José Miguel Hernández-Lobato

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All published works

Action	Title	Year	Authors
+ PDF Chat	Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach	2024	Jingyi Zhao Yuxuan Ou Austin Tripp Morteza Rasoulianboroujeni José Miguel Hernández-Lobato
+ PDF Chat	A Deep Generative Model for the Design of Synthesizable Ionizable Lipids	2024	Yuxuan Ou Jingyi Zhao Austin Tripp Morteza Rasoulianboroujeni José Miguel Hernández-Lobato
+ PDF Chat	On conditional diffusion models for PDE simulations	2024	Aliaksandra Shysheya Cristiana Diaconu Federico Bergamin Paris Perdikaris José Miguel Hernández-Lobato Richard E. Turner Émile Mathieu
+ PDF Chat	Training Neural Samplers with Reverse Diffusive KL Divergence	2024	Jiajun He Wenlin Chen Ming‐Tian Zhang David G. Barber José Miguel Hernández-Lobato
+ PDF Chat	Batched Bayesian optimization with correlated candidate uncertainties	2024	Jenna C. Fromer Runzhong Wang Mrunali Manjrekar Austin Tripp José Miguel Hernández-Lobato Connor W. Coley
+ PDF Chat	Getting Free Bits Back from Rotational Symmetries in LLMs	2024	Jiajun He Gergely Flamich José Miguel Hernández-Lobato
+ PDF Chat	Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research	2024	Víctor Sabanza-Gil Riccardo Barbano Daniel Pacheco Gutiérrez Jeremy S. Luterbacher José Miguel Hernández-Lobato Philippe Schwaller Loı̈c M. Roch
+ PDF Chat	BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching	2024	RuiKang OuYang Bo Qiang José Miguel Hernández-Lobato
+ PDF Chat	Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models	2024	Z. W. Fan Jiajun He Laurence I. Midgley Javier Antorán José Miguel Hernández-Lobato
+ PDF Chat	Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations	2024	Richard Bergna Sergio Calvo-Ordoñez Felix L. Opolka Píetro Lió José Miguel Hernández-Lobato
+ PDF Chat	Diagnosing and fixing common problems in Bayesian optimization for molecule design	2024	Austin Tripp José Miguel Hernández-Lobato
+ PDF Chat	Improving Antibody Design with Force-Guided Sampling in Diffusion Models	2024	Paulina Kulytė Francisco Vargas Simon V. Mathis Yu Guang Wang José Miguel Hernández-Lobato Píetro Lió
+ PDF Chat	Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes	2024	Jihao Andreas Lin Shreyas Padhy Bruno Mlodozeniec José Miguel Hernández-Lobato
+ PDF Chat	Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes	2024	Jihao Andreas Lin Shreyas Padhy Bruno Mlodozeniec Javier Antorán José Miguel Hernández-Lobato
+ PDF Chat	Accelerating Relative Entropy Coding with Space Partitioning	2024	Jiajun He Gergely Flamich José Miguel Hernández-Lobato
+ PDF Chat	Generative Active Learning for the Search of Small-molecule Protein Binders	2024	Maksym Korablyov Chenghao Liu Moksh Jain Almer M. van der Sloot Eric Jolicoeur Edward Ruediger Andrei Cristian Nica Emmanuel Bengio Kostiantyn Lapchevskyi Daniel J. St‐Cyr
+ PDF Chat	A Generative Model of Symmetry Transformations	2024	James Urquhart Allingham Bruno Mlodozeniec Shreyas Padhy Javier Antorán David Krueger Richard E. Turner Eric Nalisnick José Miguel Hernández-Lobato
+ PDF Chat	Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation	2024	Xuexin Chen Ruichu Cai Zhengting Huang Yuxuan Zhu Julien Horwood Zhifeng Hao Zijian Li José Miguel Hernández-Lobato
+ PDF Chat	Diffusive Gibbs Sampling	2024	Wenlin Chen Ming‐Tian Zhang Brooks Paige José Miguel Hernández-Lobato David Barber
+ PDF Chat	Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images	2023	Pablo Morales-Álvarez Arne Schmidt José Miguel Hernández-Lobato Rafael Molina
+ PDF Chat	normflows: A PyTorch Package for Normalizing Flows	2023	Vincent Stimper David Liu Andrew T. Campbell Vincent Berenz Lukas Ryll Bernhard Schölkopf José Miguel Hernández-Lobato
+	Image Reconstruction via Deep Image Prior Subspaces	2023	Riccardo Barbano Javier Antorán Johannes Leuschner José Miguel Hernández-Lobato Željko Kereta Bangti Jin
+	normflows: A PyTorch Package for Normalizing Flows	2023	Vincent Stimper David Liu Andrew T. Campbell Vincent Berenz Lukas Ryll Bernhard Schölkopf José Miguel Hernández-Lobato
+	Compression with Bayesian Implicit Neural Representations	2023	Zongyu Guo Gergely Flamich Jiajun He Zhibo Chen José Miguel Hernández-Lobato
