
SampleEfficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
Many important problems in science and engineering, such as drug design,...
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VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
Deep generative models often perform poorly in realworld applications d...
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Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care
Clinical decision making is challenging because of pathological complexi...
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Deconfounding Reinforcement Learning in Observational Settings
We propose a general formulation for addressing reinforcement learning (...
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A Model to Search for Synthesizable Molecules
Deep generative models are able to suggest new organic molecules by gene...
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EDDI: Efficient Dynamic Discovery of HighValue Information with Partial VAE
Making decisions requires information relevant to the task at hand. Many...
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Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks
Bayesian neural networks (BNNs) hold great promise as a flexible and pri...
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Icebreaker: Elementwise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
In this paper we introduce the icestart problem, i.e., the challenge of...
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'InBetween' Uncertainty in Bayesian Neural Networks
We describe a limitation in the expressiveness of the predictive uncerta...
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Bayesian Batch Active Learning as Sparse Subset Approximation
Leveraging the wealth of unlabeled data produced in recent years provide...
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Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems
Bayesian neural networks (BNNs) with latent variables are probabilistic ...
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Constrained Bayesian Optimization for Automatic Chemical Design
Automatic Chemical Design leverages recent advances in deep generative m...
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Actively Learning what makes a Discrete Sequence Valid
Deep learning techniques have been hugely successful for traditional sup...
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Bayesian Semisupervised Learning with Deep Generative Models
Neural network based generative models with discriminative components ar...
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Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables
Bayesian neural networks (BNNs) with latent variables are probabilistic ...
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Parallel and Distributed Thompson Sampling for Largescale Accelerated Exploration of Chemical Space
Chemical space is so large that brute force searches for new interesting...
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Sequence Tutor: Conservative FineTuning of Sequence Generation Models with KLcontrol
This paper proposes a general method for improving the structure and qua...
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Grammar Variational Autoencoder
Deep generative models have been wildly successful at learning coherent ...
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GANS for Sequences of Discrete Elements with the Gumbelsoftmax Distribution
Generative Adversarial Networks (GAN) have limitations when the goal is ...
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Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks
We present an algorithm for modelbased reinforcement learning that comb...
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Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Deep Gaussian processes (DGPs) are multilayer hierarchical generalisati...
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A General Framework for Constrained Bayesian Optimization using Informationbased Search
We present an informationtheoretic framework for solving global blackb...
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Predictive Entropy Search for Multiobjective Bayesian Optimization
We present PESMO, a Bayesian method for identifying the Pareto set of mu...
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Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation
Deep Gaussian processes (DGPs) are multilayer hierarchical generalisati...
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Stochastic Expectation Propagation for Large Scale Gaussian Process Classification
A method for large scale Gaussian process classification has been recent...
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Blackbox αdivergence Minimization
Blackbox alpha (BBα) is a new approximate inference method based on th...
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Stochastic Expectation Propagation
Expectation propagation (EP) is a deterministic approximation algorithm ...
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Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
Large multilayer neural networks trained with backpropagation have recen...
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Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
Unknown constraints arise in many types of expensive blackbox optimizat...
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Gaussian Process Volatility Model
The accurate prediction of timechanging variances is an important task ...
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Gaussian Process Conditional Copulas with Applications to Financial Time Series
The estimation of dependencies between multiple variables is a central p...
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Dynamic Covariance Models for Multivariate Financial Time Series
The accurate prediction of timechanging covariances is an important pro...
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Gaussian Process Vine Copulas for Multivariate Dependence
Copulas allow to learn marginal distributions separately from the multiv...
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SemiSupervised Domain Adaptation with NonParametric Copulas
A new framework based on the theory of copulas is proposed to address se...
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Convergent Expectation Propagation in Linear Models with Spikeandslab Priors
Exact inference in the linear regression model with spike and slab prior...
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Sensitivity Analysis for Predictive Uncertainty in Bayesian Neural Networks
We derive a novel sensitivity analysis of input variables for predictive...
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Deep Gaussian Processes with Decoupled Inducing Inputs
Deep Gaussian Processes (DGP) are hierarchical generalizations of Gaussi...
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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...
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Predicting Electron Paths
Chemical reactions can be described as the stepwise redistribution of el...
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Variational Measure Preserving Flows
Probabilistic modelling is a general and elegant framework to capture th...
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Variational Implicit Processes
This paper introduces the variational implicit processes (VIPs), a Bayes...
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Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
Deep Gaussian Processes (DGPs) are hierarchical generalizations of Gauss...
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MetaLearning for Stochastic Gradient MCMC
Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become increa...
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Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
While deep neural networks are a highly successful model class, their la...
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Successor Uncertainties: exploration and uncertainty in temporal difference learning
We consider the problem of balancing exploration and exploitation in seq...
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A COLD Approach to Generating Optimal Samples
Optimising discrete data for a desired characteristic using gradientbas...
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A Generative Model for Molecular Distance Geometry
Computing equilibrium states for manybody systems, such as molecules, i...
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Bayesian Variational Autoencoders for Unsupervised OutofDistribution Detection
Despite their successes, deep neural networks still make unreliable pred...
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Variational Depth Search in ResNets
Oneshot neural architecture search allows joint learning of weights and...
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Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
Automating molecular design using deep reinforcement learning (RL) holds...
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José Miguel HernándezLobato
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University Lecturer (US Assistant Professor) University of Cambridge