Daniel Hernández-lobato

21 papers · 2011–2024 · 7 conferences · across top CS/AI conferences

Achievements

+12 more ↓

🧭 Keyword Pioneer 🌈 Renaissance Researcher (5) 🌉 Interdisciplinary Bridge 🗺️ Taxonomy Completionist (13) 🐣 Hot Topic Early Bird

🌍 Conference Polyglot (7) 🗺️ Taxonomy Completionist (13) 🧭 Keyword Pioneer 🔬 Deep Specialist (10) 🏆 Keyword Champion (2) 🧬 Topic Evolution 🗃️ Keyword Collector (67) 🚀 Conference Pioneer 📈 Trend Setter 💎 Century Club (21) 🔥 Unstoppable (5) ⚡ Prolific Year (6)

Conferences

ICML (9) NIPS (4) JMLR (3) ICLR (2) AISTATS (1) CVPR (1) IJCAI (1)

Top co-authors

José Miguel Hernández-Lobato (7) Jose Hernandez-Lobato (3) Carlos Villacampa-Calvo (3) Luis A. Ortega (2) Yingzhen Li (2) Richard Turner (2) Alberto Suárez (2) Pablo Morales-Mombiela (2) Thang Bui (2) Novi Quadrianto (2)

Keywords

gaussian process (8) expectation propagation (8) variational inference (5) bayesian inference (5) approximate inference (4) multi-class classification (4) probabilistic modeling (4) multi-task learning (2) feature selection (2) gaussian process classification (2) privileged information (2) probabilistic model (2) sparse approximation (2) label noise (1) automatic differentiation (1) distributed learning (1) computer vision (1) robust classification (1) spike-and-slab prior (1) feature space (1)

Papers

Variational Linearized Laplace Approximation for Bayesian Deep Learning ICML 2024 Deep Variational Implicit Processes ICLR 2023 Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification ICML 2023 Function-space Inference with Sparse Implicit Processes ICML 2022 Input Dependent Sparse Gaussian Processes ICML 2022 Activation-level uncertainty in deep neural networks ICLR 2021 Multi-class Gaussian Process Classification with Noisy Inputs JMLR 2021 Scalable Multi-Class Gaussian Process Classification using Expectation Propagation ICML 2017 Predictive Entropy Search for Multi-objective Bayesian Optimization ICML 2016 Scalable Gaussian Process Classification via Expectation Propagation AISTATS 2016 Ambiguity Helps: Classification With Disagreements in Crowdsourced Annotations CVPR 2016 Deep Gaussian Processes for Regression using Approximate Expectation Propagation ICML 2016 Black-Box Alpha Divergence Minimization ICML 2016 Non-linear Causal Inference using Gaussianity Measures JMLR 2016 A Probabilistic Model for Dirty Multi-task Feature Selection ICML 2015 Mind the Nuisance: Gaussian Process Classification using Privileged Noise NIPS 2014 Learning Feature Selection Dependencies in Multi-task Learning NIPS 2013 Statistical Tests for the Detection of the Arrow of Time in Vector Autoregressive Models IJCAI 2013 Generalized Spike-and-Slab Priors for Bayesian Group Feature Selection Using Expectation Propagation JMLR 2013 Gaussian Process Conditional Copulas with Applications to Financial Time Series NIPS 2013 Robust Multi-Class Gaussian Process Classification NIPS 2011