José Miguel Hernández-Lobato

research

∙ 08/23/2023

Graph Neural Stochastic Differential Equations

We present a novel model Graph Neural Stochastic Differential Equations ...

0 Richard Bergna, et al. ∙

research

∙ 08/20/2023

SE(3) Equivariant Augmented Coupling Flows

Coupling normalizing flows allow for fast sampling and density evaluatio...

0 Laurence I. Midgley, et al. ∙

research

∙ 07/15/2023

Minimal Random Code Learning with Mean-KL Parameterization

This paper studies the qualitative behavior and robustness of two varian...

0 Jihao Andreas Lin, et al. ∙

research

∙ 07/12/2023

Online Laplace Model Selection Revisited

The Laplace approximation provides a closed-form model selection objecti...

0 Jihao Andreas Lin, et al. ∙

research

∙ 06/26/2023

Leveraging Task Structures for Improved Identifiability in Neural Network Representations

This work extends the theory of identifiability in supervised learning b...

0 Wenlin Chen, et al. ∙

research

∙ 06/26/2023

Tanimoto Random Features for Scalable Molecular Machine Learning

The Tanimoto coefficient is commonly used to measure the similarity betw...

0 Austin Tripp, et al. ∙

research

∙ 06/20/2023

Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent

Gaussian processes are a powerful framework for quantifying uncertainty ...

0 Jihao Andreas Lin, et al. ∙

research

∙ 05/30/2023

Compression with Bayesian Implicit Neural Representations

Many common types of data can be represented as functions that map coord...

0 Zongyu Guo, et al. ∙

research

∙ 02/20/2023

Fast and Painless Image Reconstruction in Deep Image Prior Subspaces

The deep image prior (DIP) is a state-of-the-art unsupervised approach f...

0 Riccardo Barbano, et al. ∙

research

∙ 10/10/2022

Sampling-based inference for large linear models, with application to linearised Laplace

Large-scale linear models are ubiquitous throughout machine learning, wi...

0 Javier Antoran, et al. ∙

research

∙ 08/03/2022

Flow Annealed Importance Sampling Bootstrap

Normalizing flows are tractable density models that can approximate comp...

12 Laurence Illing Midgley, et al. ∙

research

∙ 07/11/2022

Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior

We investigate adaptive design based on a single sparse pilot scan for g...

0 Riccardo Barbano, et al. ∙

research

∙ 06/17/2022

Adapting the Linearised Laplace Model Evidence for Modern Deep Learning

The linearised Laplace method for estimating model uncertainty has recei...

0 Javier Antoran, et al. ∙

research

∙ 05/05/2022

Meta-learning Feature Representations for Adaptive Gaussian Processes via Implicit Differentiation

We propose Adaptive Deep Kernel Fitting (ADKF), a general framework for ...

1 Wenlin Chen, et al. ∙

research

∙ 02/28/2022

A Probabilistic Deep Image Prior for Computational Tomography

Existing deep-learning based tomographic image reconstruction methods do...

14 Javier Antoran, et al. ∙

research

∙ 02/09/2022

Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo

Variational Autoencoders (VAEs) have recently been highly successful at ...

8 Ignacio Peis, et al. ∙

research

∙ 01/30/2022

Fast Relative Entropy Coding with A* coding

Relative entropy coding (REC) algorithms encode a sample from a target d...

0 Gergely Flamich, et al. ∙

research

∙ 12/13/2021

Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not

Farquhar et al. [2021] show that correcting for active learning bias wit...

0 Chelsea Murray, et al. ∙

research

∙ 12/13/2021

Depth Uncertainty Networks for Active Learning

In active learning, the size and complexity of the training dataset chan...

0 Chelsea Murray, et al. ∙

research

∙ 11/22/2021

Bootstrap Your Flow

Normalizing flows are flexible, parameterized distributions that can be ...

0 Laurence Illing Midgley, et al. ∙

research

∙ 10/29/2021

Resampling Base Distributions of Normalizing Flows

Normalizing flows are a popular class of models for approximating probab...

1 Vincent Stimper, et al. ∙

research

∙ 10/29/2021

DOCKSTRING: easy molecular docking yields better benchmarks for ligand design

The field of machine learning for drug discovery is witnessing an explos...

0 Miguel Garcia-Ortegon, et al. ∙

research

∙ 10/20/2021

Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation

Machine learning training methods depend plentifully and intricately on ...

0 Ross M. Clarke, et al. ∙

research

∙ 10/12/2021

Action-Sufficient State Representation Learning for Control with Structural Constraints

Perceived signals in real-world scenarios are usually high-dimensional a...

12 Biwei Huang, et al. ∙

research

∙ 06/30/2021

Improving black-box optimization in VAE latent space using decoder uncertainty

Optimization in the latent space of variational autoencoders is a promis...

