Yarin Gal

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Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models

Carl E. Rasmussen, Mark van der Wilk, Yarin Gal, 2014, NIPS.

Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning pdf

Yarin Gal, Andreas Kirsch, Clare Lyle, 2020, ArXiv.

How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?

Yee Whye Teh, Yarin Gal, Leonid Chindelevitch, 2020, NeurIPS.

Conditional BRUNO: a neural process for exchangeable labelled data

Arthur Gretton, Joni Dambre, Yarin Gal, 2020, ESANN.

Flood Detection On Low Cost Orbital Hardware pdf

Yarin Gal, Gonzalo Mateo-Garcia, Silviu Oprea, 2019, ArXiv.

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

Alex Kendall, Yarin Gal, Y. Gal, 2017, NIPS.

Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

Sergey Levine, Nicholas Rhinehart, Rowan McAllister, 2020, ICML.

Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations

Yarin Gal, Lewis Smith, Sebastian Farquhar, 2020, NeurIPS.

Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning pdf

Yarin Gal, Lucy Fortson, Mike Walmsley, 2019, Monthly Notices of the Royal Astronomical Society.

Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network pdf

Yee Whye Teh, Yarin Gal, Lewis Smith, 2020, ICML.

Concrete Dropout

Alex Kendall, Yarin Gal, Jiri Hron, 2017, NIPS.

Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning

Roberto Cipolla, Mark van der Wilk, Adrian Weller, 2017, IJCAI.

Towards Robust Evaluations of Continual Learning pdf

Yarin Gal, Sebastian Farquhar, Y. Gal, 2018, ArXiv.

Using U-Nets to Create High-Fidelity Virtual Observations of the Solar Corona pdf

Meng Jin, Yarin Gal, Mark Cheung, 2019, ArXiv.

Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs

Richard E. Turner, Richard Turner, Yarin Gal, 2015, ICML.

A Unifying Bayesian View of Continual Learning pdf

Yarin Gal, Sebastian Farquhar, Y. Gal, 2019, ArXiv.

Uncertainty Evaluation Metric for Brain Tumour Segmentation pdf

Raghav Mehta, Tal Arbel, Yarin Gal, 2020, ArXiv.

Dropout as a Bayesian Approximation: Appendix

Zoubin Ghahramani, Yarin Gal, Zoubin Ghahramani, 2015, 1506.02157.

Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search pdf

Mark van der Wilk, Yarin Gal, Clare Lyle, 2020, ArXiv.

Sufficient Conditions for Idealised Models to Have No Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks

Yarin Gal, Lewis Smith, Y. Gal, 2018, 1806.00667.

Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers pdf

Yarin Gal, Aidan N. Gomez, Tim Z. Xiao, 2020, ArXiv.

Invariant Causal Prediction for Block MDPs pdf

Joelle Pineau, Doina Precup, Yarin Gal, 2020, ICML.

A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks pdf

Yarin Gal, Lewis Smith, Milad Alizadeh, 2019, ArXiv.

Prediction of GNSS Phase Scintillations: A Machine Learning Approach pdf

Anahita Bhiwandiwalla, Asti Bhatt, Anthony Reina, 2019, ArXiv.

Differentially Private Continual Learning pdf

Yarin Gal, Sebastian Farquhar, Y. Gal, 2019, ArXiv.

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

Zoubin Ghahramani, Yarin Gal, Zoubin Ghahramani, 2015, NIPS.

Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam

Didrik Nielsen, Mohammad Emtiyaz Khan, Voot Tangkaratt, 2018, ICML.

On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes pdf

Tom Rainforth, Yarin Gal, Tim G. J. Rudner, 2020, ArXiv.

Identifying Causal Effect Inference Failure with Uncertainty-Aware Models pdf

Uri Shalit, Yarin Gal, Soren Mindermann, 2020, NeurIPS.

Adversarial recovery of agent rewards from latent spaces of the limit order book pdf

Yuanbo Wang, Ricardo Silva, Yarin Gal, 2019, ArXiv.

Exoplanetary Atmospheric Retrieval via Bayesian Machine Learning

Shawn Domagal-Goldman, Daniel Angerhausen, Giada Arney, 2019 .

Uncertainty in Deep Learning

Yarin Gal, Y. Gal, 2016 .

On the Benefits of Invariance in Neural Networks pdf

Mark van der Wilk, Yarin Gal, Clare Lyle, 2020, ArXiv.

Dropout Inference in Bayesian Neural Networks with Alpha-divergences

Yarin Gal, Yingzhen Li, Y. Gal, 2017, ICML.

Overcoming Alpha-Beta Limitations Using Evolved Artificial Neural Networks

Mireille Avigal, Yarin Gal, 2010, 2010 Ninth International Conference on Machine Learning and Applications.

