More from the Same Authors

• 2021 : A Fresh Look at De Novo Molecular Design Benchmarks »
  Austin Tripp · Gregor Simm · José Miguel Hernández-Lobato
• 2021 : Depth Uncertainty Networks for Active Learning »
  Chelsea Murray · James Allingham · Javier Antorán · José Miguel Hernández-Lobato
• 2022 : Flow Annealed Importance Sampling Bootstrap »
  Laurence Midgley · Vincent Stimper · Gregor Simm · Bernhard Schölkopf · José Miguel Hernández-Lobato
• 2022 : Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction »
  Wenlin Chen · Austin Tripp · José Miguel Hernández-Lobato
• 2022 : Learning Generative Models with Invariance to Symmetries »
  James Allingham · Javier Antorán · Shreyas Padhy · Eric Nalisnick · José Miguel Hernández-Lobato
• 2022 : Panel »
  Roman Garnett · José Miguel Hernández-Lobato · Eytan Bakshy · Syrine Belakaria · Stefanie Jegelka
• 2022 Poster: Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo »
  Ignacio Peis · Chao Ma · José Miguel Hernández-Lobato
• 2021 Workshop: Deep Generative Models and Downstream Applications »
  José Miguel Hernández-Lobato · Yingzhen Li · Yichuan Zhang · Cheng Zhang · Austin Tripp · Weiwei Pan · Oren Rippel
• 2021 Poster: Functional Variational Inference based on Stochastic Process Generators »
  Chao Ma · José Miguel Hernández-Lobato
• 2021 Poster: Improving black-box optimization in VAE latent space using decoder uncertainty »
  Pascal Notin · José Miguel Hernández-Lobato · Yarin Gal
• 2020 Workshop: Machine Learning for Molecules »
  José Miguel Hernández-Lobato · Matt Kusner · Brooks Paige · Marwin Segler · Jennifer Wei
• 2020 : Jose Miguel Hernandez Lobato »
  José Miguel Hernández-Lobato
• 2020 Poster: Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding »
  Gergely Flamich · Marton Havasi · José Miguel Hernández-Lobato
• 2020 Poster: Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining »
  Austin Tripp · Erik Daxberger · José Miguel Hernández-Lobato
• 2020 Poster: Depth Uncertainty in Neural Networks »
  Javier Antorán · James Allingham · José Miguel Hernández-Lobato
• 2020 Poster: VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data »
  Chao Ma · Sebastian Tschiatschek · Richard Turner · José Miguel Hernández-Lobato · Cheng Zhang
• 2020 Poster: Barking up the right tree: an approach to search over molecule synthesis DAGs »
  John Bradshaw · Brooks Paige · Matt Kusner · Marwin Segler · José Miguel Hernández-Lobato
• 2020 Spotlight: Barking up the right tree: an approach to search over molecule synthesis DAGs »
  John Bradshaw · Brooks Paige · Matt Kusner · Marwin Segler · José Miguel Hernández-Lobato
• 2019 Workshop: Bayesian Deep Learning »
  Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Eric Nalisnick · Zoubin Ghahramani · Kevin Murphy · Max Welling
• 2019 Poster: Bayesian Batch Active Learning as Sparse Subset Approximation »
  Robert Pinsler · Jonathan Gordon · Eric Nalisnick · José Miguel Hernández-Lobato
• 2019 Poster: Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model »
  Wenbo Gong · Sebastian Tschiatschek · Sebastian Nowozin · Richard Turner · José Miguel Hernández-Lobato · Cheng Zhang
• 2019 Poster: A Model to Search for Synthesizable Molecules »
  John Bradshaw · Brooks Paige · Matt Kusner · Marwin Segler · José Miguel Hernández-Lobato
• 2019 Poster: Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning »
  David Janz · Jiri Hron · Przemysław Mazur · Katja Hofmann · José Miguel Hernández-Lobato · Sebastian Tschiatschek
• 2018 Workshop: Machine Learning for Molecules and Materials »
  José Miguel Hernández-Lobato · Klaus-Robert Müller · Brooks Paige · Matt Kusner · Stefan Chmiela · Kristof Schütt
• 2018 Workshop: Bayesian Deep Learning »
  Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew Wilson · Zoubin Ghahramani · Kevin Murphy · Max Welling
• 2018 Poster: Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo »
  Marton Havasi · José Miguel Hernández-Lobato · Juan J. Murillo-Fuentes
• 2017 Workshop: Bayesian Deep Learning »
  Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew Wilson · Andrew Wilson · Diederik Kingma · Zoubin Ghahramani · Kevin Murphy · Max Welling
• 2017 Workshop: Bayesian optimization for science and engineering »
  Ruben Martinez-Cantin · José Miguel Hernández-Lobato · Javier Gonzalez
• 2017 : Closing remarks »
  José Miguel Hernández-Lobato
• 2017 Workshop: Machine Learning for Molecules and Materials »
  Kristof Schütt · Klaus-Robert Müller · Anatole von Lilienfeld · José Miguel Hernández-Lobato · Klaus-Robert Müller · Alan Aspuru-Guzik · Bharath Ramsundar · Matt Kusner · Brooks Paige · Stefan Chmiela · Alexandre Tkatchenko · Anatole von Lilienfeld · Koji Tsuda
• 2016 : Panel Discussion »
  Shakir Mohamed · David Blei · Ryan Adams · José Miguel Hernández-Lobato · Ian Goodfellow · Yarin Gal
• 2016 : Automatic Chemical Design using Variational Autoencoders »
  José Miguel Hernández-Lobato
• 2016 : Alpha divergence minimization for Bayesian deep learning »
  José Miguel Hernández-Lobato
• 2015 Poster: Stochastic Expectation Propagation »
  Yingzhen Li · José Miguel Hernández-Lobato · Richard Turner
• 2015 Spotlight: Stochastic Expectation Propagation »
  Yingzhen Li · José Miguel Hernández-Lobato · Richard Turner
• 2014 Poster: Predictive Entropy Search for Efficient Global Optimization of Black-box Functions »
  José Miguel Hernández-Lobato · Matthew Hoffman · Zoubin Ghahramani
• 2014 Poster: Gaussian Process Volatility Model »
  Yue Wu · José Miguel Hernández-Lobato · Zoubin Ghahramani
• 2014 Spotlight: Predictive Entropy Search for Efficient Global Optimization of Black-box Functions »
  José Miguel Hernández-Lobato · Matthew Hoffman · Zoubin Ghahramani
• 2013 Poster: Learning Feature Selection Dependencies in Multi-task Learning »
  Daniel Hernández-lobato · José Miguel Hernández-Lobato
• 2013 Poster: Gaussian Process Conditional Copulas with Applications to Financial Time Series »
  José Miguel Hernández-Lobato · James R Lloyd · Daniel Hernández-lobato
• 2012 Poster: Collaborative Gaussian Processes for Preference Learning »
  Neil Houlsby · José Miguel Hernández-Lobato · Ferenc Huszar · Zoubin Ghahramani
• 2012 Poster: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
  David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf
• 2012 Spotlight: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
  David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf
• 2011 Poster: Robust Multi-Class Gaussian Process Classification »
  Daniel Hernández-lobato · José Miguel Hernández-Lobato · Pierre Dupont
• 2007 Poster: Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach »
  José Miguel Hernández-Lobato · Tjeerd M Dijkstra · Tom Heskes