Yarin Gal

ORCID

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 219

Top Venues

Publications

Jishnu Mukhoti, Yarin Gal, Philip Torr, Puneet K. Dokania
Fine-tuning can cripple your foundation model; preserving features may be the solution. Trans. Mach. Learn. Res. 2024 (2024)
Angus Nicolson, Lisa Schut, J. Alison Noble, Yarin Gal
Explaining Explainability: Understanding Concept Activation Vectors. CoRR (2024)
Lorenzo Pacchiardi, Alex James Chan, Sören Mindermann, Ilan Moscovitz, Alexa Y. Pan, Yarin Gal, Owain Evans, Jan Markus Brauner
How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions. ICLR (2024)
Amir Mohammad Karimi-Mamaghan, Panagiotis Tigas, Karl Henrik Johansson, Yarin Gal, Yashas Annadani, Stefan Bauer
Challenges and Considerations in the Evaluation of Bayesian Causal Discovery. CoRR (2024)
Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. Cunningham, David M. Blei
Estimating the Hallucination Rate of Generative AI. CoRR (2024)
Luckeciano C. Melo, Panagiotis Tigas, Alessandro Abate, Yarin Gal
Deep Bayesian Active Learning for Preference Modeling in Large Language Models. CoRR (2024)
Alexander Nikitin, Jannik Kossen, Yarin Gal, Pekka Marttinen
Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities. CoRR (2024)
Muhammed Razzak, Andreas Kirsch, Yarin Gal
The Benefits and Risks of Transductive Approaches for AI Fairness. CoRR (2024)
Kunal Handa, Yarin Gal, Ellie Pavlick, Noah D. Goodman, Jacob Andreas, Alex Tamkin, Belinda Z. Li
Bayesian Preference Elicitation with Language Models. CoRR (2024)
Jannik Kossen, Jiatong Han, Muhammed Razzak, Lisa Schut, Shreshth A. Malik, Yarin Gal
Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs. CoRR (2024)
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross J. Anderson, Yarin Gal
AI models collapse when trained on recursively generated data. Nat. 631 (8022) (2024)
Jannik Kossen, Yarin Gal, Tom Rainforth
In-Context Learning Learns Label Relationships but Is Not Conventional Learning. ICLR (2024)
Gunshi Gupta, Karmesh Yadav, Yarin Gal, Dhruv Batra, Zsolt Kira, Cong Lu, Tim G. J. Rudner
Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control. CoRR (2024)
Sebastian Farquhar, Jannik Kossen, Lorenz Kuhn, Yarin Gal
Detecting hallucinations in large language models using semantic entropy. Nat. 630 (8017) (2024)
Lorenzo Pacchiardi, Alex J. Chan, Sören Mindermann, Ilan Moscovitz, Alexa Y. Pan, Yarin Gal, Owain Evans, Jan Brauner
How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions. CoRR (2023)
Jannik Kossen, Tom Rainforth, Yarin Gal
In-Context Learning in Large Language Models Learns Label Relationships but Is Not Conventional Learning. CoRR (2023)
Peter A. Zachares, Vahan Hovhannisyan, Alan Mosca, Yarin Gal
Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirements. CoRR (2023)
Kelsey Doerksen, Yarin Gal, Freddie Kalaitzis, Cristian Rossi, David Petit, Sihan Li, Simon J. Dadson
Precipitation-Triggered Landslide Prediction in Nepal Using Machine Learning and Deep Learning. IGARSS (2023)
Maëlys Solal, Andrew Jesson, Yarin Gal, Alyson Douglas
Using uncertainty-aware machine learning models to study aerosol-cloud interactions. CoRR (2023)
Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab
DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design. CoRR (2023)
Lorenz Kuhn, Yarin Gal, Sebastian Farquhar
Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation. CoRR (2023)
Andrew Jesson, Chris Lu, Gunshi Gupta, Angelos Filos, Jakob Nicolaus Foerster, Yarin Gal
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages. CoRR (2023)
Shreshth A. Malik, Salem Lahlou, Andrew Jesson, Moksh Jain, Nikolay Malkin, Tristan Deleu, Yoshua Bengio, Yarin Gal
BatchGFN: Generative Flow Networks for Batch Active Learning. CoRR (2023)
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, Ross J. Anderson
The Curse of Recursion: Training on Generated Data Makes Models Forget. CoRR (2023)
Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal
Tractable Function-Space Variational Inference in Bayesian Neural Networks. CoRR (2023)
Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal
Continual Learning via Sequential Function-Space Variational Inference. CoRR (2023)
Pascal Notin, Aaron Kollasch, Daniel Ritter, Lood van Niekerk, Steffanie Paul, Han Spinner, Nathan J. Rollins, Ada Shaw, Rose Orenbuch, Ruben Weitzman, Jonathan Frazer, Mafalda Dias, Dinko Franceschi, Yarin Gal, Debora S. Marks
ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design. NeurIPS (2023)
Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer
Differentiable Multi-Target Causal Bayesian Experimental Design. ICML (2023)
Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth
Prediction-Oriented Bayesian Active Learning. AISTATS (2023)
Lorenz Kuhn, Yarin Gal, Sebastian Farquhar
Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation. ICLR (2023)
Andreas Kirsch, Sebastian Farquhar, Parmida Atighehchian, Andrew Jesson, Frédéric Branchaud-Charron, Yarin Gal
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning. Trans. Mach. Learn. Res. 2023 (2023)
Yulin Zhou, Yiren Zhao, Ilia Shumailov, Robert D. Mullins, Yarin Gal
Revisiting Automated Prompting: Are We Actually Doing Better? ACL (2) (2023)
Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal
Deep Deterministic Uncertainty: A New Simple Baseline. CVPR (2023)
Pascal Notin, Ruben Weitzman, Debora S. Marks, Yarin Gal
ProteinNPT: Improving protein property prediction and design with non-parametric transformers. NeurIPS (2023)
Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? CoRR (2023)
David Glukhov, Ilia Shumailov, Yarin Gal, Nicolas Papernot, Vardan Papyan
LLM Censorship: A Machine Learning Challenge or a Computer Security Problem? CoRR (2023)
Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab
DiscoBAX: Discovery of optimal intervention sets in genomic experiment design. ICML (2023)
Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth
Prediction-Oriented Bayesian Active Learning. CoRR (2023)
Yashas Annadani, Panagiotis Tigas, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer
Differentiable Multi-Target Causal Bayesian Experimental Design. CoRR (2023)
Yulin Zhou, Yiren Zhao, Ilia Shumailov, Robert Mullins, Yarin Gal
Revisiting Automated Prompting: Are We Actually Doing Better? CoRR (2023)
Jishnu Mukhoti, Yarin Gal, Philip H. S. Torr, Puneet K. Dokania
Fine-tuning can cripple your foundation model; preserving features may be the solution. CoRR (2023)
Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? CLeaR (2023)
Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena-Hurtado, Aidan N. Gomez, Debora S. Marks, Yarin Gal
Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML (2022)
Milad Alizadeh, Shyam A. Tailor, Luisa M. Zintgraf, Joost van Amersfoort, Sebastian Farquhar, Nicholas Donald Lane, Yarin Gal
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients. CoRR (2022)
Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer
Interventions, Where and How? Experimental Design for Causal Models at Scale. NeurIPS (2022)
Raghav Mehta, Thomas Christinck, Tanya Nair, Aurélie Bussy, Swapna Premasiri, Manuela Costantino, M. Mallar Chakravarthy, Douglas L. Arnold, Yarin Gal, Tal Arbel
Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference. IEEE Trans. Medical Imaging 41 (2) (2022)
Vishal Upendran, Panagiotis Tigas, Banafsheh Ferdousi, Téo Bloch, Mark C. M. Cheung, Siddha Ganju, Asti Bhatt, Ryan M. McGranaghan, Yarin Gal
Global geomagnetic perturbation forecasting using Deep Learning. CoRR (2022)
Valentina Salvatelli, Luiz F. G. dos Santos, Souvik Bose, Brad Neuberg, Mark C. M. Cheung, Miho Janvier, Meng Jin, Yarin Gal, Atilim Gunes Baydin
Exploring the Limits of Synthetic Creation of Solar EUV Images via Image-to-Image Translation. CoRR (2022)
Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal
Learning Dynamics and Generalization in Reinforcement Learning. CoRR (2022)
Lorenz Kuhn, Yarin Gal, Sebastian Farquhar
CLAM: Selective Clarification for Ambiguous Questions with Large Language Models. CoRR (2022)
Andreas Kirsch, Yarin Gal
A Note on "Assessing Generalization of SGD via Disagreement". CoRR (2022)
A. Tuan Nguyen, Toan Tran, Yarin Gal, Philip H. S. Torr, Atilim Gunes Baydin
KL Guided Domain Adaptation. ICLR (2022)
Milad Alizadeh, Shyam A. Tailor, Luisa M. Zintgraf, Joost van Amersfoort, Sebastian Farquhar, Nicholas Donald Lane, Yarin Gal
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients. ICLR (2022)
Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation. CoRR (2022)
Sören Mindermann, Jan Markus Brauner, Muhammed Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N. Gomez, Adrien Morisot, Sebastian Farquhar, Yarin Gal
Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt. ICML (2022)
Andreas Kirsch, Yarin Gal
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities. Trans. Mach. Learn. Res. 2022 (2022)
Andreas Kirsch, Yarin Gal
A Note on "Assessing Generalization of SGD via Disagreement". Trans. Mach. Learn. Res. 2022 (2022)
Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal
Continual Learning via Sequential Function-Space Variational Inference. ICML (2022)
Andreas Kirsch, Yarin Gal
Unifying Approaches in Data Subset Selection via Fisher Information and Information-Theoretic Quantities. CoRR (2022)
Aidan N. Gomez, Oscar Key, Kuba Perlin, Stephen Gou, Nick Frosst, Jeff Dean, Yarin Gal
Interlocking Backpropagation: Improving depthwise model-parallelism. J. Mach. Learn. Res. 23 (2022)
Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal
Learning Dynamics and Generalization in Deep Reinforcement Learning. ICML (2022)
Siddhartha Rao Kamalakara, Acyr Locatelli, Bharat Venkitesh, Jimmy Ba, Yarin Gal, Aidan N. Gomez
Exploring Low Rank Training of Deep Neural Networks. CoRR (2022)
Chetan Gohil, Evan Roberts, Ryan C. Timms, Alex Skates, Cameron Higgins, Andrew Quinn, Usama Pervaiz, Joost van Amersfoort, Pascal Notin, Yarin Gal, Stanislaw Adaszewski, Mark W. Woolrich
Mixtures of large-scale dynamic functional brain network modes. NeuroImage 263 (2022)
Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W. Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. CoRR (2022)
Andrew Jesson, Alyson Douglas, Peter Manshausen, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit
Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions. CoRR (2022)
Dustin Tran, Jeremiah Liu, Michael W. Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan
Plex: Towards Reliability using Pretrained Large Model Extensions. CoRR (2022)
Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer
Interventions, Where and How? Experimental Design for Causal Models at Scale. CoRR (2022)
Sören Mindermann, Jan Markus Brauner, Muhammed Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N. Gomez, Adrien Morisot, Sebastian Farquhar, Yarin Gal
Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt. CoRR (2022)
Andrew Jesson, Alyson Douglas, Peter Manshausen, Maëlys Solal, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit
Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions. NeurIPS (2022)
Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab
GeneDisco: A Benchmark for Experimental Design in Drug Discovery. ICLR (2022)
Jannik Kossen, Sebastian Farquhar, Yarin Gal, Thomas Rainforth
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation. NeurIPS (2022)
Shreshth A. Malik, Nora L. Eisner, Chris J. Lintott, Yarin Gal
Discovering Long-period Exoplanets using Deep Learning with Citizen Science Labels. CoRR (2022)
Tim G. J. Rudner, Cong Lu, Michael A. Osborne, Yarin Gal, Yee Whye Teh
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations. CoRR (2022)
Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal
Tractable Function-Space Variational Inference in Bayesian Neural Networks. NeurIPS (2022)
Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena-Hurtado, Aidan N. Gomez, Debora S. Marks, Yarin Gal
Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval. CoRR (2022)
Andreas Kirsch, Jannik Kossen, Yarin Gal
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling. CoRR (2022)
Tim G. J. Rudner, Cong Lu, Michael A. Osborne, Yarin Gal, Yee Whye Teh
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations. NeurIPS (2021)
Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Tom Rainforth, Yarin Gal
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning. CoRR (2021)
A. Tuan Nguyen, Toan Tran, Yarin Gal, Atilim Günes Baydin
Domain Invariant Representation Learning with Domain Density Transformations. CoRR (2021)
Masanori Koyama, Kentaro Minami, Takeru Miyato, Yarin Gal
Contrastive Representation Learning with Trainable Augmentation Channel. CoRR (2021)
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 Newman
Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization. CoRR (2021)
Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Thomas Rainforth, Yarin Gal
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning. NeurIPS (2021)
Lisa Schut, Oscar Key, Rory McGrath, Luca Costabello, Bogdan Sacaleanu, Medb Corcoran, Yarin Gal
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties. AISTATS (2021)
Joost van Amersfoort, Lewis Smith, Andrew Jesson, Oscar Key, Yarin Gal
Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression. CoRR (2021)
Haiwen Huang, Joost van Amersfoort, Yarin Gal
Decomposing Representations for Deterministic Uncertainty Estimation. CoRR (2021)
Sebastian Farquhar, Yarin Gal, Tom Rainforth
On Statistical Bias In Active Learning: How and When To Fix It. CoRR (2021)
Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. NeurIPS (2021)
Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal
Improving black-box optimization in VAE latent space using decoder uncertainty. CoRR (2021)
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, Denis Roginskiy, Mariya Shmatova, Panagiotis Tigas, Boris Yangel
Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks. NeurIPS Datasets and Benchmarks (2021)
Muhammed Razzak, Gonzalo Mateo-Garcia, Luis Gómez-Chova, Yarin Gal, Freddie Kalaitzis
Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation. CoRR (2021)
Andreas Kirsch, Sebastian Farquhar, Yarin Gal
A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions. CoRR (2021)
Lisa Schut, Oscar Key, Rory McGrath, Luca Costabello, Bogdan Sacaleanu, Medb Corcoran, Yarin Gal
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties. CoRR (2021)
Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. ICML (2021)
Sebastian Farquhar, Yarin Gal, Tom Rainforth
On Statistical Bias In Active Learning: How and When to Fix It. ICLR (2021)
Miroslav Fil, Binxin Ru, Clare Lyle, Yarin Gal
DARTS without a Validation Set: Optimizing the Marginal Likelihood. CoRR (2021)
Sören Mindermann, Muhammed Razzak, Winnie Xu, Andreas Kirsch, Mrinank Sharma, Adrien Morisot, Aidan N. Gomez, Sebastian Farquhar, Jan Markus Brauner, Yarin Gal
Prioritized training on points that are learnable, worth learning, and not yet learned. CoRR (2021)
Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. CoRR (2021)
Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth
Active Testing: Sample-Efficient Model Evaluation. ICML (2021)
Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab
GeneDisco: A Benchmark for Experimental Design in Drug Discovery. CoRR (2021)
Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth
Active Testing: Sample-Efficient Model Evaluation. CoRR (2021)

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Publication Activity (10 Years)

Top Topics

Top Venues

Yarin Gal