Yarin Gal
Orcid: 0000-0002-2733-2078Affiliations:
- University of Oxford, Department of Computer Science, UK
- Alan Turing Institute, London, UK
According to our database1,
Yarin Gal
authored at least 165 papers
between 2010 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
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Online presence:
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on turing.ac.uk
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on cs.ox.ac.uk
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on twitter.com
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on orcid.org
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on github.com
On csauthors.net:
Bibliography
2024
2023
Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirements.
CoRR, 2023
How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions.
CoRR, 2023
Fine-tuning can cripple your foundation model; preserving features may be the solution.
CoRR, 2023
In-Context Learning in Large Language Models Learns Label Relationships but Is Not Conventional Learning.
CoRR, 2023
CoRR, 2023
CoRR, 2023
CoRR, 2023
ProteinNPT: Improving protein property prediction and design with non-parametric transformers.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Precipitation-Triggered Landslide Prediction in Nepal Using Machine Learning and Deep Learning.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2023
Proceedings of the International Conference on Machine Learning, 2023
Proceedings of the International Conference on Machine Learning, 2023
Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation.
Proceedings of the Eleventh International Conference on Learning Representations, 2023
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
Proceedings of the Conference on Causal Learning and Reasoning, 2023
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2023
2022
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities.
Trans. Mach. Learn. Res., 2022
Trans. Mach. Learn. Res., 2022
Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference.
IEEE Trans. Medical Imaging, 2022
J. Mach. Learn. Res., 2022
CoRR, 2022
CoRR, 2022
Exploring the Limits of Synthetic Creation of Solar EUV Images via Image-to-Image Translation.
CoRR, 2022
Unifying Approaches in Data Subset Selection via Fisher Information and Information-Theoretic Quantities.
CoRR, 2022
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling.
CoRR, 2022
Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions.
CoRR, 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the International Conference on Machine Learning, 2022
Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval.
Proceedings of the International Conference on Machine Learning, 2022
Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt.
Proceedings of the International Conference on Machine Learning, 2022
Proceedings of the International Conference on Machine Learning, 2022
Proceedings of the Tenth International Conference on Learning Representations, 2022
Proceedings of the Tenth International Conference on Learning Representations, 2022
Proceedings of the Tenth International Conference on Learning Representations, 2022
2021
J. Mach. Learn. Res., 2021
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results.
CoRR, 2021
Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation.
CoRR, 2021
Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific.
CoRR, 2021
CoRR, 2021
Prioritized training on points that are learnable, worth learning, and not yet learned.
CoRR, 2021
CoRR, 2021
CoRR, 2021
CoRR, 2021
Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective.
CoRR, 2021
Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization.
CoRR, 2021
Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty.
CoRR, 2021
Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression.
CoRR, 2021
Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition.
CoRR, 2021
Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes.
Proceedings of the 38th International Conference on Machine Learning, 2021
Proceedings of the 38th International Conference on Machine Learning, 2021
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding.
Proceedings of the 38th International Conference on Machine Learning, 2021
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning.
Proceedings of the 38th International Conference on Machine Learning, 2021
Proceedings of the 9th International Conference on Learning Representations, 2021
Learning Invariant Representations for Reinforcement Learning without Reconstruction.
Proceedings of the 9th International Conference on Learning Representations, 2021
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021
2020
Neurocomputing, 2020
Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning.
CoRR, 2020
Semi-supervised Learning of Galaxy Morphology using Equivariant Transformer Variational Autoencoders.
CoRR, 2020
On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission.
CoRR, 2020
Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers.
CoRR, 2020
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search.
CoRR, 2020
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning.
CoRR, 2020
Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network.
CoRR, 2020
Try Depth Instead of Weight Correlations: Mean-field is a Less Restrictive Assumption for Deeper Networks.
CoRR, 2020
How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Model and Data Uncertainty for Satellite Time Series Forecasting with Deep Recurrent Models.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2020
Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020
Proceedings of the 37th International Conference on Machine Learning, 2020
Proceedings of the 37th International Conference on Machine Learning, 2020
Proceedings of the 37th International Conference on Machine Learning, 2020
Proceedings of the 37th International Conference on Machine Learning, 2020
Proceedings of the 8th International Conference on Learning Representations, 2020
Proceedings of the 8th International Conference on Learning Representations, 2020
Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020
2019
A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks.
CoRR, 2019
CoRR, 2019
CoRR, 2019
Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses.
CoRR, 2019
Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties.
CoRR, 2019
CoRR, 2019
CoRR, 2019
CoRR, 2019
CoRR, 2019
CoRR, 2019
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
Proceedings of the 7th International Conference on Learning Representations, 2019
2018
CoRR, 2018
Idealised Bayesian Neural Networks Cannot Have Adversarial Examples: Theoretical and Empirical Study.
CoRR, 2018
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
Proceedings of the 35th International Conference on Machine Learning, 2018
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018
2017
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017
Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017
Proceedings of the 34th International Conference on Machine Learning, 2017
Proceedings of the 34th International Conference on Machine Learning, 2017
2016
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning.
Proceedings of the 33nd International Conference on Machine Learning, 2016
2015
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference.
CoRR, 2015
Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs.
Proceedings of the 32nd International Conference on Machine Learning, 2015
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data.
Proceedings of the 32nd International Conference on Machine Learning, 2015
2014
Semantics, Modelling, and the Problem of Representation of Meaning - a Brief Survey of Recent Literature.
CoRR, 2014
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014
Proceedings of the 31th International Conference on Machine Learning, 2014
2013
Proceedings of the Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, 2013
2010
Proceedings of the Ninth International Conference on Machine Learning and Applications, 2010