Overcoming Alpha-Beta Limitations Using Evolved Artificial Neural Networks. - Yarin Gal; Mireille Avigal (ICMLA, 2010)

A Systematic Bayesian Treatment of the IBM Alignment Models. - Yarin Gal; Phil Blunsom (HLT-NAACL, 2013)

Pitfalls in the use of Parallel Inference for the Dirichlet Process. - Yarin Gal; Zoubin Ghahramani (ICML, 2014)

Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models. - Yarin Gal; Mark van der Wilk; Carl E. Rasmussen (NIPS, 2014)

Semantics, Modelling, and the Problem of Representation of Meaning - a Brief Survey of Recent Literature. - Yarin Gal (CoRR, 2014)

Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data. - Yarin Gal; Yutian Chen 001; Zoubin Ghahramani (ICML, 2015)

Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs. - Yarin Gal; Richard E. Turner (ICML, 2015)

Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference. - Yarin Gal; Zoubin Ghahramani (CoRR, 2015)

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. - Yarin Gal; Zoubin Ghahramani (ICML, 2016)

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks. - Yarin Gal; Zoubin Ghahramani (NIPS, 2016)

Deep Bayesian Active Learning with Image Data. - Yarin Gal; Riashat Islam; Zoubin Ghahramani (ICML, 2017)

Dropout Inference in Bayesian Neural Networks with Alpha-divergences. - Yingzhen Li; Yarin Gal (ICML, 2017)

Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning. - Rowan McAllister; Yarin Gal; Alex Kendall; Mark van der Wilk; Amar Shah 001; Roberto Cipolla; Adrian Weller (IJCAI, 2017)

Real Time Image Saliency for Black Box Classifiers. - Piotr Dabkowski; Yarin Gal (NIPS, 2017)

Concrete Dropout. - Yarin Gal; Jiri Hron; Alex Kendall (NIPS, 2017)

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? - Alex Kendall; Yarin Gal (NIPS, 2017)

Vprop: Variational Inference using RMSprop. - Mohammad Emtiyaz Khan; Zuozhu Liu; Voot Tangkaratt; Yarin Gal (CoRR, 2017)

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics. - Alex Kendall; Yarin Gal; Roberto Cipolla (CVPR, 2018)

Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. - Mohammad Emtiyaz Khan; Didrik Nielsen; Voot Tangkaratt; Wu Lin; Yarin Gal; Akash Srivastava (ICML, 2018)

BRUNO: A Deep Recurrent Model for Exchangeable Data. - Iryna Korshunova; Jonas Degrave; Ferenc Huszar; Yarin Gal; Arthur Gretton; Joni Dambre (NeurIPS, 2018)

Understanding Measures of Uncertainty for Adversarial Example Detection. - Lewis Smith; Yarin Gal (UAI, 2018)

Loss-Calibrated Approximate Inference in Bayesian Neural Networks. - Adam D. Cobb; Stephen J. Roberts; Yarin Gal (CoRR, 2018)

Towards Robust Evaluations of Continual Learning. - Sebastian Farquhar; Yarin Gal (CoRR, 2018)

Idealised Bayesian Neural Networks Cannot Have Adversarial Examples: Theoretical and Empirical Study. - Yarin Gal; Lewis Smith (CoRR, 2018)

Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control. - Rhiannon Michelmore; Marta Kwiatkowska; Yarin Gal (CoRR, 2018)

On the Importance of Strong Baselines in Bayesian Deep Learning. - Jishnu Mukhoti; Pontus Stenetorp; Yarin Gal (CoRR, 2018)

Evaluating Bayesian Deep Learning Methods for Semantic Segmentation. - Jishnu Mukhoti; Yarin Gal (CoRR, 2018)

An Empirical study of Binary Neural Networks' Optimisation. - Milad Alizadeh; Javier Fernández-Marqués; Nicholas D. Lane; Yarin Gal (ICLR (Poster), 2019)

BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning. - Andreas Kirsch 002; Joost van Amersfoort; Yarin Gal (NeurIPS, 2019)

A Unifying Bayesian View of Continual Learning. - Sebastian Farquhar; Yarin Gal (CoRR, 2019)

Differentially Private Continual Learning. - Sebastian Farquhar; Yarin Gal (CoRR, 2019)

Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning. - Mike Walmsley; Lewis Smith; Chris J. Lintott; Yarin Gal; Steven Bamford; Hugh Dickinson; Lucy Fortson; Sandor Kruk; Karen Masters; Claudia Scarlata; Brooke Simmons; Rebecca J. Smethurst; et al. (CoRR, 2019)

An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval. - Adam D. Cobb; Michael D. Himes; Frank Soboczenski; Simone Zorzan; Molly D. O'Beirne; Atilim Günes Baydin; Yarin Gal; Shawn D. Domagal-Goldman; Giada N. Arney; Daniel Angerhausen (CoRR, 2019)

Learning Sparse Networks Using Targeted Dropout. - Aidan N. Gomez; Ivan Zhang; Kevin Swersky; Yarin Gal; Geoffrey E. Hinton (CoRR, 2019)

Towards Inverse Reinforcement Learning for Limit Order Book Dynamics. - Jacobo Roa-Vicens; Cyrine Chtourou; Angelos Filos; Francisco Rullan; Yarin Gal; Ricardo Silva 001 (CoRR, 2019)

