian Processes AISTATS 2019 Random Feature Expansions for Deep Gaussian Processes ICML 2017 Scalable Inference for Gaussian Process Models with Black-Box Likelihoods NIPS 2015 Extended and Unscented Gaussian Processes NIPS 2014 Automated Variational Inference for Gaussian Process Models NIPS 2014 Learning Community-Based Preferences via Dirichlet Process Mixtures of Gaussian Processes IJCAI 2013 Bayesian Joint Inversions for the Exploration of Earth Resources IJCAI 2013 Improving Topic Coherence with Regularized Topic Models NIPS 2011 Gaussian Process Preference Elicitation NIPS 2010 Multi-task Gaussian Process Prediction NIPS 2007