Pitfalls in the use of Parallel Inference for the Dirichlet Process

Zoubin Ghahramani, Yarin Gal, Zoubin Ghahramani, 2014, ICML.

Machine Learning for Generalizable Prediction of Flood Susceptibility pdf

Yarin Gal, Piotr Bilinski, Dylan J. Fitzpatrick, 2019, ArXiv.

Loss-Calibrated Approximate Inference in Bayesian Neural Networks pdf

Stephen J. Roberts, Yarin Gal, Adam D. Cobb, 2018, ArXiv.

Using Bayesian Optimization to Find Asteroids' Pole Directions

Chedy Raïssi, Yarin Gal, Adam D. Cobb, 2018 .

BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning

Yarin Gal, Andreas Kirsch, Joost van Amersfoort, 2019, NeurIPS.

Generalizing from a few environments in safety-critical reinforcement learning pdf

Yarin Gal, Angelos Filos, Owain Evans, 2019, ArXiv.

FDL: Mission Support Challenge

Yarin Gal, Callum Wilson, Ben Day, 2019 .

Interlocking Backpropagation: Improving depthwise model-parallelism pdf

Yarin Gal, Aidan N. Gomez, Jeff Dean, 2020, ArXiv.

On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission pdf

Yee Whye Teh, Yarin Gal, Leonid Chindelevitch, 2020, ArXiv.

Model and Data Uncertainty for Satellite Time Series Forecasting with Deep Recurrent Models

Marc Rußwurm, Xiao Xiang Zhu, Yarin Gal, 2020, IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium.

BayesOpt Adversarial Attack

Arno Blaas, Yarin Gal, Binxin Ru, 2020, ICLR.

On Batch Normalisation for Approximate Bayesian Inference pdf

Philip H.S. Torr, Jishnu Mukhoti, Yarin Gal, 2020, ArXiv.

Semantics, Modelling, and the Problem of Representation of Meaning - a Brief Survey of Recent Literature pdf

Yarin Gal, Y. Gal, 2014, ArXiv.

Deep Bayesian Active Learning with Image Data pdf

Zoubin Ghahramani, Yarin Gal, Riashat Islam, 2017, ICML.

An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval pdf

Daniel Angerhausen, Yarin Gal, Adam D. Cobb, 2019, The Astronomical Journal.

Single Shot Structured Pruning Before Training pdf

Nicholas Lane, Yarin Gal, Milad Alizadeh, 2020, ArXiv.

Inter-domain Deep Gaussian Processes with RKHS Fourier Features

Yarin Gal, Tim G. J. Rudner, Dino Sejdinovic, 2020, International Conference on Machine Learning.

Semi-supervised Learning of Galaxy Morphology using Equivariant Transformer Variational Autoencoders pdf

Yarin Gal, Lewis Smith, Mizu Nishikawa-Toomey, 2020, ArXiv.

Idealised Bayesian Neural Networks Cannot Have Adversarial Examples: Theoretical and Empirical Study pdf

Yarin Gal, Lewis Smith, 2018, ArXiv.

Model-based Saliency for the Detection of Adversarial Examples

Yarin Gal, Lisa Schut, Y. Gal, 2019 .

Learning Sparse Networks Using Targeted Dropout pdf

Geoffrey E. Hinton, Yarin Gal, Kevin Swersky, 2019, ArXiv.

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning pdf

Zoubin Ghahramani, Yarin Gal, Zoubin Ghahramani, 2015, ICML.

Learning Invariant Representations for Reinforcement Learning without Reconstruction pdf

Rowan McAllister, Yarin Gal, Roberto Calandra, 2020, ICLR.

A Bayesian Perspective on Training Speed and Model Selection pdf

Mark van der Wilk, Yarin Gal, Clare Lyle, 2020, NeurIPS.

Evaluating Bayesian Deep Learning Methods for Semantic Segmentation pdf

Jishnu Mukhoti, Yarin Gal, Y. Gal, 2018, ArXiv.

Real Time Image Saliency for Black Box Classifiers

Yarin Gal, Piotr Dabkowski, Y. Gal, 2017, NIPS.

Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder pdf

Anahita Bhiwandiwalla, Asti Bhatt, Anthony Reina, 2019, ArXiv.

A Systematic Bayesian Treatment of the IBM Alignment Models

Phil Blunsom, Yarin Gal, 2013, HLT-NAACL.

Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties pdf

Atilim Gunecs Baydin, Carl Shneider, Yarin Gal, 2019, ArXiv.

Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

Roberto Cipolla, Alex Kendall, Yarin Gal, 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data

Zoubin Ghahramani, Yutian Chen, Yarin Gal, 2015, ICML.

A Generative Deep Recurrent Model for Exchangeable Data

Arthur Gretton, Jonas Degrave, Joni Dambre, 2018 .