Radial Bayesian Neural Networks: Robust Variational Inference In Big Models. - Sebastian Farquhar; Michael A. Osborne; Yarin Gal (CoRR, 2019)

Generalizing from a few environments in safety-critical reinforcement learning. - Zachary Kenton; Angelos Filos; Owain Evans; Yarin Gal (CoRR, 2019)

Prediction of GNSS Phase Scintillations: A Machine Learning Approach. - Kara Lamb; Garima Malhotra; Athanasios Vlontzos; Edward Wagstaff; Atilim Günes Baydin; Anahita Bhiwandiwalla; Yarin Gal; Alfredo Kalaitzis; Anthony Reina; Asti Bhatt (CoRR, 2019)

Flood Detection On Low Cost Orbital Hardware. - Gonzalo Mateo-Garcia; Silviu Oprea; Lewis Smith; Josh Veitch-Michaelis; Guy Schumann; Yarin Gal; Atilim Günes Baydin; Dietmar Backes (CoRR, 2019)

Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder. - Kara Lamb; Garima Malhotra; Athanasios Vlontzos; Edward Wagstaff; Atilim Günes Baydin; Anahita Bhiwandiwalla; Yarin Gal; Alfredo Kalaitzis; Anthony Reina; Asti Bhatt (CoRR, 2019)

Machine Learning for Generalizable Prediction of Flood Susceptibility. - Chelsea Sidrane; Dylan J. Fitzpatrick; Andrew Annex; Diane O'Donoghue; Yarin Gal; Piotr Bilinski (CoRR, 2019)

Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties. - Xavier Gitiaux; Shane A. Maloney; Anna Jungbluth; Carl Shneider; Paul J. Wright; Atilim Günes Baydin; Michel Deudon; Yarin Gal; Alfredo Kalaitzis; Andrés Muñoz-Jaramillo (CoRR, 2019)

Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics & Losses. - Anna Jungbluth; Xavier Gitiaux; Shane A. Maloney; Carl Shneider; Paul J. Wright; Alfredo Kalaitzis; Michel Deudon; Atilim Günes Baydin; Yarin Gal; Andrés Muñoz-Jaramillo (CoRR, 2019)

Using U-Nets to Create High-Fidelity Virtual Observations of the Solar Corona. - Valentina Salvatelli; Souvik Bose; Brad Neuberg; Luiz F. G. dos Santos; Mark C. M. Cheung; Miho Janvier; Atilim Gunes Baydin; Yarin Gal; Meng Jin (CoRR, 2019)

Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning. - Brad Neuberg; Souvik Bose; Valentina Salvatelli; Luiz F. G. dos Santos; Mark C. M. Cheung; Miho Janvier; Atilim Gunes Baydin; Yarin Gal; Meng Jin (CoRR, 2019)

Adversarial recovery of agent rewards from latent spaces of the limit order book. - Jacobo Roa-Vicens; Yuanbo Wang; Virgile Mison; Yarin Gal; Ricardo Silva 001 (CoRR, 2019)

A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks. - Angelos Filos; Sebastian Farquhar; Aidan N. Gomez; Tim G. J. Rudner; Zachary Kenton; Lewis Smith; Milad Alizadeh; Arnoud de Kroon; Yarin Gal (CoRR, 2019)

Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning. - Sebastian Farquhar; Michael A. Osborne; Yarin Gal (AISTATS, 2020)

BayesOpt Adversarial Attack. - Binxin Ru; Adam D. Cobb; Arno Blaas; Yarin Gal (ICLR, 2020)

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning. - Luisa M. Zintgraf; Kyriacos Shiarlis; Maximilian Igl; Sebastian Schulze; Yarin Gal; Katja Hofmann; Shimon Whiteson (ICLR, 2020)

Invariant Causal Prediction for Block MDPs. - Amy Zhang 001; Clare Lyle; Shagun Sodhani; Angelos Filos; Marta Kwiatkowska; Joelle Pineau; Yarin Gal; Doina Precup (ICML, 2020)

Uncertainty Estimation Using a Single Deep Deterministic Neural Network. - Joost van Amersfoort; Lewis Smith; Yee Whye Teh; Yarin Gal (ICML, 2020)

Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? - Angelos Filos; Panagiotis Tigas; Rowan McAllister; Nicholas Rhinehart; Sergey Levine; Yarin Gal (ICML, 2020)

Inter-domain Deep Gaussian Processes. - Tim G. J. Rudner; Dino Sejdinovic; Yarin Gal (ICML, 2020)

Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control. - Rhiannon Michelmore; Matthew Wicker; Luca Laurenti; Luca Cardelli; Yarin Gal; Marta Kwiatkowska (ICRA, 2020)

Model and Data Uncertainty for Satellite Time Series Forecasting with Deep Recurrent Models. - Marc Rußwurm; Mohsin Ali; Xiaoxiang Zhu 001; Yarin Gal; Marco Körner 001 (IGARSS, 2020)

Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations. - Sebastian Farquhar; Lewis Smith; Yarin Gal (NeurIPS, 2020)

Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models. - Andrew Jesson; Sören Mindermann; Uri Shalit; Yarin Gal (NeurIPS, 2020)

A Bayesian Perspective on Training Speed and Model Selection. - Clare Lyle; Lisa Schut; Binxin Ru; Yarin Gal; Mark van der Wilk (NeurIPS, 2020)