+	Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent	2023	Jihao Andreas Lin Javier Antorán Shreyas Padhy David M. Janz José Miguel Hernández-Lobato Alexander Terenin
+	Tanimoto Random Features for Scalable Molecular Machine Learning	2023	Austin Tripp Sergio Bacallado Sukriti Singh José Miguel Hernández-Lobato
+	Leveraging Task Structures for Improved Identifiability in Neural Network Representations	2023	Wenlin Chen Julien Horwood Juyeon Heo José Miguel Hernández-Lobato
+	Online Laplace Model Selection Revisited	2023	Jihao Andreas Lin Javier Antorán José Miguel Hernández-Lobato
+	Minimal Random Code Learning with Mean-KL Parameterization	2023	Jihao Andreas Lin Gergely Flamich José Miguel Hernández-Lobato
+	SE(3) Equivariant Augmented Coupling Flows	2023	Laurence I. Midgley Vincent Stimper Javier Antorán Émile Mathieu Bernhard Schölkopf José Miguel Hernández-Lobato
+	Graph Neural Stochastic Differential Equations	2023	Richard Bergna Felix L. Opolka Píetro Lió José Miguel Hernández-Lobato
+	RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations	2023	Jiajun He Gergely Flamich Zongyu Guo José Miguel Hernández-Lobato
+	Retro-fallback: retrosynthetic planning in an uncertain world	2023	Austin Tripp Krzysztof Maziarz Sarah Lewis Marwin Segler José Miguel Hernández-Lobato
+	Genetic algorithms are strong baselines for molecule generation	2023	Austin Tripp José Miguel Hernández-Lobato
+	Adam through a Second-Order Lens	2023	Ross M. Clarke Baiyu Su José Miguel Hernández-Lobato
+	Series of Hessian-Vector Products for Tractable Saddle-Free Newton Optimisation of Neural Networks	2023	Elre T. Oldewage Ross M. Clarke José Miguel Hernández-Lobato
+	Stochastic Gradient Descent for Gaussian Processes Done Right	2023	Jihao Andreas Lin Shreyas Padhy Javier Antorán Austin Tripp Alexander Terenin Csaba Szepesvári José Miguel Hernández-Lobato David M. Janz
+	DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design	2022	Miguel García-Ortegón Gregor N. C. Simm Austin Tripp José Miguel Hernández-Lobato Andreas Bender Sergio Bacallado
+ PDF Chat	BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis	2022	Weijie He Xiaohao Mao Chao Ma Yu Huang José Miguel Hernández-Lobato Ting Chen
+	Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo	2022	Ignacio Peis Chao Ma José Miguel Hernández-Lobato
+	Fast Relative Entropy Coding with A* coding	2022	Gergely Flamich Stratis Markou José Miguel Hernández-Lobato
+	Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior	2022	Javier Antorán Riccardo Barbano Johannes Leuschner José Miguel Hernández-Lobato Bangti Jin
+	Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction	2022	Wenlin Chen Austin Tripp José Miguel Hernández-Lobato
+	Adapting the Linearised Laplace Model Evidence for Modern Deep Learning	2022	Javier Antorán David M. Janz James Urquhart Allingham Erik Daxberger Riccardo Barbano Eric Nalisnick José Miguel Hernández-Lobato
+	Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior	2022	Riccardo Barbano Johannes Leuschner Javier Antorán Bangti Jin José Miguel Hernández-Lobato
+	Flow Annealed Importance Sampling Bootstrap	2022	Laurence Illing Midgley Vincent Stimper Gregor N. C. Simm Bernhard Schölkopf José Miguel Hernández-Lobato
+	Sampling-based inference for large linear models, with application to linearised Laplace	2022	Javier Antorán Shreyas Padhy Riccardo Barbano Eric Nalisnick David M. Janz José Miguel Hernández-Lobato
+ PDF Chat	Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not	2021	Chelsea Murray James Urquhart Allingham Javier Antorán José Miguel Hernández-Lobato
+	Bootstrap Your Flow	2021	Laurence Illing Midgley Vincent Stimper Gregor N. C. Simm José Miguel Hernández-Lobato
+	Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation.	2021	Ross M. Clarke Elre T. Oldewage José Miguel Hernández-Lobato
+	A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization	2021	Andrew R. Campbell Wenlong Chen Vincent Stimper José Miguel Hernández-Lobato Yichuan Zhang
+ PDF Chat	Educational Question Mining At Scale: Prediction, Analysis and Personalization	2021	Zichao Wang Sebastian Tschiatschek Simon Woodhead José Miguel Hernández-Lobato Simon Peyton Jones Richard G. Baraniuk Cheng Zhang
