
Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care
Clinical decision making is challenging because of pathological complexi...
05/07/2019 ∙ by AnnaLena Popkes, et al. ∙ 12 ∙ shareread it

Deconfounding Reinforcement Learning in Observational Settings
We propose a general formulation for addressing reinforcement learning (...
12/26/2018 ∙ by Chaochao Lu, et al. ∙ 10 ∙ shareread it

A Model to Search for Synthesizable Molecules
Deep generative models are able to suggest new organic molecules by gene...
06/12/2019 ∙ by John Bradshaw, et al. ∙ 7 ∙ shareread it

EDDI: Efficient Dynamic Discovery of HighValue Information with Partial VAE
Making decisions requires information relevant to the task at hand. Many...
09/28/2018 ∙ by Chao Ma, et al. ∙ 6 ∙ shareread it

Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks
Bayesian neural networks (BNNs) hold great promise as a flexible and pri...
10/09/2018 ∙ by Anqi Wu, et al. ∙ 6 ∙ shareread it

Icebreaker: Elementwise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
In this paper we introduce the icestart problem, i.e., the challenge of...
08/13/2019 ∙ by Wenbo Gong, et al. ∙ 3 ∙ shareread it

'InBetween' Uncertainty in Bayesian Neural Networks
We describe a limitation in the expressiveness of the predictive uncerta...
06/27/2019 ∙ by Andrew Y. K. Foong, et al. ∙ 1 ∙ shareread it

Bayesian Batch Active Learning as Sparse Subset Approximation
Leveraging the wealth of unlabeled data produced in recent years provide...
08/06/2019 ∙ by Robert Pinsler, et al. ∙ 1 ∙ shareread it

Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems
Bayesian neural networks (BNNs) with latent variables are probabilistic ...
10/19/2017 ∙ by Stefan Depeweg, et al. ∙ 0 ∙ shareread it

Constrained Bayesian Optimization for Automatic Chemical Design
Automatic Chemical Design leverages recent advances in deep generative m...
09/16/2017 ∙ by RyanRhys Griffiths, et al. ∙ 0 ∙ shareread it

Actively Learning what makes a Discrete Sequence Valid
Deep learning techniques have been hugely successful for traditional sup...
08/15/2017 ∙ by David Janz, et al. ∙ 0 ∙ shareread it

Bayesian Semisupervised Learning with Deep Generative Models
Neural network based generative models with discriminative components ar...
06/29/2017 ∙ by Jonathan Gordon, et al. ∙ 0 ∙ shareread it

Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables
Bayesian neural networks (BNNs) with latent variables are probabilistic ...
06/26/2017 ∙ by Stefan Depeweg, et al. ∙ 0 ∙ shareread it

Parallel and Distributed Thompson Sampling for Largescale Accelerated Exploration of Chemical Space
Chemical space is so large that brute force searches for new interesting...
06/06/2017 ∙ by José Miguel HernándezLobato, et al. ∙ 0 ∙ shareread it

Sequence Tutor: Conservative FineTuning of Sequence Generation Models with KLcontrol
This paper proposes a general method for improving the structure and qua...
11/09/2016 ∙ by Natasha Jaques, et al. ∙ 0 ∙ shareread it

Grammar Variational Autoencoder
Deep generative models have been wildly successful at learning coherent ...
03/06/2017 ∙ by Matt J. Kusner, et al. ∙ 0 ∙ shareread it

GANS for Sequences of Discrete Elements with the Gumbelsoftmax Distribution
Generative Adversarial Networks (GAN) have limitations when the goal is ...
11/12/2016 ∙ by Matt J. Kusner, et al. ∙ 0 ∙ shareread it

Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks
We present an algorithm for modelbased reinforcement learning that comb...
05/23/2016 ∙ by Stefan Depeweg, et al. ∙ 0 ∙ shareread it

Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Deep Gaussian processes (DGPs) are multilayer hierarchical generalisati...
02/12/2016 ∙ by Thang D. Bui, et al. ∙ 0 ∙ shareread it

A General Framework for Constrained Bayesian Optimization using Informationbased Search
We present an informationtheoretic framework for solving global blackb...
11/30/2015 ∙ by José Miguel HernándezLobato, et al. ∙ 0 ∙ shareread it

Predictive Entropy Search for Multiobjective Bayesian Optimization
We present PESMO, a Bayesian method for identifying the Pareto set of mu...
11/17/2015 ∙ by Daniel HernándezLobato, et al. ∙ 0 ∙ shareread it

Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation
Deep Gaussian processes (DGPs) are multilayer hierarchical generalisati...
11/11/2015 ∙ by Thang D. Bui, et al. ∙ 0 ∙ shareread it