How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19? - Mrinank Sharma; Sören Mindermann; Jan Markus Brauner; Gavin Leech; Anna B. Stephenson; Tomas Gavenciak; Jan Kulveit; Yee Whye Teh; Leonid Chindelevitch; Yarin Gal (NeurIPS, 2020)

Try Depth Instead of Weight Correlations: Mean-field is a Less Restrictive Assumption for Deeper Networks. - Sebastian Farquhar; Lewis Smith; Yarin Gal (CoRR, 2020)

Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network. - Joost van Amersfoort; Lewis Smith; Yee Whye Teh; Yarin Gal (CoRR, 2020)

Baryons from Mesons: A Machine Learning Perspective. - Yarin Gal; Vishnu Jejjala; Damian Kaloni Mayorga Pena; Challenger Mishra (CoRR, 2020)

Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning. - Andreas Kirsch 002; Clare Lyle; Yarin Gal (CoRR, 2020)

Capsule Networks - A Probabilistic Perspective. - Lewis Smith; Lisa Schut; Yarin Gal; Mark van der Wilk (CoRR, 2020)

On the Benefits of Invariance in Neural Networks. - Clare Lyle; Mark van der Wilk; Marta Kwiatkowska; Yarin Gal; Benjamin Bloem-Reddy (CoRR, 2020)

Uncertainty Evaluation Metric for Brain Tumour Segmentation. - Raghav Mehta; Angelos Filos; Yarin Gal; Tal Arbel (CoRR, 2020)

Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search. - Binxin Ru; Clare Lyle; Lisa Schut; Mark van der Wilk; Yarin Gal (CoRR, 2020)

Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers. - Tim Z. Xiao; Aidan N. Gomez; Yarin Gal (CoRR, 2020)

Single Shot Structured Pruning Before Training. - Joost van Amersfoort; Milad Alizadeh; Sebastian Farquhar; Nicholas D. Lane; Yarin Gal (CoRR, 2020)

SliceOut: Training Transformers and CNNs faster while using less memory. - Pascal Notin; Aidan N. Gomez; Joanna Yoo; Yarin Gal (CoRR, 2020)

On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission. - Mrinank Sharma; Sören Mindermann; Jan Markus Brauner; Gavin Leech; Anna B. Stephenson; Tomas Gavenciak; Jan Kulveit; Yee Whye Teh; Leonid Chindelevitch; Yarin Gal (CoRR, 2020)

Interlocking Backpropagation: Improving depthwise model-parallelism. - Aidan N. Gomez; Oscar Key; Stephen Gou; Nick Frosst; Jeff Dean; Yarin Gal (CoRR, 2020)

Physics-informed GANs for Coastal Flood Visualization. - Björn Lütjens; Brandon Leshchinskiy; Christian Requena-Mesa; Farrukh Chishtie; Natalia Díaz Rodríguez; Océane Boulais; Aaron Piña; Dava J. Newman; Alexander Lavin; Yarin Gal; Chedy Raïssi (CoRR, 2020)

Semi-supervised Learning of Galaxy Morphology using Equivariant Transformer Variational Autoencoders. - Mizu Nishikawa-Toomey; Lewis Smith; Yarin Gal (CoRR, 2020)

On Batch Normalisation for Approximate Bayesian Inference. - Jishnu Mukhoti; Puneet K. Dokania; Philip H. S. Torr; Yarin Gal (CoRR, 2020)

Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning. - Luiz F. G. dos Santos; Souvik Bose; Valentina Salvatelli; Brad Neuberg; Mark C. M. Cheung; Miho Janvier; Meng Jin; Yarin Gal; Paul Boerner; Atilim Günes Baydin (CoRR, 2020)

Conditional BRUNO: A neural process for exchangeable labelled data. - Iryna Korshunova; Yarin Gal; Arthur Gretton; Joni Dambre (Neurocomputing, 2020)

Converter software to calibrate and super-resolve solar magnetograms. - Andrés Muñoz-Jaramillo; Xavier Gitiaux; Anna Jungbluth; Shane A. Maloney; Carl Shneider; Paul J. Wright; Michel Deudon; Alfredo Kalaitzis; Atilim Günes Baydin; Yarin Gal (Zenodo, 2020)

Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties. - Lisa Schut; Oscar Key; Rory McGrath; Luca Costabello; Bogdan Sacaleanu; Medb Corcoran; Yarin Gal (AISTATS, 2021)

Learning Invariant Representations for Reinforcement Learning without Reconstruction. - Amy Zhang 001; Rowan Thomas McAllister; Roberto Calandra; Yarin Gal; Sergey Levine (ICLR, 2021)

On Statistical Bias In Active Learning: How and When to Fix It. - Sebastian Farquhar; Yarin Gal; Tom Rainforth (ICLR, 2021)

PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning. - Angelos Filos; Clare Lyle; Yarin Gal; Sergey Levine; Natasha Jaques; Gregory Farquhar (ICML, 2021)

Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. - Andrew Jesson; Sören Mindermann; Yarin Gal; Uri Shalit (ICML, 2021)

Active Testing: Sample-Efficient Model Evaluation. - Jannik Kossen; Sebastian Farquhar; Yarin Gal; Tom Rainforth (ICML, 2021)

On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. - Tim G. J. Rudner; Oscar Key; Yarin Gal; Tom Rainforth (ICML, 2021)

Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. - Neil Band; Tim G. J. Rudner; Qixuan Feng; Angelos Filos; Zachary Nado; Mike Dusenberry; Ghassen Jerfel; Dustin Tran; Yarin Gal (NeurIPS Datasets and Benchmarks, 2021)

Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. - Andrew Jesson; Panagiotis Tigas; Joost van Amersfoort; Andreas Kirsch 002; Uri Shalit; Yarin Gal (NeurIPS, 2021)

Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning. - Jannik Kossen; Neil Band; Clare Lyle; Aidan N. Gomez; Thomas Rainforth; Yarin Gal (NeurIPS, 2021)

Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks. - Andrey Malinin; Neil Band; Yarin Gal; Mark J. F. Gales; Alexander Ganshin; German Chesnokov; Alexey Noskov; Andrey Ploskonosov; Liudmila Prokhorenkova; Ivan Provilkov; Vatsal Raina; Vyas Raina; et al. (NeurIPS Datasets and Benchmarks, 2021)

Domain Invariant Representation Learning with Domain Density Transformations. - A. Tuan Nguyen; Toan Tran 003; Yarin Gal; Atilim Gunes Baydin (NeurIPS, 2021)

Improving black-box optimization in VAE latent space using decoder uncertainty. - Pascal Notin; José Miguel Hernández-Lobato; Yarin Gal (NeurIPS, 2021)

Speedy Performance Estimation for Neural Architecture Search. - Robin Ru; Clare Lyle; Lisa Schut; Miroslav Fil; Mark van der Wilk; Yarin Gal (NeurIPS, 2021)

On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations. - Tim G. J. Rudner; Cong Lu; Michael A. Osborne; Yarin Gal; Yee Whye Teh (NeurIPS, 2021)

Outcome-Driven Reinforcement Learning via Variational Inference. - Tim G. J. Rudner; Vitchyr Pong; Rowan McAllister; Yarin Gal; Sergey Levine (NeurIPS, 2021)

Evaluating Approximate Inference in Bayesian Deep Learning. - Andrew Gordon Wilson; Pavel Izmailov; Matthew D. Hoffman; Yarin Gal; Yingzhen Li; Melanie F. Pradier; Sharad Vikram; Andrew Y. K. Foong; Sanae Lotfi; Sebastian Farquhar (NeurIPS (Competition and Demos), 2021)

Technology Readiness Levels for Machine Learning Systems. - Alexander Lavin; Ciarán M. Gilligan-Lee; Alessya Visnjic; Siddha Ganju; Dava J. Newman; Sujoy Ganguly; Danny Lange; Atilim Günes Baydin; Amit Sharma 007; Adam Gibson; Yarin Gal; Eric P. Xing; et al. (CoRR, 2021)

Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition. - Panagiotis Tigas; Téo Bloch; Vishal Upendran; Banafsheh Ferdoushi; Mark C. M. Cheung; Siddha Ganju; Ryan M. McGranaghan; Yarin Gal; Asti Bhatt (CoRR, 2021)

Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression. - Joost van Amersfoort; Lewis Smith; Andrew Jesson; Oscar Key; Yarin Gal (CoRR, 2021)

Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty. - Jishnu Mukhoti; Andreas Kirsch 002; Joost van Amersfoort; Philip H. S. Torr; Yarin Gal (CoRR, 2021)

Robustness to Pruning Predicts Generalization in Deep Neural Networks. - Lorenz Kuhn; Clare Lyle; Aidan N. Gomez; Jonas Rothfuss; Yarin Gal (CoRR, 2021)

Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization. - Björn Lütjens; Brandon Leshchinskiy; Christian Requena-Mesa; Farrukh Chishtie; Natalia Díaz Rodríguez; Océane Boulais; Aruna Sankaranarayanan; Aaron Piña; Yarin Gal; Chedy Raïssi; Alexander Lavin; Dava J. Newman (CoRR, 2021)

Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective. - Lewis Smith; Joost van Amersfoort; Haiwen Huang; Stephen J. Roberts; Yarin Gal (CoRR, 2021)

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. - Zachary Nado; Neil Band; Mark Collier; Josip Djolonga; Michael W. Dusenberry; Sebastian Farquhar; Angelos Filos; Marton Havasi; Rodolphe Jenatton; Ghassen Jerfel; Jeremiah Z. Liu; Zelda Mariet; et al. (CoRR, 2021)

Active Learning under Pool Set Distribution Shift and Noisy Data. - Andreas Kirsch 002; Tom Rainforth; Yarin Gal (CoRR, 2021)

A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions. - Andreas Kirsch 002; Sebastian Farquhar; Yarin Gal (CoRR, 2021)

A Practical & Unified Notation for Information-Theoretic Quantities in ML. - Andreas Kirsch 002; Yarin Gal (CoRR, 2021)

Prioritized training on points that are learnable, worth learning, and not yet learned. - Sören Mindermann; Muhammed Razzak; Winnie Xu; Andreas Kirsch 002; Mrinank Sharma; Adrien Morisot; Aidan N. Gomez; Sebastian Farquhar; Jan Markus Brauner; Yarin Gal (CoRR, 2021)

Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks. - Andrey Malinin; Neil Band; Alexander Ganshin; German Chesnokov; Yarin Gal; Mark J. F. Gales; Alexey Noskov; Andrey Ploskonosov; Liudmila Prokhorenkova; Ivan Provilkov; Vatsal Raina; Vyas Raina; et al. (CoRR, 2021)

Quantifying Uncertainty for Machine Learning Based Diagnostic. - Owen Convery; Lewis Smith; Yarin Gal; Adi Hanuka (CoRR, 2021)

Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific. - Andrew Jesson; Peter Manshausen; Alyson Douglas; Duncan Watson-Parris; Yarin Gal; Philip Stier (CoRR, 2021)

Deep Deterministic Uncertainty for Semantic Segmentation. - Jishnu Mukhoti; Joost van Amersfoort; Philip H. S. Torr; Yarin Gal (CoRR, 2021)

Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation. - Muhammed Razzak; Gonzalo Mateo-Garcia; Luis Gómez-Chova; Yarin Gal; Freddie Kalaitzis (CoRR, 2021)

Contrastive Representation Learning with Trainable Augmentation Channel. - Masanori Koyama; Kentaro Minami; Takeru Miyato; Yarin Gal (CoRR, 2021)

DeDUCE: Generating Counterfactual Explanations Efficiently. - Benedikt Höltgen; Lisa Schut; Jan Markus Brauner; Yarin Gal (CoRR, 2021)

Decomposing Representations for Deterministic Uncertainty Estimation. - Haiwen Huang; Joost van Amersfoort; Yarin Gal (CoRR, 2021)

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results. - Raghav Mehta; Angelos Filos; Ujjwal Baid; Chiharu Sako; Richard McKinley; Michael Rebsamen; Katrin Dätwyler; Raphael Meier; Piotr Radojewski; Gowtham Krishnan Murugesan; Sahil S. Nalawade; Chandan Ganesh; et al. (CoRR, 2021)

DARTS without a Validation Set: Optimizing the Marginal Likelihood. - Miroslav Fil; Binxin Ru; Clare Lyle; Yarin Gal (CoRR, 2021)

VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning. - Luisa M. Zintgraf; Sebastian Schulze; Cong Lu; Leo Feng; Maximilian Igl; Kyriacos Shiarlis; Yarin Gal; Katja Hofmann; Shimon Whiteson (J. Mach. Learn. Res., 2021)

Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients. - Milad Alizadeh; Shyam A. Tailor; Luisa M. Zintgraf; Joost van Amersfoort; Sebastian Farquhar; Nicholas Donald Lane; Yarin Gal (ICLR, 2022)

GeneDisco: A Benchmark for Experimental Design in Drug Discovery. - Arash Mehrjou; Ashkan Soleymani; Andrew Jesson; Pascal Notin; Yarin Gal; Stefan Bauer; Patrick Schwab (ICLR, 2022)

KL Guided Domain Adaptation. - A. Tuan Nguyen; Toan Tran 003; Yarin Gal; Philip H. S. Torr; Atilim Gunes Baydin (ICLR, 2022)

Learning Dynamics and Generalization in Deep Reinforcement Learning. - Clare Lyle; Mark Rowland 001; Will Dabney; Marta Kwiatkowska; Yarin Gal (ICML, 2022)

Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt. - Sören Mindermann; Jan Markus Brauner; Muhammed Razzak; Mrinank Sharma; Andreas Kirsch 002; Winnie Xu; Benedikt Höltgen; Aidan N. Gomez; Adrien Morisot; Sebastian Farquhar; Yarin Gal (ICML, 2022)

Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. - Pascal Notin; Mafalda Dias; Jonathan Frazer; Javier Marchena-Hurtado; Aidan N. Gomez; Debora S. Marks; Yarin Gal (ICML, 2022)

Continual Learning via Sequential Function-Space Variational Inference. - Tim G. J. Rudner; Freddie Bickford Smith; Qixuan Feng; Yee Whye Teh; Yarin Gal (ICML, 2022)

Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions. - Andrew Jesson; Alyson Douglas; Peter Manshausen; Maëlys Solal; Nicolai Meinshausen; Philip Stier; Yarin Gal; Uri Shalit (NeurIPS, 2022)

Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation. - Jannik Kossen; Sebastian Farquhar; Yarin Gal; Thomas Rainforth (NeurIPS, 2022)

Tractable Function-Space Variational Inference in Bayesian Neural Networks. - Tim G. J. Rudner; Zonghao Chen; Yee Whye Teh; Yarin Gal (NeurIPS, 2022)

Interventions, Where and How? Experimental Design for Causal Models at Scale. - Panagiotis Tigas; Yashas Annadani; Andrew Jesson; Bernhard Schölkopf; Yarin Gal; Stefan Bauer (NeurIPS, 2022)

Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions. - Andrew Jesson; Alyson Douglas; Peter Manshausen; Nicolai Meinshausen; Philip Stier; Yarin Gal; Uri Shalit (CoRR, 2022)

Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling. - Andreas Kirsch 002; Jannik Kossen; Yarin Gal (CoRR, 2022)

Global geomagnetic perturbation forecasting using Deep Learning. - Vishal Upendran; Panagiotis Tigas; Banafsheh Ferdousi; Téo Bloch; Mark C. M. Cheung; Siddha Ganju; Asti Bhatt; Ryan M. McGranaghan; Yarin Gal (CoRR, 2022)

Learning Dynamics and Generalization in Reinforcement Learning. - Clare Lyle; Mark Rowland 001; Will Dabney; Marta Kwiatkowska; Yarin Gal (CoRR, 2022)

Plex: Towards Reliability using Pretrained Large Model Extensions. - Dustin Tran; Jeremiah Z. Liu; Michael W. Dusenberry; Du Phan; Mark Collier; Jie Ren 006; Kehang Han; Zi Wang; Zelda Mariet; Huiyi Hu; Neil Band; Tim G. J. Rudner; et al. (CoRR, 2022)