+	Gradient-based tuning of Hamiltonian Monte Carlo hyperparameters	2021	Andrew R. Campbell Wenlong Chen Vincent Stimper José Miguel Hernández-Lobato Yichuan Zhang
+	Symmetry-Aware Actor-Critic for 3D Molecular Design	2021	Gregor N. C. Simm Robert Pinsler Gábor Cśanyi José Miguel Hernández-Lobato
+	Contextual HyperNetworks for Novel Feature Adaptation	2021	Angus Lamb Evgeny Saveliev Yingzhen Li Sebastian Tschiatschek Camilla Longden Simon Woodhead José Miguel Hernández-Lobato Richard E. Turner Pashmina Cameron Cheng Zhang
+	Active Slices for Sliced Stein Discrepancy	2021	Wenbo Gong Kaibo Zhang Yingzhen Li José Miguel Hernández-Lobato
+	Nonlinear Invariant Risk Minimization: A Causal Approach	2021	Chaochao Lu Yuhuai Wu José Miguel Hernández-Lobato Bernhard Schölkopf
+	Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge	2021	Zichao Wang Angus Lamb Evgeny Saveliev Pashmina Cameron Yordan Zaykov José Miguel Hernández-Lobato Richard E. Turner Richard G. Baraniuk Craig Barton Simon Peyton Jones
+	Improving black-box optimization in VAE latent space using decoder uncertainty	2021	Pascal Notin José Miguel Hernández-Lobato Yarin Gal
+	Action-Sufficient State Representation Learning for Control with Structural Constraints	2021	Biwei Huang Chaochao Lu Liu Leqi José Miguel Hernández-Lobato Clark Glymour Bernhard Schölkopf Kun Zhang
+	Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not	2021	Chelsea Murray James Urquhart Allingham Javier Antorán José Miguel Hernández-Lobato
+	Depth Uncertainty Networks for Active Learning	2021	Chelsea Murray James Urquhart Allingham Javier Antorán José Miguel Hernández-Lobato
+	Resampling Base Distributions of Normalizing Flows	2021	Vincent Stimper Bernhard Schölkopf José Miguel Hernández-Lobato
+	DOCKSTRING: easy molecular docking yields better benchmarks for ligand design	2021	Miguel García-Ortegón Gregor N. C. Simm Austin Tripp José Miguel Hernández-Lobato Andreas Bender Sergio Bacallado
+	Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation	2021	Ross M. Clarke Elre T. Oldewage José Miguel Hernández-Lobato
+	Bootstrap Your Flow	2021	Laurence Illing Midgley Vincent Stimper Gregor N. C. Simm José Miguel Hernández-Lobato
+	Barking up the right tree: an approach to search over molecule synthesis DAGs	2020	John Bradshaw Brooks Paige Matt J. Kusner Marwin Segler José Miguel Hernández-Lobato
+	Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding	2020	Gergely Flamich Marton Havasi José Miguel Hernández-Lobato
+	FIT: a Fast and Accurate Framework for Solving Medical Inquiring and Diagnosing Tasks.	2020	Weijie He Xiaohao Mao Chao Ma José Miguel Hernández-Lobato Ting Chen
+	Symmetry-Aware Actor-Critic for 3D Molecular Design	2020	Gregor N. C. Simm Robert Pinsler Gábor Cśanyi José Miguel Hernández-Lobato
+	Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding	2020	Gergely Flamich Marton Havasi José Miguel Hernández-Lobato
+	Diagnostic Questions: The NeurIPS 2020 Education Challenge.	2020	Zichao Wang Angus Lamb Evgeny Saveliev Pashmina Cameron Yordan Zaykov José Miguel Hernández-Lobato Richard E. Turner Richard G. Baraniuk Craig Barton Simon Peyton Jones
+	Predictive Complexity Priors	2020	Eric Nalisnick Jonathan Gordon José Miguel Hernández-Lobato
+	Depth Uncertainty in Neural Networks	2020	Javier Antorán James Urquhart Allingham José Miguel Hernández-Lobato
+	Reinforcement Learning for Molecular Design Guided by Quantum Mechanics	2020	Gregor N. C. Simm Robert Pinsler José Miguel Hernández-Lobato
+	Variational Depth Search in ResNets.	2020	Javier Antorán James Urquhart Allingham José Miguel Hernández-Lobato
+	DRIFT: Deep Reinforcement Learning for Functional Software Testing	2020	Luke Harries Rebekah Storan Clarke Timothy Chapman Swamy V. P. L. N. Nallamalli Levent Özgür Shuktika Jain Alex Po Leung Steve Lim Aaron Dietrich José Miguel Hernández-Lobato
+	Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures	2020	Alonso Marco Alexander von Rohr Dominik Baumann José Miguel Hernández-Lobato Sebastian Trimpe
+	Getting a CLUE: A Method for Explaining Uncertainty Estimates	2020	Javier Antorán Umang Bhatt Tameem Adel Adrian Weller José Miguel Hernández-Lobato
+	Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining	2020	Austin Tripp Erik Daxberger José Miguel Hernández-Lobato