0 Pascal Notin, et al. ∙

research

∙ 02/24/2021

Nonlinear Invariant Risk Minimization: A Causal Approach

Due to spurious correlations, machine learning systems often fail to gen...

38 Chaochao Lu, et al. ∙

research

∙ 02/05/2021

Active Slices for Sliced Stein Discrepancy

Sliced Stein discrepancy (SSD) and its kernelized variants have demonstr...

0 Wenbo Gong, et al. ∙

research

∙ 12/21/2020

Barking up the right tree: an approach to search over molecule synthesis DAGs

When designing new molecules with particular properties, it is not only ...

11 John Bradshaw, et al. ∙

research

∙ 12/16/2020

Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation

Reinforcement learning (RL) algorithms usually require a substantial amo...

20 Chaochao Lu, et al. ∙

research

∙ 12/02/2020

FIT: a Fast and Accurate Framework for Solving Medical Inquiring and Diagnosing Tasks

Automatic self-diagnosis provides low-cost and accessible healthcare via...

0 Weijie He, et al. ∙

research

∙ 11/25/2020

Symmetry-Aware Actor-Critic for 3D Molecular Design

Automating molecular design using deep reinforcement learning (RL) has t...

0 Gregor N. C. Simm, et al. ∙

research

∙ 10/28/2020

Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference

The Bayesian paradigm has the potential to solve some of the core issues...

0 Erik Daxberger, et al. ∙

research

∙ 10/02/2020

Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding

Variational Autoencoders (VAEs) have seen widespread use in learned imag...

0 Gergely Flamich, et al. ∙

research

∙ 07/23/2020

Diagnostic Questions:The NeurIPS 2020 Education Challenge

Digital technologies are becoming increasingly prevalent in education, e...

0 Zichao Wang, et al. ∙

research

∙ 07/16/2020

DRIFT: Deep Reinforcement Learning for Functional Software Testing

Efficient software testing is essential for productive software developm...

32 Luke Harries, et al. ∙

research

∙ 06/30/2020

Sliced Kernelized Stein Discrepancy

Kernelized Stein discrepancy (KSD), though being extensively used in goo...

0 Wenbo Gong, et al. ∙

research

∙ 06/21/2020

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data

Deep generative models often perform poorly in real-world applications d...

14 Chao Ma, et al. ∙

research

∙ 06/18/2020

Predictive Complexity Priors

Specifying a Bayesian prior is notoriously difficult for complex models ...

0 Eric Nalisnick, et al. ∙

research

∙ 06/16/2020

Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining

Many important problems in science and engineering, such as drug design,...

31 Austin Tripp, et al. ∙

research

∙ 06/15/2020

Depth Uncertainty in Neural Networks

Existing methods for estimating uncertainty in deep learning tend to req...

0 Javier Antoran, et al. ∙

research

∙ 06/11/2020

Getting a CLUE: A Method for Explaining Uncertainty Estimates

Both uncertainty estimation and interpretability are important factors f...

0 Javier Antoran, et al. ∙

research

∙ 05/15/2020

Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures

When learning to ride a bike, a child falls down a number of times befor...

0 Alonso Marco, et al. ∙

research

∙ 03/12/2020

Large-Scale Educational Question Analysis with Partial Variational Auto-encoders

Online education platforms enable teachers to share a large number of ed...

0 Zichao Wang, et al. ∙

research

∙ 02/18/2020

Reinforcement Learning for Molecular Design Guided by Quantum Mechanics

Automating molecular design using deep reinforcement learning (RL) holds...

0 Gregor N. C. Simm, et al. ∙

research

∙ 02/06/2020

Variational Depth Search in ResNets

One-shot neural architecture search allows joint learning of weights and...

0 Javier Antoran, et al. ∙

research

∙ 12/11/2019

Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection

Despite their successes, deep neural networks still make unreliable pred...

0 Erik Daxberger, et al. ∙

research

∙ 09/25/2019

A Generative Model for Molecular Distance Geometry

Computing equilibrium states for many-body systems, such as molecules, i...

0 Gregor N. C. Simm, et al. ∙

research

∙ 08/13/2019

Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model

In this paper we introduce the ice-start problem, i.e., the challenge of...

3 Wenbo Gong, et al. ∙

research

∙ 08/06/2019

Bayesian Batch Active Learning as Sparse Subset Approximation

Leveraging the wealth of unlabeled data produced in recent years provide...

1 Robert Pinsler, et al. ∙

research

∙ 06/27/2019

'In-Between' Uncertainty in Bayesian Neural Networks

We describe a limitation in the expressiveness of the predictive uncerta...

1 Andrew Y. K. Foong, et al. ∙

José Miguel Hernández-Lobato

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