Stochastic Expectation Propagation for Large Scale Gaussian Process Classification
A method for large scale Gaussian process classification has been recent...
11/10/2015 ∙ by Daniel HernándezLobato, et al. ∙ 0 ∙ shareread it

Blackbox αdivergence Minimization
Blackbox alpha (BBα) is a new approximate inference method based on th...
11/10/2015 ∙ by José Miguel HernándezLobato, et al. ∙ 0 ∙ shareread it

Stochastic Expectation Propagation
Expectation propagation (EP) is a deterministic approximation algorithm ...
06/12/2015 ∙ by Yingzhen Li, et al. ∙ 0 ∙ shareread it

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
Large multilayer neural networks trained with backpropagation have recen...
02/18/2015 ∙ by José Miguel HernándezLobato, et al. ∙ 0 ∙ shareread it

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
Unknown constraints arise in many types of expensive blackbox optimizat...
02/18/2015 ∙ by José Miguel HernándezLobato, et al. ∙ 0 ∙ shareread it

Gaussian Process Volatility Model
The accurate prediction of timechanging variances is an important task ...
02/13/2014 ∙ by Yue Wu, et al. ∙ 0 ∙ shareread it

Gaussian Process Conditional Copulas with Applications to Financial Time Series
The estimation of dependencies between multiple variables is a central p...
07/01/2013 ∙ by José Miguel HernándezLobato, et al. ∙ 0 ∙ shareread it

Dynamic Covariance Models for Multivariate Financial Time Series
The accurate prediction of timechanging covariances is an important pro...
05/18/2013 ∙ by Yue Wu, et al. ∙ 0 ∙ shareread it

Gaussian Process Vine Copulas for Multivariate Dependence
Copulas allow to learn marginal distributions separately from the multiv...
02/16/2013 ∙ by David LopezPaz, et al. ∙ 0 ∙ shareread it

SemiSupervised Domain Adaptation with NonParametric Copulas
A new framework based on the theory of copulas is proposed to address se...
01/01/2013 ∙ by David LopezPaz, et al. ∙ 0 ∙ shareread it

Convergent Expectation Propagation in Linear Models with Spikeandslab Priors
Exact inference in the linear regression model with spike and slab prior...
12/10/2011 ∙ by José Miguel HernándezLobato, et al. ∙ 0 ∙ shareread it

Sensitivity Analysis for Predictive Uncertainty in Bayesian Neural Networks
We derive a novel sensitivity analysis of input variables for predictive...
12/10/2017 ∙ by Stefan Depeweg, et al. ∙ 0 ∙ shareread it

Deep Gaussian Processes with Decoupled Inducing Inputs
Deep Gaussian Processes (DGP) are hierarchical generalizations of Gaussi...
01/09/2018 ∙ by Marton Havasi, et al. ∙ 0 ∙ shareread it

Taking gradients through experiments: LSTMs and memory proximal policy optimization for blackbox quantum control
In this work we introduce the application of blackbox quantum control a...
02/12/2018 ∙ by Moritz August, et al. ∙ 0 ∙ shareread it

Predicting Electron Paths
Chemical reactions can be described as the stepwise redistribution of el...
05/23/2018 ∙ by John Bradshaw, et al. ∙ 0 ∙ shareread it

Variational Measure Preserving Flows
Probabilistic modelling is a general and elegant framework to capture th...
05/25/2018 ∙ by Yichuan Zhang, et al. ∙ 0 ∙ shareread it

Variational Implicit Processes
This paper introduces the variational implicit processes (VIPs), a Bayes...
06/06/2018 ∙ by Chao Ma, et al. ∙ 0 ∙ shareread it

Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
Deep Gaussian Processes (DGPs) are hierarchical generalizations of Gauss...
06/14/2018 ∙ by Marton Havasi, et al. ∙ 0 ∙ shareread it

MetaLearning for Stochastic Gradient MCMC
Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become increa...
06/12/2018 ∙ by Wenbo Gong, et al. ∙ 0 ∙ shareread it

Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
While deep neural networks are a highly successful model class, their la...
09/30/2018 ∙ by Marton Havasi, et al. ∙ 0 ∙ shareread it

Successor Uncertainties: exploration and uncertainty in temporal difference learning
We consider the problem of balancing exploration and exploitation in seq...
10/15/2018 ∙ by David Janz, et al. ∙ 0 ∙ shareread it

A COLD Approach to Generating Optimal Samples
Optimising discrete data for a desired characteristic using gradientbas...
05/23/2019 ∙ by Omar Mahmood, et al. ∙ 0 ∙ shareread it

A Generative Model for Molecular Distance Geometry
Computing equilibrium states for manybody systems, such as molecules, i...
09/25/2019 ∙ by Gregor N. C. Simm, et al. ∙ 0 ∙ shareread it
José Miguel HernándezLobato
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University Lecturer (US Assistant Professor) University of Cambridge