+	Sliced Kernelized Stein Discrepancy	2020	Wenbo Gong Yingzhen Li José Miguel Hernández-Lobato
+	VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data	2020	Chao Ma Sebastian Tschiatschek José Miguel Hernández-Lobato Richard E. Turner Cheng Zhang
+	Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation	2020	Chaochao Lu Biwei Huang Ke Wang José Miguel Hernández-Lobato Kun Zhang Bernhard Schölkopf
+	Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge	2020	Zichao Wang Angus Lamb Evgeny Saveliev Pashmina Cameron Yordan Zaykov José Miguel Hernández-Lobato Richard E. Turner Richard G. Baraniuk Craig Barton Simon Peyton Jones
+	Educational Question Mining At Scale: Prediction, Analysis and Personalization	2020	Zichao Wang Sebastian Tschiatschek Simon Woodhead José Miguel Hernández-Lobato Simon Peyton Jones Richard G. Baraniuk Cheng Zhang
+	Barking up the right tree: an approach to search over molecule synthesis DAGs	2020	John L. Bradshaw Brooks Paige Matt J. Kusner Marwin Segler José Miguel Hernández-Lobato
+	Symmetry-Aware Actor-Critic for 3D Molecular Design	2020	Gregor N. C. Simm Robert Pinsler Gábor Csányi José Miguel Hernández-Lobato
+	Bayesian Deep Learning via Subnetwork Inference	2020	Erik Daxberger Eric Nalisnick James Urquhart Allingham Javier Antorán José Miguel Hernández-Lobato
+	Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding	2020	Gergely Flamich Marton Havasi José Miguel Hernández-Lobato
+	Predictive Complexity Priors	2020	Eric Nalisnick Jonathan Gordon José Miguel Hernández-Lobato
+	Depth Uncertainty in Neural Networks	2020	Javier Antorán James Urquhart Allingham José Miguel Hernández-Lobato
+	Reinforcement Learning for Molecular Design Guided by Quantum Mechanics	2020	Gregor N. C. Simm Robert Pinsler José Miguel Hernández-Lobato
+	Variational Depth Search in ResNets	2020	Javier Antorán James Urquhart Allingham José Miguel Hernández-Lobato
+	Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model	2019	Wenbo Gong Sebastian Tschiatschek Sebastian Nowozin Richard E. Turner José Miguel Hernández-Lobato Cheng Zhang
+	Successor Uncertainties: exploration and uncertainty in temporal difference learning	2019	David M. Janz Jiri Hron P. Mazur Katja Hofmann José Miguel Hernández-Lobato Sebastian Tschiatschek
+	A Generative Model for Molecular Distance Geometry	2019	Gregor N. C. Simm José Miguel Hernández-Lobato
+	Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection	2019	Erik Daxberger José Miguel Hernández-Lobato
+	Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model.	2019	Wenbo Gong Sebastian Tschiatschek Richard E. Turner Sebastian Nowozin José Miguel Hernández-Lobato Cheng Zhang
+	'In-Between' Uncertainty in Bayesian Neural Networks	2019	Andrew Y. K. Foong Yingzhen Li José Miguel Hernández-Lobato Richard E. Turner
+	A COLD Approach to Generating Optimal Samples.	2019	Omar Mahmood José Miguel Hernández-Lobato
+	Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care	2019	Anna-Lena Popkes Hiske Overweg Ari Ercole Yingzhen Li José Miguel Hernández-Lobato Yordan Zaykov Cheng Zhang
+	A Model to Search for Synthesizable Molecules	2019	John Bradshaw Brooks Paige Matt J. Kusner Marwin Segler José Miguel Hernández-Lobato
+	Minimal random code learning : Getting bits back from compressed model parameters	2019	Marton Havasi Robert Peharz José Miguel Hernández-Lobato
+	Bayesian Batch Active Learning as Sparse Subset Approximation	2019	Robert Pinsler Jonathan Gordon Eric Nalisnick José Miguel Hernández-Lobato
+	Bayesian Batch Active Learning as Sparse Subset Approximation	2019	Robert Pinsler Jonathan Gordon Eric Nalisnick José Miguel Hernández-Lobato
+	HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals	2019	Chao Ma Sebastian Tschiatschek Yingzhen Li Richard E. Turner José Miguel Hernández-Lobato Cheng Zhang
+	Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection	2019	Erik Daxberger José Miguel Hernández-Lobato
+	A Generative Model for Molecular Distance Geometry	2019	Gregor N. C. Simm José Miguel Hernández-Lobato
+	Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model	2019	Wenbo Gong Sebastian Tschiatschek Richard E. Turner Sebastian Nowozin José Miguel Hernández-Lobato Cheng Zhang
