| 2025 | EMNLP | Simple Factuality Probes Detect Hallucinations in Long-Form Natural Language Generation. | Jiatong Han, Neil Band, Muhammed Razzak, Jannik Kossen, Tim G. J. Rudner, Yarin Gal |
| 2025 | EMNLP | Detecting LLM Hallucination Through Layer-wise Information Deficiency: Analysis of Ambiguous Prompts and Unanswerable Questions. | Hazel Kim, Tom A. Lamb, Adel Bibi, Philip Torr, Yarin Gal |
| 2025 | ICLR | 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 |
| 2025 | ICML | Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction. | Ruben Weitzman, Peter Mrch Groth, Lood van Niekerk, Aoi Otani, Yarin Gal, Debora Susan Marks, Pascal Notin |
| 2024 | ICLR | In-Context Learning Learns Label Relationships but Is Not Conventional Learning. | Jannik Kossen, Yarin Gal, Tom Rainforth |
| 2024 | ICLR | How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions. | Lorenzo Pacchiardi, Alex James Chan, Sren Mindermann, Ilan Moscovitz, Alexa Y. Pan, Yarin Gal, Owain Evans, Jan Markus Brauner |
| 2024 | ICML | Position: Fundamental Limitations of LLM Censorship Necessitate New Approaches. | David Glukhov, Ilia Shumailov, Yarin Gal, Nicolas Papernot, Vardan Papyan |
| 2024 | ICML | ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages. | Andrew Jesson, Chris Lu, Gunshi Gupta, Nicolas Beltran-Velez, Angelos Filos, Jakob Nicolaus Foerster, Yarin Gal |
| 2024 | ICML | 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 |
| 2024 | MICCAI | TextCAVs: Debugging Vision Models Using Text. | Angus Nicolson, Yarin Gal, J. Alison Noble |
| 2023 | ACL | Revisiting Automated Prompting: Are We Actually Doing Better? | Yulin Zhou, Yiren Zhao, Ilia Shumailov, Robert Mullins, Yarin Gal |
| 2023 | AISTATS | Prediction-Oriented Bayesian Active Learning. | Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth |
| 2023 | CVPR | Deep Deterministic Uncertainty: A New Simple Baseline. | Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal |
| 2023 | ICLR | Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation. | Lorenz Kuhn, Yarin Gal, Sebastian Farquhar |
| 2023 | ICML | DiscoBAX: Discovery of optimal intervention sets in genomic experiment design. | Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab |
| 2023 | ICML | Differentiable Multi-Target Causal Bayesian Experimental Design. | Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer |
| 2023 | IGARSS | 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 |
| 2022 | ICLR | 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 |
| 2022 | ICLR | GeneDisco: A Benchmark for Experimental Design in Drug Discovery. | Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab |
| 2022 | ICLR | KL Guided Domain Adaptation. | A. Tuan Nguyen, Toan Tran, Yarin Gal, Philip H. S. Torr, Atilim Gunes Baydin |
| 2022 | ICML | Learning Dynamics and Generalization in Deep Reinforcement Learning. | Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal |
| 2022 | ICML | Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt. | Sren Mindermann, Jan Markus Brauner, Muhammed Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Hltgen, Aidan N. Gomez, Adrien Morisot, Sebastian Farquhar, Yarin Gal |
| 2022 | ICML | 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 |
| 2022 | ICML | Continual Learning via Sequential Function-Space Variational Inference. | Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal |
| 2021 | AISTATS | 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 |
| 2021 | ICLR | Learning Invariant Representations for Reinforcement Learning without Reconstruction. | Amy Zhang, Rowan Thomas McAllister, Roberto Calandra, Yarin Gal, Sergey Levine |
| 2021 | ICLR | On Statistical Bias In Active Learning: How and When to Fix It. | Sebastian Farquhar, Yarin Gal, Tom Rainforth |
| 2021 | ICML | 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 |
| 2021 | ICML | Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. | Andrew Jesson, Sren Mindermann, Yarin Gal, Uri Shalit |
| 2021 | ICML | Active Testing: Sample-Efficient Model Evaluation. | Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth |
| 2021 | ICML | On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. | Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth |
| 2020 | AISTATS | Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning. | Sebastian Farquhar, Michael A. Osborne, Yarin Gal |
| 2020 | ICLR | BayesOpt Adversarial Attack. | Binxin Ru, Adam D. Cobb, Arno Blaas, Yarin Gal |
| 2020 | ICLR | 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 |
| 2020 | ICML | Invariant Causal Prediction for Block MDPs. | Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup |
| 2020 | ICML | Uncertainty Estimation Using a Single Deep Deterministic Neural Network. | Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal |
| 2020 | ICML | Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? | Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal |
| 2020 | ICML | Inter-domain Deep Gaussian Processes. | Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal |
| 2020 | IGARSS | Model and Data Uncertainty for Satellite Time Series Forecasting with Deep Recurrent Models. | Marc Ruwurm, Mohsin Ali, Xiaoxiang Zhu, Yarin Gal, Marco Krner |
| 2020 | ICRA | Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control. | Rhiannon Michelmore, Matthew Wicker, Luca Laurenti, Luca Cardelli, Yarin Gal, Marta Kwiatkowska |
| 2019 | ESANN | Conditional BRUNO: a neural process for exchangeable labelled data. | Iryna Korshunova, Yarin Gal, Arthur Gretton, Joni Dambre |
| 2019 | ICLR | An Empirical study of Binary Neural Networks' Optimisation. | Milad Alizadeh, Javier Fernndez-Marqus, Nicholas D. Lane, Yarin Gal |
| 2018 | CVPR | Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics. | Alex Kendall, Yarin Gal, Roberto Cipolla |
| 2018 | ICML | Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. | Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava |
| 2018 | UAI | Understanding Measures of Uncertainty for Adversarial Example Detection. | Lewis Smith, Yarin Gal |
| 2017 | ICML | Deep Bayesian Active Learning with Image Data. | Yarin Gal, Riashat Islam, Zoubin Ghahramani |
| 2017 | ICML | Dropout Inference in Bayesian Neural Networks with Alpha-divergences. | Yingzhen Li, Yarin Gal |
| 2017 | IJCAI | Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning. | Rowan McAllister, Yarin Gal, Alex Kendall, Mark van der Wilk, Amar Shah, Roberto Cipolla, Adrian Weller |
| 2016 | ICML | Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. | Yarin Gal, Zoubin Ghahramani |
| 2015 | ICML | Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data. | Yarin Gal, Yutian Chen, Zoubin Ghahramani |
| 2015 | ICML | Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs. | Yarin Gal, Richard E. Turner |
| 2014 | ICML | Pitfalls in the use of Parallel Inference for the Dirichlet Process. | Yarin Gal, Zoubin Ghahramani |
| 2013 | NAACL | A Systematic Bayesian Treatment of the IBM Alignment Models. | Yarin Gal, Phil Blunsom |
| 2010 | ICMLA | Overcoming Alpha-Beta Limitations Using Evolved Artificial Neural Networks. | Yarin Gal, Mireille Avigal |