Unifying Approaches in Data Subset Selection via Fisher Information and Information-Theoretic Quantities. - Andreas Kirsch 002; Yarin Gal (CoRR, 2022)

Exploring the Limits of Synthetic Creation of Solar EUV Images via Image-to-Image Translation. - Valentina Salvatelli; Luiz F. G. dos Santos; Souvik Bose; Brad Neuberg; Mark C. M. Cheung; Miho Janvier; Meng Jin; Yarin Gal; Atilim Gunes Baydin (CoRR, 2022)

Exploring Low Rank Training of Deep Neural Networks. - Siddhartha Rao Kamalakara; Acyr Locatelli; Bharat Venkitesh; Jimmy Ba; Yarin Gal; Aidan N. Gomez (CoRR, 2022)

Discovering Long-period Exoplanets using Deep Learning with Citizen Science Labels. - Shreshth A. Malik; Nora L. Eisner; Chris J. Lintott; Yarin Gal (CoRR, 2022)

CLAM: Selective Clarification for Ambiguous Questions with Large Language Models. - Lorenz Kuhn; Yarin Gal; Sebastian Farquhar (CoRR, 2022)

Interlocking Backpropagation: Improving depthwise model-parallelism. - Aidan N. Gomez; Oscar Key; Kuba Perlin; Stephen Gou; Nick Frosst; Jeff Dean; Yarin Gal (J. Mach. Learn. Res., 2022)

Mixtures of large-scale dynamic functional brain network modes. - Chetan Gohil; Evan Roberts; Ryan C. Timms; Alex Skates; Cameron Higgins; Andrew Quinn 003; Usama Pervaiz; Joost van Amersfoort; Pascal Notin; Yarin Gal; Stanislaw Adaszewski; Mark W. Woolrich (NeuroImage, 2022)

Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference. - Raghav Mehta; Thomas Christinck; Tanya Nair; Aurélie Bussy; Swapna Premasiri; Manuela Costantino; M. Mallar Chakravarthy; Douglas L. Arnold; Yarin Gal; Tal Arbel (IEEE Trans. Medical Imaging, 2022)

A Note on "Assessing Generalization of SGD via Disagreement". - Andreas Kirsch 002; Yarin Gal (Trans. Mach. Learn. Res., 2022)

Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities. - Andreas Kirsch 002; Yarin Gal (Trans. Mach. Learn. Res., 2022)

Revisiting Automated Prompting: Are We Actually Doing Better? - Yulin Zhou; Yiren Zhao; Ilia Shumailov; Robert D. Mullins; Yarin Gal (ACL (2), 2023)

Prediction-Oriented Bayesian Active Learning. - Freddie Bickford Smith; Andreas Kirsch 002; Sebastian Farquhar; Yarin Gal; Adam Foster 001; Tom Rainforth (AISTATS, 2023)

Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? - Gunshi Gupta; Tim G. J. Rudner; Rowan Thomas McAllister; Adrien Gaidon; Yarin Gal (CLeaR, 2023)

Deep Deterministic Uncertainty: A New Simple Baseline. - Jishnu Mukhoti; Andreas Kirsch 002; Joost van Amersfoort; Philip H. S. Torr; Yarin Gal (CVPR, 2023)

Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation. - Lorenz Kuhn; Yarin Gal; Sebastian Farquhar (ICLR, 2023)

DiscoBAX: Discovery of optimal intervention sets in genomic experiment design. - Clare Lyle; Arash Mehrjou; Pascal Notin; Andrew Jesson; Stefan Bauer; Yarin Gal; Patrick Schwab (ICML, 2023)

Differentiable Multi-Target Causal Bayesian Experimental Design. - Panagiotis Tigas; Yashas Annadani; Desi R. Ivanova; Andrew Jesson; Yarin Gal; Adam Foster 001; Stefan Bauer (ICML, 2023)

Precipitation-Triggered Landslide Prediction in Nepal Using Machine Learning and Deep Learning. - Kelsey Doerksen; Yarin Gal; Freddie Kalaitzis; Cristian Rossi; David Petit; Sihan Li; Simon J. Dadson (IGARSS, 2023)

ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design. - Pascal Notin; Aaron Kollasch; Daniel Ritter 006; Lood van Niekerk; Steffanie Paul; Han Spinner; Nathan J. Rollins; Ada Shaw; Rose Orenbuch; Ruben Weitzman; Jonathan Frazer; Mafalda Dias; et al. (NeurIPS, 2023)

ProteinNPT: Improving protein property prediction and design with non-parametric transformers. - Pascal Notin; Ruben Weitzman; Debora S. Marks; Yarin Gal (NeurIPS, 2023)

Using uncertainty-aware machine learning models to study aerosol-cloud interactions. - Maëlys Solal; Andrew Jesson; Yarin Gal; Alyson Douglas (CoRR, 2023)

The Curse of Recursion: Training on Generated Data Makes Models Forget. - Ilia Shumailov; Zakhar Shumaylov; Yiren Zhao; Yarin Gal; Nicolas Papernot; Ross J. Anderson (CoRR, 2023)

ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages. - Andrew Jesson; Chris Lu 001; Gunshi Gupta; Angelos Filos; Jakob Nicolaus Foerster; Yarin Gal (CoRR, 2023)