+	'In-Between' Uncertainty in Bayesian Neural Networks	2019	Andrew Y. K. Foong Yingzhen Li José Miguel Hernández-Lobato Richard E. Turner
+	A COLD Approach to Generating Optimal Samples	2019	Omar Mahmood José Miguel Hernández-Lobato
+	Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care	2019	Hiske Overweg Anna-Lena Popkes Ari Ercole Yingzhen Li José Miguel Hernández-Lobato Yordan Zaykov Cheng Zhang
+	Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks	2018	Anqi Wu Sebastian Nowozin Edward Meeds Richard E. Turner José Miguel Hernández-Lobato Alexander L. Gaunt
+	Ergodic Measure Preserving Flows	2018	Yichuan Zhang José Miguel Hernández-Lobato Zoubin Ghahramani
+	Meta-Learning For Stochastic Gradient MCMC.	2018	Wenbo Gong Yingzhen Li José Miguel Hernández-Lobato
+	Variational Measure Preserving Flows.	2018	Yichuan Zhang José Miguel Hernández-Lobato Zoubin Ghahramani
+	Predicting Electron Paths.	2018	John Bradshaw Matt J. Kusner Brooks Paige Marwin Segler José Miguel Hernández-Lobato
+	A Generative Model For Electron Paths	2018	John Bradshaw Matt J. Kusner Brooks Paige Marwin Segler José Miguel Hernández-Lobato
+	Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation	2018	Thang D. Bui José Miguel Hernández-Lobato Yingzhen Li Daniel Hernández-Lobato Richard E. Turner Richard E. Turner
+	Taking gradients through experiments: LSTMs and memory proximal policy optimization for black-box quantum control	2018	Moritz August José Miguel Hernández-Lobato
+	Stochastic Expectation Propagation for Large Scale Gaussian Process Classification	2018	Daniel Hernández-Lobato José Miguel Hernández-Lobato Yingzhen Li Thang D. Bui Richard E. Turner
+	Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules	2018	Rafael Gómez‐Bombarelli Jennifer N. Wei David Duvenaud José Miguel Hernández-Lobato Benjamín Sánchez-Lengeling Dennis Sheberla Jorge Aguilera‐Iparraguirre Timothy Hirzel Ryan P. Adams Alán Aspuru‐Guzik
+	Deep Gaussian Processes with Decoupled Inducing Inputs	2018	Marton Havasi José Miguel Hernández-Lobato Juan José Murillo-Fuentes
+	Variational Implicit Processes	2018	Chao Ma Yingzhen Li José Miguel Hernández-Lobato
+	Meta-Learning for Stochastic Gradient MCMC	2018	Wenbo Gong Yingzhen Li José Miguel Hernández-Lobato
+	Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo	2018	Marton Havasi José Miguel Hernández-Lobato Juan José Murillo-Fuentes
+	Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters	2018	Marton Havasi Robert Peharz José Miguel Hernández-Lobato
+	Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning	2018	David M. Janz Jiri Hron P. Mazur Katja Hofmann José Miguel Hernández-Lobato Sebastian Tschiatschek
+	Deconfounding Reinforcement Learning in Observational Settings	2018	Chaochao Lu Bernhard Schölkopf José Miguel Hernández-Lobato
+	Dropout as a Structured Shrinkage Prior	2018	Eric Nalisnick José Miguel Hernández-Lobato Padhraic Smyth
+	Deterministic Variational Inference for Robust Bayesian Neural Networks	2018	Anqi Wu Sebastian Nowozin Edward Meeds Richard E. Turner José Miguel Hernández-Lobato Alexander L. Gaunt
+	EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE	2018	Chao Ma Sebastian Tschiatschek Konstantina Palla José Miguel Hernández-Lobato Sebastian Nowozin Cheng Zhang
+ PDF Chat	Taking Gradients Through Experiments: LSTMs and Memory Proximal Policy Optimization for Black-Box Quantum Control	2018	Moritz August José Miguel Hernández-Lobato
+	Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo	2018	Marton Havasi José Miguel Hernández-Lobato Juan José Murillo-Fuentes
+	Ergodic Inference: Accelerate Convergence by Optimisation	2018	Yichuan Zhang José Miguel Hernández-Lobato
+	A Generative Model For Electron Paths	2018	John L. Bradshaw Matt J. Kusner Brooks Paige Marwin Segler José Miguel Hernández-Lobato
+	Taking gradients through experiments: LSTMs and memory proximal policy optimization for black-box quantum control	2018	Moritz August José Miguel Hernández-Lobato
+	Deep Gaussian Processes with Decoupled Inducing Inputs	2018	Marton Havasi José Miguel Hernández-Lobato Juan José Murillo-Fuentes
+	Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems.	2017	Stefan Depeweg José Miguel Hernández-Lobato Finale Doshi‐Velez Steffen Udluft
+	Grammar Variational Autoencoder	2017	Matt J. Kusner Brooks Paige José Miguel Hernández-Lobato