INARA: Intelligent exoplaNet Atmospheric RetrievAl A Machine Learning Retrieval Framework with a Data Set of 3 Million Simulated Exoplanet Atmospheric Spectra

Shawn Domagal-Goldman, Daniel Angerhausen, Giada Arney, 2019 .

Capsule Networks - A Probabilistic Perspective pdf

Mark van der Wilk, Yarin Gal, Lewis Smith, 2020, ArXiv.

BRUNO: A Deep Recurrent Model for Exchangeable Data

Arthur Gretton, Jonas Degrave, Joni Dambre, 2018, NeurIPS.

Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning

Roberto Cipolla, Mark van der Wilk, Adrian Weller, 2017, IJCAI 2017.

Physics-informed GANs for Coastal Flood Visualization pdf

Chedy Raïssi, Farrukh Chishtie, Christian Requena-Mesa, 2020, ArXiv.

Radial Bayesian Neural Networks: Robust Variational Inference In Big Models pdf

Yarin Gal, Sebastian Farquhar, Michael Osborne, 2019, ArXiv.

An Empirical study of Binary Neural Networks' Optimisation

Nicholas D. Lane, Yarin Gal, Javier Fernández-Marqués, 2018, ICLR.

Towards Inverse Reinforcement Learning for Limit Order Book Dynamics pdf

Yarin Gal, Angelos Filos, Ricardo Silva, 2019, ICML 2019.

Understanding Measures of Uncertainty for Adversarial Example Detection pdf

Yarin Gal, Lewis Smith, Y. Gal, 2018, UAI.

Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference pdf

Zoubin Ghahramani, Yarin Gal, Zoubin Ghahramani, 2015, ArXiv.

Sufficient Conditions for Robustness to Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks

Yarin Gal, Lewis Smith, Y. Gal, 2018 .

Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control pdf

Marta Z. Kwiatkowska, Yarin Gal, Rhiannon Michelmore, 2018, ArXiv.

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning pdf

Shimon Whiteson, Sebastian Schulze, Katja Hofmann, 2020, ICLR.

SliceOut: Training Transformers and CNNs faster while using less memory pdf

Yarin Gal, Aidan N. Gomez, Pascal Notin, 2020, ArXiv.

Baryons from Mesons: A Machine Learning Perspective pdf

Yarin Gal, Vishnu Jejjala, Damian Kaloni Mayorga Pena, 2020, ArXiv.

Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning pdf

Meng Jin, Yarin Gal, Mark Cheung, 2019, ArXiv.

Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning

Michael A. Osborne, Yarin Gal, Sebastian Farquhar, 2020, AISTATS.

Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models - a Gentle Tutorial

Mark van der Wilk, Yarin Gal, 2014 .

Try Depth Instead of Weight Correlations: Mean-field is a Less Restrictive Assumption for Deeper Networks pdf

Yarin Gal, Lewis Smith, Sebastian Farquhar, 2020, ArXiv.

Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses pdf

Carl Shneider, Yarin Gal, Atilim Günes Baydin, 2019, ArXiv.

Vprop: Variational Inference using RMSprop pdf

Mohammad Emtiyaz Khan, Voot Tangkaratt, Zuozhu Liu, 2017, ArXiv.

On the Importance of Strong Baselines in Bayesian Deep Learning pdf

Jishnu Mukhoti, Yarin Gal, Pontus Stenetorp, 2018, ArXiv.

Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control

Luca Cardelli, Matthew Wicker, Luca Laurenti, 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).

Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning

Meng Jin, Atilim Gunecs Baydin, Mark C. M. Cheung, 2020, Astronomy & Astrophysics.

Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific pdf

Duncan Watson-Parris, Yarin Gal, Philip Stier, 2021, ArXiv.

Deterministic Neural Networks with Inductive Biases Capture Epistemic and Aleatoric Uncertainty

Philip H. S. Torr, Philip H.S. Torr, Jishnu Mukhoti, 2021 .

Domain Invariant Representation Learning with Domain Density Transformations pdf

Yarin Gal, Atilim Gunes Baydin, Toan Tran, 2021, ArXiv.

Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition pdf

Asti Bhatt, Yarin Gal, Ryan M. McGranaghan, 2021, ArXiv.

Contrastive Representation Learning with Trainable Augmentation Channel pdf

Yarin Gal, Takeru Miyato, Masanori Koyama, 2021, ArXiv.

Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers

Tom Rainforth, Yarin Gal, Andreas Kirsch, 2021, 2106.11719.

Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding pdf

Uri Shalit, Yarin Gal, Andrew Jesson, 2021, ICML.

Prioritized training on points that are learnable, worth learning, and not yet learned pdf

Yarin Gal, Andreas Kirsch, Mrinank Sharma, 2021, ArXiv.

Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data pdf

Uri Shalit, Yarin Gal, Andreas Kirsch, 2021, ArXiv.

Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty pdf

Philip H. S. Torr, Philip H.S. Torr, Jishnu Mukhoti, 2021, ArXiv.

Robustness to Pruning Predicts Generalization in Deep Neural Networks pdf

Yarin Gal, Clare Lyle, Aidan N. Gomez, 2021, ArXiv.

Deep Deterministic Uncertainty for Semantic Segmentation pdf

Philip H. S. Torr, Philip H.S. Torr, Yarin Gal, 2021, ArXiv.

Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation pdf

Yarin Gal, Gonzalo Mateo-Garcia, Freddie Kalaitzis, 2021, ISPRS Journal of Photogrammetry and Remote Sensing.

Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning pdf

Tom Rainforth, Yarin Gal, Clare Lyle, 2021, ArXiv.

Outcome-Driven Reinforcement Learning via Variational Inference pdf

Sergey Levine, Rowan McAllister, Yarin Gal, 2021, NeurIPS.

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning pdf

D. Sculley, Jasper Snoek, Jeremy Nixon, 2021, ArXiv.

A Practical & Unified Notation for Information-Theoretic Quantities in ML pdf

Yarin Gal, Andreas Kirsch, Y. Gal, 2021, ArXiv.

Decomposing Representations for Deterministic Uncertainty Estimation pdf

Yarin Gal, Joost van Amersfoort, Haiwen Huang, 2021, ArXiv.

On Statistical Bias In Active Learning: How and When To Fix It pdf

Tom Rainforth, Yarin Gal, Sebastian Farquhar, 2021, ICLR.

On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty

Yarin Gal, Lewis Smith, Joost van Amersfoort, 2021 .

DeDUCE: Generating Counterfactual Explanations Efficiently pdf

Yarin Gal, Jan Markus Brauner, Lisa Schut, 2021, ArXiv.

On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes

Tom Rainforth, Yarin Gal, Tim G. J. Rudner, 2021, ICML.

Uncertainty quantification for virtual diagnostic of particle accelerators

Yarin Gal, Lewis Smith, Adi Hanuka, 2021, Physical Review Accelerators and Beams.

Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization pdf

Chedy Raïssi, Christian Requena-Mesa, Yarin Gal, 2021, ArXiv.

Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks pdf

Mark J. F. Gales, Andrey Malinin, Alexey Noskov, 2021, ArXiv.

Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression pdf

Yarin Gal, Lewis Smith, Joost van Amersfoort, 2021, ArXiv.

Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective pdf

Stephen Roberts, Yarin Gal, Lewis Smith, 2021, ArXiv.

Active Learning under Pool Set Distribution Shift and Noisy Data pdf

Tom Rainforth, Yarin Gal, Andreas Kirsch, 2021, ArXiv.

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results pdf

Christos Davatzikos, Gowtham Krishnan Murugesan, Chandan Ganesh, 2021, ArXiv.

A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions pdf

Yarin Gal, Andreas Kirsch, Sebastian Farquhar, 2021, ArXiv.

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

Jos'e Miguel Hern'andez-Lobato, José Miguel Hernández-Lobato, Yarin Gal, 2021, NeurIPS.

Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties

Bogdan Sacaleanu, Yarin Gal, Luca Costabello, 2021, AISTATS.

Quantifying Uncertainty for Machine Learning Based Diagnostic pdf

Yarin Gal, Lewis Smith, Adi Hanuka, 2021, ArXiv.

KL Guided Domain Adaptation pdf

Philip H. S. Torr, Atilim Gunecs Baydin, Yarin Gal, 2021, ArXiv.

Active Testing: Sample-Efficient Model Evaluation

Tom Rainforth, Yarin Gal, Sebastian Farquhar, 2021, ICML.

DARTS without a Validation Set: Optimizing the Marginal Likelihood pdf

Yarin Gal, Clare Lyle, Binxin Ru, 2021, ArXiv.

GeneDisco: A Benchmark for Experimental Design in Drug Discovery pdf

Arash Mehrjou, Yarin Gal, Stefan Bauer, 2021, ICLR.

PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning

Sergey Levine, Natasha Jaques, Gregory Farquhar, 2021, ICML.

Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning

Michael A. Osborne, Yarin Gal, Sebastian Farquhar, 2019, AISTATS.

BINARY NEURAL NETWORKS’ OPTIMISATION

Nicholas D. Lane, Yarin Gal, Javier Fernández-Marqués, 2019 .

Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning pdf

Yarin Gal, Andreas Kirsch, Clare Lyle, 2020, ArXiv.

On Modern Deep Learning and Variational Inference

Zoubin Ghahramani, Yarin Gal, 2015 .