BatchGFN: Generative Flow Networks for Batch Active Learning. - Shreshth A. Malik; Salem Lahlou; Andrew Jesson; Moksh Jain; Nikolay Malkin; Tristan Deleu; Yoshua Bengio; Yarin Gal (CoRR, 2023)

LLM Censorship: A Machine Learning Challenge or a Computer Security Problem? - David Glukhov; Ilia Shumailov; Yarin Gal; Nicolas Papernot; Vardan Papyan (CoRR, 2023)

In-Context Learning in Large Language Models Learns Label Relationships but Is Not Conventional Learning. - Jannik Kossen; Tom Rainforth; Yarin Gal (CoRR, 2023)

Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirements. - Peter A. Zachares; Vahan Hovhannisyan; Alan Mosca; Yarin Gal (CoRR, 2023)

Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning. - Andreas Kirsch 002; Sebastian Farquhar; Parmida Atighehchian; Andrew Jesson; Frédéric Branchaud-Charron; Yarin Gal (Trans. Mach. Learn. Res., 2023)

In-Context Learning Learns Label Relationships but Is Not Conventional Learning. - Jannik Kossen; Yarin Gal; Tom Rainforth (ICLR, 2024)

How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions. - Lorenzo Pacchiardi; Alex James Chan; Sören Mindermann; Ilan Moscovitz; Alexa Y. Pan; Yarin Gal; Owain Evans; Jan Markus Brauner (ICLR, 2024)

Position: Fundamental Limitations of LLM Censorship Necessitate New Approaches. - David Glukhov; Ilia Shumailov; Yarin Gal; Nicolas Papernot; Vardan Papyan (ICML, 2024)

ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages. - Andrew Jesson; Chris Lu 001; Gunshi Gupta; Nicolas Beltran-Velez; Angelos Filos; Jakob Nicolaus Foerster; Yarin Gal (ICML, 2024)

Challenges and Considerations in the Evaluation of Bayesian Causal Discovery. - Amir Mohammad Karimi-Mamaghan; Panagiotis Tigas; Karl Henrik Johansson; Yarin Gal; Yashas Annadani; Stefan Bauer (ICML, 2024)

TextCAVs: Debugging Vision Models Using Text. - Angus Nicolson; Yarin Gal; J. Alison Noble (ISIC/iMIMIC/EARTH/DeCaF@MICCAI, 2024)

Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control. - Gunshi Gupta; Karmesh Yadav; Yarin Gal; Dhruv Batra; Zsolt Kira; Cong Lu; Tim G. J. Rudner (NeurIPS, 2024)

Estimating the Hallucination Rate of Generative AI. - Andrew Jesson; Nicolas Beltran-Velez; Quentin Chu; Sweta Karlekar; Jannik Kossen; Yarin Gal; John P. Cunningham; David M. Blei (NeurIPS, 2024)

Deep Bayesian Active Learning for Preference Modeling in Large Language Models. - Luckeciano Carvalho Melo; Panagiotis Tigas; Alessandro Abate; Yarin Gal (NeurIPS, 2024)

Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities. - Alexander Nikitin; Jannik Kossen; Yarin Gal; Pekka Marttinen (NeurIPS, 2024)

Bayesian Preference Elicitation with Language Models. - Kunal Handa; Yarin Gal; Ellie Pavlick; Noah D. Goodman; Jacob Andreas; Alex Tamkin; Belinda Z. Li (CoRR, 2024)

Explaining Explainability: Understanding Concept Activation Vectors. - Angus Nicolson; Lisa Schut; J. Alison Noble; Yarin Gal (CoRR, 2024)

The Benefits and Risks of Transductive Approaches for AI Fairness. - Muhammed Razzak; Andreas Kirsch 002; Yarin Gal (CoRR, 2024)

Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs. - Jannik Kossen; Jiatong Han; Muhammed Razzak; Lisa Schut; Shreshth A. Malik; Yarin Gal (CoRR, 2024)

Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks. - Yoav Gelberg; Tycho F. A. van der Ouderaa; Mark van der Wilk; Yarin Gal (CoRR, 2024)

Temporal-Difference Variational Continual Learning. - Luckeciano C. Melo; Alessandro Abate; Yarin Gal (CoRR, 2024)

AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents. - Maksym Andriushchenko; Alexandra Souly; Mateusz Dziemian; Derek Duenas; Maxwell Lin; Justin Wang; Dan Hendrycks; Andy Zou; Zico Kolter; Matt Fredrikson; Eric Winsor; Jerome Wynne; et al. (CoRR, 2024)

Fine-Tuning Large Language Models to Appropriately Abstain with Semantic Entropy. - Benedict Aaron Tjandra; Muhammed Razzak; Jannik Kossen; Kunal Handa; Yarin Gal (CoRR, 2024)

Detecting LLM Hallucination Through Layer-wise Information Deficiency: Analysis of Unanswerable Questions and Ambiguous Prompts. - Hazel Kim; Adel Bibi; Philip Torr 001; Yarin Gal (CoRR, 2024)

Generating Physically-Consistent Satellite Imagery for Climate Visualizations. - Björn Lütjens; Brandon Leshchinskiy; Océane Boulais; Farrukh Chishtie; Natalia Díaz Rodríguez; Margaux Masson-Forsythe; Ana Mata-Payerro; Christian Requena-Mesa; Aruna Sankaranarayanan; Aaron Piña; Yarin Gal; Chedy Raïssi; et al. (IEEE Trans. Geosci. Remote. Sens., 2024)