+	Bayesian Semisupervised Learning with Deep Generative Models	2017	Jonathan Gordon José Miguel Hernández-Lobato
+	Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables	2017	Stefan Depeweg José Miguel Hernández-Lobato Finale Doshi‐Velez Steffen Udluft
+	Actively Learning what makes a Discrete Sequence Valid	2017	David M. Janz Jos van der Westhuizen José Miguel Hernández-Lobato
+	Constrained Bayesian Optimization for Automatic Chemical Design	2017	Ryan‐Rhys Griffiths José Miguel Hernández-Lobato
+	Learning a Generative Model for Validity in Complex Discrete Structures	2017	David M. Janz Jos van der Westhuizen Brooks Paige Matt J. Kusner José Miguel Hernández-Lobato
+	Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning	2017	Stefan Depeweg José Miguel Hernández-Lobato Finale Doshi‐Velez Steffen Udluft
+	Grammar Variational Autoencoder	2017	Matt J. Kusner Brooks Paige José Miguel Hernández-Lobato
+	Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space	2017	José Miguel Hernández-Lobato James Requeima Edward O. Pyzer‐Knapp Alán Aspuru‐Guzik
+	GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution	2016	Matt J. Kusner José Miguel Hernández-Lobato
+	GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution	2016	Matt J. Kusner José Miguel Hernández-Lobato
+	Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks	2016	Stefan Depeweg José Miguel Hernández-Lobato Finale Doshi‐Velez Steffen Udluft
+	Learning and policy search in stochastic dynamical systems with Bayesian neural networks	2016	Stefan Depeweg José Miguel Hernández-Lobato Finale Doshi‐Velez Steffen Udluft
+	Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control	2016	Natasha Jaques Shixiang Gu Dzmitry Bahdanau José Miguel Hernández-Lobato Richard E. Turner Douglas Eck
+	Deep Gaussian Processes for Regression using Approximate Expectation Propagation	2016	Thang D. Bui Daniel Hernández-Lobato Yingzhen Li José Miguel Hernández-Lobato Richard E. Turner
+	GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution	2016	Matt J. Kusner José Miguel Hernández-Lobato
+	Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks	2016	Stefan Depeweg José Miguel Hernández-Lobato Finale Doshi‐Velez Steffen Udluft
+	Predictive Entropy Search for Multi-objective Bayesian Optimization	2015	Daniel Hernández-Lobato José Miguel Hernández-Lobato Amar Shah Ryan P. Adams
+	Black-box $\alpha$-divergence Minimization	2015	José Miguel Hernández-Lobato Yingzhen Li Mark Rowland Daniel Hernández-Lobato Thang D. Bui Richard E. Turner
+	Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks	2015	José Miguel Hernández-Lobato Ryan P. Adams
+	Predictive Entropy Search for Bayesian Optimization with Unknown Constraints	2015	José Miguel Hernández-Lobato Michael A. Gelbart Matthew W. Hoffman Ryan P. Adams Zoubin Ghahramani
+	Predictive Entropy Search for Bayesian Optimization with Unknown Constraints	2015	José Miguel Hernández-Lobato Michael A. Gelbart Matthew D. Hoffman Ryan P. Adams Zoubin Ghahramani
+	Stochastic Expectation Propagation	2015	Yingzhen Li José Miguel Hernández-Lobato Richard E. Turner
+	A General Framework for Constrained Bayesian Optimization using Information-based Search	2015	José Miguel Hernández-Lobato Michael A. Gelbart Ryan P. Adams Matthew W. Hoffman Zoubin Ghahramani
+	Stochastic Expectation Propagation for Large Scale Gaussian Process Classification	2015	Daniel Hernández-Lobato José Miguel Hernández-Lobato Yingzhen Li Thang Bui Richard E. Turner
+	Black-box $α$-divergence Minimization	2015	José Miguel Hernández-Lobato Yingzhen Li Mark Rowland Daniel Hernández-Lobato Thang Bui Richard E. Turner
+	Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks	2015	José Miguel Hernández-Lobato Ryan P. Adams
+	Predictive Entropy Search for Multi-objective Bayesian Optimization	2015	Daniel Hernández-Lobato José Miguel Hernández-Lobato Anoop Shah Ryan P. Adams
+	Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation	2015	Thang D. Bui José Miguel Hernández-Lobato Yingzhen Li Daniel Hernández-Lobato Richard E. Turner
+	Scalable Gaussian Process Classification via Expectation Propagation	2015	Daniel Hernández-Lobato José Miguel Hernández-Lobato