Fine-tuning can cripple your foundation model; preserving features may be the solution. - Jishnu Mukhoti; Yarin Gal; Philip Torr 001; Puneet K. Dokania (Trans. Mach. Learn. Res., 2024)

Detecting hallucinations in large language models using semantic entropy. - Sebastian Farquhar; Jannik Kossen; Lorenz Kuhn; Yarin Gal (Nat., 2024)

AI models collapse when trained on recursively generated data. - Ilia Shumailov; Zakhar Shumaylov; Yiren Zhao; Nicolas Papernot; Ross J. Anderson; Yarin Gal (Nat., 2024)

AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents. - Maksym Andriushchenko; Alexandra Souly; Mateusz Dziemian; Derek Duenas; Maxwell Lin; Justin Wang; Dan Hendrycks; Andy Zou; J. Zico Kolter; Matt Fredrikson; Yarin Gal; Xander Davies (ICLR, 2025)

Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities. - Zora Che; Stephen Casper; Robert Kirk; Anirudh Satheesh; Stewart Slocum; Lev E. McKinney; Rohit Gandikota; Aidan Ewart; Domenic Rosati; Zichu Wu; Zikui Cai; Bilal Chughtai; et al. (Trans. Mach. Learn. Res., 2025)

Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors. - Angus Nicolson; Lisa Schut; J. Alison Noble; Yarin Gal (Trans. Mach. Learn. Res., 2025)

Open Problems in Machine Unlearning for AI Safety. - Fazl Barez; Tingchen Fu; Ameya Prabhu; Stephen Casper; Amartya Sanyal; Adel Bibi; Aidan O'Gara; Robert Kirk; Ben Bucknall; Timothy Fist; Luke Ong; Philip Torr 001; et al. (CoRR, 2025)

Uncertainty-Aware Step-wise Verification with Generative Reward Models. - Zihuiwen Ye; Luckeciano Carvalho Melo; Younesse Kaddar; Phil Blunsom; Sam Staton; Yarin Gal (CoRR, 2025)

Fundamental Limitations in Defending LLM Finetuning APIs. - Xander Davies; Eric Winsor; Tomek Korbak; Alexandra Souly; Robert Kirk; Christian Schröder de Witt; Yarin Gal (CoRR, 2025)

Do Multilingual LLMs Think In English? - Lisa Schut; Yarin Gal; Sebastian Farquhar (CoRR, 2025)

Reducing Large Language Model Safety Risks in Women's Health using Semantic Entropy. - Jahan C. Penny-Dimri; Magdalena Bachmann; William R. Cooke; Sam Mathewlynn; Samuel Dockree; John Tolladay; Jannik Kossen; Lin Li; Yarin Gal; Gabriel Davis Jones (CoRR, 2025)

Attacking Multimodal OS Agents with Malicious Image Patches. - Lukas Aichberger; Alasdair Paren; Yarin Gal; Philip Torr 001; Adel Bibi (CoRR, 2025)

Existing Large Language Model Unlearning Evaluations Are Inconclusive. - Zhili Feng; Yixuan Even Xu; Alexander Robey; Robert Kirk; Xander Davies; Yarin Gal; Avi Schwarzschild; J. Zico Kolter (CoRR, 2025)

Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction. - Ruben Weitzman; Peter Mørch Groth; Lood van Niekerk; Aoi Otani; Yarin Gal; Debora S. Marks; Pascal Notin (CoRR, 2025)

FindingDory: A Benchmark to Evaluate Memory in Embodied Agents. - Karmesh Yadav; Yusuf Ali; Gunshi Gupta; Yarin Gal; Zsolt Kira (CoRR, 2025)

Language Models Change Facts Based on the Way You Talk. - Matthew Kearney; Reuben Binns; Yarin Gal (CoRR, 2025)

Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition. - Andy Zou; Maxwell Lin; Eliot Krzysztof Jones; Micha Nowak; Mateusz Dziemian; Nick Winter; Alexander Grattan; Valent Nathanael; Ayla Croft; Xander Davies; Jai Patel; Robert Kirk; et al. (CoRR, 2025)

Weighted Conditional Flow Matching. - Sergio Calvo-Ordoñez; Matthieu Meunier; Alvaro Cartea; Christoph Reisinger; Yarin Gal; José Miguel Hernández-Lobato (CoRR, 2025)

Uncertainty Quantification for Surface Ozone Emulators using Deep Learning. - Kelsey Doerksen; Yuliya Marchetti; Steven Lu; Kevin Bowman; James Montgomery; Kazuyuki Miyazaki; Yarin Gal; Freddie Kalaitzis (CoRR, 2025)

Leveraging Deep Learning for Physical Model Bias of Global Air Quality Estimates. - Kelsey Doerksen; Yuliya Marchetti; Kevin Bowman; Steven Lu; James Montgomery; Yarin Gal; Freddie Kalaitzis; Kazuyuki Miyazaki (CoRR, 2025)

Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs. - Kyle O'Brien; Stephen Casper; Quentin Anthony; Tomek Korbak; Robert Kirk; Xander Davies; Ishan Mishra; Geoffrey Irving; Yarin Gal; Stella Biderman (CoRR, 2025)

Scaling Up Active Testing to Large Language Models. - Gabrielle Berrada; Jannik Kossen; Muhammed Razzak; Freddie Bickford Smith; Yarin Gal; Tom Rainforth (CoRR, 2025)