+	Predictive Entropy Search for Efficient Global Optimization of Black-box Functions	2014	José Miguel Hernández-Lobato Matthew W. Hoffman Zoubin Ghahramani
+	Gaussian Process Volatility Model	2014	Yue Wu José Miguel Hernández-Lobato Zoubin Ghahramani
+	Predictive Entropy Search for Efficient Global Optimization of Black-box Functions	2014	José Miguel Hernández-Lobato Matthew W. Hoffman Zoubin Ghahramani
+	Dynamic Covariance Models for Multivariate Financial Time Series	2013	Yue Wu José Miguel Hernández-Lobato Zoubin Ghahramani
+	Gaussian Process Vine Copulas for Multivariate Dependence	2013	David Lopez‐Paz José Miguel Hernández-Lobato Zoubin Ghahramani
+	Semi-Supervised Domain Adaptation with Non-Parametric Copulas	2013	David López-Paz José Miguel Hernández-Lobato Bernhard Schölkopf
+	Gaussian Process Conditional Copulas with Applications to Financial Time Series	2013	José Miguel Hernández-Lobato James Robert Lloyd Daniel Hernández-Lobato
+	Convergent Expectation Propagation in Linear Models with Spike-and-slab Priors	2011	José Miguel Hernández-Lobato Daniel Hernández-Lobato
+	Balancing flexibility and robustness in machine learning: semi-parametric methods and sparse linear models	2010	José Miguel Hernández-Lobato

Common Coauthors

Coauthor	Papers Together
Richard E. Turner	23
Javier Antorán	21
Yingzhen Li	19
Gregor N. C. Simm	12
Matt J. Kusner	12
Austin Tripp	12
Daniel Hernández-Lobato	12
Brooks Paige	11
Sebastian Tschiatschek	11
Bernhard Schölkopf	10
James Urquhart Allingham	10
Marton Havasi	9
Zoubin Ghahramani	9
Gergely Flamich	9
Vincent Stimper	9
Eric Nalisnick	9
David M. Janz	8
Ryan P. Adams	8
Marwin Segler	8
Wenbo Gong	7
Robert Pinsler	7
Riccardo Barbano	6
Shreyas Padhy	6
Finale Doshi‐Velez	6
Jihao Andreas Lin	6
Sebastian Nowozin	6
Simon Woodhead	6
Jiajun He	6
Stefan Depeweg	6
Steffen Udluft	6
Jonathan Gordon	5
Yordan Zaykov	5
Thang D. Bui	5
Erik Daxberger	5
Richard G. Baraniuk	5
Cheng Zhang	5
Simon Peyton Jones	5
Zichao Wang	5
Evgeny Saveliev	4
Matthew W. Hoffman	4
John Bradshaw	4
Yichuan Zhang	4
Ross M. Clarke	4
Chao Ma	4
Juan José Murillo-Fuentes	4
Pashmina Cameron	4
Chaochao Lu	4
Angus Lamb	4
Sergio Bacallado	3
Johannes Leuschner	3

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning	2015	Yarin Gal Zoubin Ghahramani	15
+	Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules	2018	Rafael Gómez‐Bombarelli Jennifer N. Wei David Duvenaud José Miguel Hernández-Lobato Benjamín Sánchez-Lengeling Dennis Sheberla Jorge Aguilera‐Iparraguirre Timothy Hirzel Ryan P. Adams Alán Aspuru‐Guzik	14
+	Stochastic Backpropagation and Approximate Inference in Deep Generative Models	2014	Danilo Jimenez Rezende Shakir Mohamed Daan Wierstra	13
+	Adam: A Method for Stochastic Optimization	2014	Diederik P. Kingma Jimmy Ba	13
+	Weight Uncertainty in Neural Networks	2015	Charles Blundell Julien Cornebise Koray Kavukcuoglu Daan Wierstra	9
+	Generating Sentences from a Continuous Space	2016	Samuel R. Bowman Luke Vilnis Oriol Vinyals Andrew M. Dai Rafał Józefowicz Samy Bengio	8
+	Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation	2018	Jiaxuan You Bowen Liu Rex Ying Vijay S. Pande Jure Leskovec	8
+	Expectation Propagation for approximate Bayesian inference	2013	Thomas P. Minka	8
+	Practical Bayesian Optimization of Machine Learning Algorithms	2012	Jasper Snoek Hugo Larochelle Ryan P. Adams	8
+ PDF Chat	GuacaMol: Benchmarking Models for de Novo Molecular Design	2019	Nathan Brown Marco Fiscato Marwin Segler Alain C. Vaucher	8
+	Learning Deep Generative Models of Graphs	2018	Yujia Li Oriol Vinyals Chris Dyer Razvan Pascanu Peter Battaglia	8
+	Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models	2017	Gabriel Lima Guimaraes Benjamín Sánchez-Lengeling Pedro Luis Cunha Farias Alán Aspuru‐Guzik	8
+	Pattern Recognition and Machine Learning	2007	Christopher Bishop	7
+	MolGAN: An implicit generative model for small molecular graphs	2018	Nicola De Cao Thomas Kipf	7
+ PDF Chat	Molecular de-novo design through deep reinforcement learning	2017	Marcus Olivecrona Thomas Blaschke Ola Engkvist Hongming Chen	7
+	A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning	2010	Eric Brochu Vlad M. Cora Nando de Freitas	6
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	6
+ PDF Chat	Multi-objective de novo drug design with conditional graph generative model	2018	Yibo Li Liangren Zhang Zhenming Liu	6
+	Deep Gaussian Processes for Regression using Approximate Expectation Propagation	2016	Thang D. Bui Daniel Hernández-Lobato Yingzhen Li José Miguel Hernández-Lobato Richard E. Turner	6
+	Practical Bayesian Optimization of Machine Learning Algorithms	2012	Jasper Snoek Hugo Larochelle Ryan P. Adams	6
+ PDF Chat	Optimization of Molecules via Deep Reinforcement Learning	2019	Zhenpeng Zhou Steven Kearnes Li Li Richard N. Zare Patrick Riley	6
+	PyTorch: An Imperative Style, High-Performance Deep Learning Library	2019	Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury Gregory Chanan Trevor Killeen Zeming Lin Natalia Gimelshein Luca Antiga	5
+	Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles	2016	Balaji Lakshminarayanan Alexander Pritzel Charles Blundell	5
+ PDF Chat	PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation	2017	Raffaelli Charles Hao Su Kaichun Mo Leonidas Guibas	5
+	A kernel two-sample test	2012	Arthur Gretton Karsten Borgwardt Malte J. Rasch Bernhard Schölkopf Alexander J. Smola	5
+	Predictive Entropy Search for Efficient Global Optimization of Black-box Functions	2014	José Miguel Hernández-Lobato Matthew W. Hoffman Zoubin Ghahramani	5
+	Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks	2016	Stefan Depeweg José Miguel Hernández-Lobato Finale Doshi‐Velez Steffen Udluft	5
+	Constrained Graph Variational Autoencoders for Molecule Design	2018	Qi Liu Miltiadis Allamanis Marc Brockschmidt Alexander L. Gaunt	5
+	Bayesian Active Learning for Classification and Preference Learning	2011	Neil Houlsby Ferenc Huszár Zoubin Ghahramani Máté Lengyel	5
+	Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation	2014	Kyunghyun Cho Bart van Merriënboer Çaǧlar Gülçehre Dzmitry Bahdanau Fethi Bougares Holger Schwenk Yoshua Bengio	5
+	Bayesian Learning via Stochastic Gradient Langevin Dynamics	2011	Max Welling Yee Whye Teh	5
+	Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks	2015	José Miguel Hernández-Lobato Ryan P. Adams	5
+	Spatial Point Processes	2011	Mark Huber	5
+ PDF Chat	“Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models	2018	Philippe Schwaller Théophile Gaudin Dávid Lányi Costas Bekas Teodoro Laino	4
+ PDF Chat	Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning	2019	Frank Noé Simon Olsson Jonas Köhler Hao Wu	4
+	Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms	2017	Xiao Han Kashif Rasul Roland Vollgraf	4
+	Statistical Analysis With Missing Data	1989	Maureen Lahiff Roderick J. A. Little Donald B. Rubin	4
+	Importance Weighted Autoencoders	2015	Yuri Burda Roger Grosse Ruslan Salakhutdinov	4
+	Convolutional Networks on Graphs for Learning Molecular Fingerprints	2015	David Duvenaud Dougal Maclaurin Jorge Aguilera‐Iparraguirre Rafael Gómez‐Bombarelli Timothy Hirzel Alán Aspuru‐Guzik Ryan P. Adams	4
+	Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model	2019	Wenbo Gong Sebastian Tschiatschek Sebastian Nowozin Richard E. Turner José Miguel Hernández-Lobato Cheng Zhang	4
+	Stochastic Backpropagation and Approximate Inference in Deep Generative Models	2014	Danilo Jimenez Rezende Shakir Mohamed Daan Wierstra	4
+ PDF Chat	An informational approach to the global optimization of expensive-to-evaluate functions	2008	Julien Villemonteix Emmanuel Vázquez Éric Walter	4
+ PDF Chat	Advances in Variational Inference	2018	Cheng Zhang Judith Bütepage Hedvig Kjellström Stephan Mandt	4
+	Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks	2015	Alec Radford Luke Metz Soumith Chintala	4
+	Gated Graph Sequence Neural Networks	2015	Yujia Li Daniel Tarlow Marc Brockschmidt Richard S. Zemel	4
+	Markov Chain Monte Carlo and Variational Inference: Bridging the Gap	2014	Tim Salimans Diederik P. Kingma Max Welling	4
+	Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks	2017	Marwin Segler Thierry Kogej Christian Tyrchan Mark P. Waller	4
+	Stochastic Gradient Hamiltonian Monte Carlo	2014	Tianqi Chen Emily B. Fox Carlos Guestrin	4
+	Learning Multimodal Graph-to-Graph Translation for Molecular Optimization	2018	Wengong Jin Kevin Yang Regina Barzilay Tommi Jaakkola	4
+	Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning	2017	Thomas M. Moerland Joost Broekens Catholijn M. Jonker	4