default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDYarin Gal
Person information
- affiliation: University of Oxford, Department of Computer Science, UK
- affiliation: Alan Turing Institute, London, UK
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j11]Sebastian Farquhar
, Jannik Kossen, Lorenz Kuhn, Yarin Gal:
Detecting hallucinations in large language models using semantic entropy. Nat. 630(8017): 625-630 (2024)- [j10]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): 755-759 (2024) - [j9]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) - [c77]Jannik Kossen, Yarin Gal, Tom Rainforth:
In-Context Learning Learns Label Relationships but Is Not Conventional Learning. ICLR 2024 - [c76]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 - [c75]David Glukhov, Ilia Shumailov, Yarin Gal, Nicolas Papernot, Vardan Papyan:
Position: Fundamental Limitations of LLM Censorship Necessitate New Approaches. ICML 2024 - [c74]Andrew Jesson, Chris Lu, Gunshi Gupta, Nicolas Beltran-Velez, Angelos Filos, Jakob Nicolaus Foerster, Yarin Gal:
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages. ICML 2024 - [c73]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. ICML 2024 - [i145]Kunal Handa, Yarin Gal, Ellie Pavlick, Noah D. Goodman, Jacob Andreas, Alex Tamkin, Belinda Z. Li:
Bayesian Preference Elicitation with Language Models. CoRR abs/2403.05534 (2024) - [i144]Angus Nicolson, Lisa Schut, J. Alison Noble, Yarin Gal:
Explaining Explainability: Understanding Concept Activation Vectors. CoRR abs/2404.03713 (2024) - [i143]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 abs/2405.05852 (2024) - [i142]Alexander Nikitin, Jannik Kossen, Yarin Gal, Pekka Marttinen:
Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities. CoRR abs/2405.20003 (2024) - [i141]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 abs/2406.03209 (2024) - [i140]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 abs/2406.07457 (2024) - [i139]Luckeciano C. Melo, Panagiotis Tigas, Alessandro Abate, Yarin Gal:
Deep Bayesian Active Learning for Preference Modeling in Large Language Models. CoRR abs/2406.10023 (2024) - [i138]Muhammed Razzak, Andreas Kirsch, Yarin Gal:
The Benefits and Risks of Transductive Approaches for AI Fairness. CoRR abs/2406.12011 (2024) - [i137]Jannik Kossen, Jiatong Han, Muhammed Razzak, Lisa Schut, Shreshth A. Malik, Yarin Gal:
Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs. CoRR abs/2406.15927 (2024) - [i136]Yoav Gelberg, Tycho F. A. van der Ouderaa, Mark van der Wilk, Yarin Gal:
Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks. CoRR abs/2408.05496 (2024) - [i135]Angus Nicolson, Yarin Gal, J. Alison Noble:
TextCAVs: Debugging vision models using text. CoRR abs/2408.08652 (2024) - [i134]Luckeciano C. Melo, Alessandro Abate, Yarin Gal:
Temporal-Difference Variational Continual Learning. CoRR abs/2410.07812 (2024) - [i133]Maksym Andriushchenko, Alexandra Souly, Mateusz Dziemian, Derek Duenas, Maxwell Lin, Justin Wang, Dan Hendrycks, Andy Zou, Zico Kolter, Matt Fredrikson, Eric Winsor, Jerome Wynne, Yarin Gal, Xander Davies:
AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents. CoRR abs/2410.09024 (2024) - [i132]Benedict Aaron Tjandra, Muhammed Razzak, Jannik Kossen, Kunal Handa, Yarin Gal:
Fine-Tuning Large Language Models to Appropriately Abstain with Semantic Entropy. CoRR abs/2410.17234 (2024) - 2023
- [j8]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) - [c72]Yulin Zhou, Yiren Zhao, Ilia Shumailov, Robert D. Mullins, Yarin Gal:
Revisiting Automated Prompting: Are We Actually Doing Better? ACL (2) 2023: 1822-1832 - [c71]Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth:
Prediction-Oriented Bayesian Active Learning. AISTATS 2023: 7331-7348 - [c70]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: 386-407 - [c69]Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal:
Deep Deterministic Uncertainty: A New Simple Baseline. CVPR 2023: 24384-24394 - [c68]Lorenz Kuhn, Yarin Gal, Sebastian Farquhar:
Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation. ICLR 2023 - [c67]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: 23170-23189 - [c66]Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer:
Differentiable Multi-Target Causal Bayesian Experimental Design. ICML 2023: 34263-34279 - [c65]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: 4962-4965 - [c64]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 - [c63]Pascal Notin, Ruben Weitzman, Debora S. Marks, Yarin Gal:
ProteinNPT: Improving protein property prediction and design with non-parametric transformers. NeurIPS 2023 - [i131]Maëlys Solal, Andrew Jesson, Yarin Gal, Alyson Douglas:
Using uncertainty-aware machine learning models to study aerosol-cloud interactions. CoRR abs/2301.11921 (2023) - [i130]Lorenz Kuhn, Yarin Gal, Sebastian Farquhar:
Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation. CoRR abs/2302.09664 (2023) - [i129]Yashas Annadani, Panagiotis Tigas, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer:
Differentiable Multi-Target Causal Bayesian Experimental Design. CoRR abs/2302.10607 (2023) - [i128]Yulin Zhou, Yiren Zhao, Ilia Shumailov, Robert D. Mullins, Yarin Gal:
Revisiting Automated Prompting: Are We Actually Doing Better? CoRR abs/2304.03609 (2023) - [i127]Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth:
Prediction-Oriented Bayesian Active Learning. CoRR abs/2304.08151 (2023) - [i126]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 abs/2305.17493 (2023) - [i125]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 abs/2306.01460 (2023) - [i124]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 abs/2306.15058 (2023) - [i123]David Glukhov, Ilia Shumailov, Yarin Gal, Nicolas Papernot, Vardan Papyan:
LLM Censorship: A Machine Learning Challenge or a Computer Security Problem? CoRR abs/2307.10719 (2023) - [i122]Jannik Kossen, Tom Rainforth, Yarin Gal:
In-Context Learning in Large Language Models Learns Label Relationships but Is Not Conventional Learning. CoRR abs/2307.12375 (2023) - [i121]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 abs/2308.13320 (2023) - [i120]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 abs/2309.15840 (2023) - [i119]Peter A. Zachares, Vahan Hovhannisyan, Alan Mosca, Yarin Gal:
Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirements. CoRR abs/2311.00444 (2023) - [i118]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 abs/2312.04064 (2023) - [i117]Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal:
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? CoRR abs/2312.17168 (2023) - [i116]Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal:
Tractable Function-Space Variational Inference in Bayesian Neural Networks. CoRR abs/2312.17199 (2023) - [i115]Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal:
Continual Learning via Sequential Function-Space Variational Inference. CoRR abs/2312.17210 (2023) - 2022
- [j7]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: 171:1-171:28 (2022) - [j6]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: 119595 (2022) - [j5]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): 360-373 (2022) - [j4]Andreas Kirsch, Yarin Gal:
A Note on "Assessing Generalization of SGD via Disagreement". Trans. Mach. Learn. Res. 2022 (2022) - [j3]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) - [c62]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 - [c61]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 - [c60]A. Tuan Nguyen, Toan Tran, Yarin Gal, Philip H. S. Torr, Atilim Gunes Baydin:
KL Guided Domain Adaptation. ICLR 2022 - [c59]Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal:
Learning Dynamics and Generalization in Deep Reinforcement Learning. ICML 2022: 14560-14581 - [c58]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: 15630-15649 - [c57]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: 16990-17017 - [c56]Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal:
Continual Learning via Sequential Function-Space Variational Inference. ICML 2022: 18871-18887 - [c55]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 - [c54]Jannik Kossen, Sebastian Farquhar, Yarin Gal, Thomas Rainforth:
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation. NeurIPS 2022 - [c53]Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal:
Tractable Function-Space Variational Inference in Bayesian Neural Networks. NeurIPS 2022 - [c52]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 - [i114]Andreas Kirsch, Yarin Gal:
A Note on "Assessing Generalization of SGD via Disagreement". CoRR abs/2202.01851 (2022) - [i113]Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth:
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation. CoRR abs/2202.06881 (2022) - [i112]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 abs/2202.08132 (2022) - [i111]Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer:
Interventions, Where and How? Experimental Design for Causal Models at Scale. CoRR abs/2203.02016 (2022) - [i110]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 abs/2204.10022 (2022) - [i109]Andreas Kirsch, Jannik Kossen, Yarin Gal:
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling. CoRR abs/2205.08766 (2022) - [i108]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 abs/2205.12734 (2022) - [i107]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 abs/2205.13760 (2022) - [i106]Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal:
Learning Dynamics and Generalization in Reinforcement Learning. CoRR abs/2206.02126 (2022) - [i105]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 abs/2206.07137 (2022) - [i104]Dustin Tran, Jeremiah Z. 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 abs/2207.07411 (2022) - [i103]Andreas Kirsch, Yarin Gal:
Unifying Approaches in Data Subset Selection via Fisher Information and Information-Theoretic Quantities. CoRR abs/2208.00549 (2022) - [i102]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 abs/2208.09512 (2022) - [i101]Siddhartha Rao Kamalakara, Acyr Locatelli, Bharat Venkitesh, Jimmy Ba, Yarin Gal, Aidan N. Gomez:
Exploring Low Rank Training of Deep Neural Networks. CoRR abs/2209.13569 (2022) - [i100]Shreshth A. Malik, Nora L. Eisner, Chris J. Lintott, Yarin Gal:
Discovering Long-period Exoplanets using Deep Learning with Citizen Science Labels. CoRR abs/2211.06903 (2022) - [i99]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 abs/2211.12717 (2022) - [i98]Lorenz Kuhn, Yarin Gal, Sebastian Farquhar:
CLAM: Selective Clarification for Ambiguous Questions with Large Language Models. CoRR abs/2212.07769 (2022) - [i97]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 abs/2212.13936 (2022) - 2021
- [j2]Luisa M. Zintgraf, Sebastian Schulze, Cong Lu, Leo Feng, Maximilian Igl, Kyriacos Shiarlis, Yarin Gal, Katja Hofmann, Shimon Whiteson:
VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning. J. Mach. Learn. Res. 22: 289:1-289:39 (2021) - [c51]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: 1756-1764 - [c50]Amy Zhang, Rowan Thomas McAllister, Roberto Calandra, Yarin Gal, Sergey Levine:
Learning Invariant Representations for Reinforcement Learning without Reconstruction. ICLR 2021 - [c49]Sebastian Farquhar, Yarin Gal, Tom Rainforth:
On Statistical Bias In Active Learning: How and When to Fix It. ICLR 2021 - [c48]Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar:
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning. ICML 2021: 3305-3317 - [c47]Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit:
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. ICML 2021: 4829-4838 - [c46]Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth:
Active Testing: Sample-Efficient Model Evaluation. ICML 2021: 5753-5763 - [c45]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: 9148-9156 - [c44]Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Mike Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal:
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. NeurIPS Datasets and Benchmarks 2021 - [c43]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: 30465-30478 - [c42]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 - [c41]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:
Evaluating Approximate Inference in Bayesian Deep Learning. NeurIPS (Competition and Demos) 2021: 113-124 - [c40]Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal:
Improving black-box optimization in VAE latent space using decoder uncertainty. NeurIPS 2021: 802-814 - [c39]Robin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal:
Speedy Performance Estimation for Neural Architecture Search. NeurIPS 2021: 4079-4092 - [c38]A. Tuan Nguyen, Toan Tran, Yarin Gal, Atilim Gunes Baydin:
Domain Invariant Representation Learning with Domain Density Transformations. NeurIPS 2021: 5264-5275 - [c37]Tim G. J. Rudner, Vitchyr Pong, Rowan McAllister, Yarin Gal, Sergey Levine:
Outcome-Driven Reinforcement Learning via Variational Inference. NeurIPS 2021: 13045-13058 - [c36]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: 28376-28389 - [c35]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: 28742-28756 - [i96]Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic, Siddha Ganju, Dava Newman, Sujoy Ganguly, Danny Lange, Atilim Günes Baydin, Amit Sharma, Adam Gibson, Yarin Gal, Eric P. Xing, Chris Mattmann, James Parr:
Technology Readiness Levels for Machine Learning Systems. CoRR abs/2101.03989 (2021) - [i95]Sebastian Farquhar, Yarin Gal, Tom Rainforth:
On Statistical Bias In Active Learning: How and When To Fix It. CoRR abs/2101.11665 (2021) - [i94]Panagiotis Tigas, Téo Bloch, Vishal Upendran, Banafsheh Ferdoushi, Mark C. M. Cheung, Siddha Ganju, Ryan M. McGranaghan, Yarin Gal, Asti Bhatt:
Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition. CoRR abs/2102.01447 (2021) - [i93]A. Tuan Nguyen, Toan Tran, Yarin Gal, Atilim Günes Baydin:
Domain Invariant Representation Learning with Domain Density Transformations. CoRR abs/2102.05082 (2021) - [i92]Joost van Amersfoort, Lewis Smith, Andrew Jesson, Oscar Key, Yarin Gal:
Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression. CoRR abs/2102.11409 (2021) - [i91]Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal:
Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty. CoRR abs/2102.11582 (2021) - [i90]Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar:
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning. CoRR abs/2102.12560 (2021) - [i89]Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit:
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. CoRR abs/2103.04850 (2021) - [i88]Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth:
Active Testing: Sample-Efficient Model Evaluation. CoRR abs/2103.05331 (2021) - [i87]Lorenz Kuhn, Clare Lyle, Aidan N. Gomez, Jonas Rothfuss, Yarin Gal:
Robustness to Pruning Predicts Generalization in Deep Neural Networks. CoRR abs/2103.06002 (2021) - [i86]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 abs/2103.08951 (2021) - [i85]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 abs/2104.04785 (2021) - [i84]Tim G. J. Rudner, Vitchyr H. Pong, Rowan McAllister, Yarin Gal, Sergey Levine:
Outcome-Driven Reinforcement Learning via Variational Inference. CoRR abs/2104.10190 (2021) - [i83]Lewis Smith, Joost van Amersfoort, Haiwen Huang, Stephen J. Roberts, Yarin Gal:
Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective. CoRR abs/2106.02469 (2021) - [i82]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 abs/2106.02584 (2021) - [i81]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, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran:
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR abs/2106.04015 (2021) - [i80]A. Tuan Nguyen, Toan Tran, Yarin Gal, Philip H. S. Torr, Atilim Günes Baydin:
KL Guided Domain Adaptation. CoRR abs/2106.07780 (2021) - [i79]Andreas Kirsch, Tom Rainforth, Yarin Gal:
Active Learning under Pool Set Distribution Shift and Noisy Data. CoRR abs/2106.11719 (2021) - [i78]Andreas Kirsch, Sebastian Farquhar, Yarin Gal:
A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions. CoRR abs/2106.12059 (2021) - [i77]Andreas Kirsch, Yarin Gal:
A Practical & Unified Notation for Information-Theoretic Quantities in ML. CoRR abs/2106.12062 (2021) - [i76]Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal:
Improving black-box optimization in VAE latent space using decoder uncertainty. CoRR abs/2107.00096 (2021) - [i75]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 abs/2107.02565 (2021) - [i74]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, Mariya Shmatova, Panos Tigas, Boris Yangel:
Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks. CoRR abs/2107.07455 (2021) - [i73]Owen Convery, Lewis Smith, Yarin Gal, Adi Hanuka:
Quantifying Uncertainty for Machine Learning Based Diagnostic. CoRR abs/2107.14261 (2021) - [i72]Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab:
GeneDisco: A Benchmark for Experimental Design in Drug Discovery. CoRR abs/2110.11875 (2021) - [i71]Andrew Jesson, Peter Manshausen, Alyson Douglas, Duncan Watson-Parris, Yarin Gal, Philip Stier:
Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific. CoRR abs/2110.15084 (2021) - [i70]Jishnu Mukhoti, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal:
Deep Deterministic Uncertainty for Semantic Segmentation. CoRR abs/2111.00079 (2021) - [i69]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 abs/2111.02275 (2021) - [i68]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 abs/2111.03231 (2021) - [i67]Masanori Koyama, Kentaro Minami, Takeru Miyato, Yarin Gal:
Contrastive Representation Learning with Trainable Augmentation Channel. CoRR abs/2111.07679 (2021) - [i66]Benedikt Höltgen, Lisa Schut, Jan Markus Brauner, Yarin Gal:
DeDUCE: Generating Counterfactual Explanations Efficiently. CoRR abs/2111.15639 (2021) - [i65]Haiwen Huang, Joost van Amersfoort, Yarin Gal:
Decomposing Representations for Deterministic Uncertainty Estimation. CoRR abs/2112.00856 (2021) - [i64]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, Benjamin C. Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Alexandra Daza, Catalina Gómez Caballero, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Raynaud, Yuanhan Mo, Elsa D. Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Linmin Pei, Murat Ak, Sarahi Rosas-González, Ilyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Verónica Vilaplana, Hugh McHugh, Gonzalo D. Maso Talou, Alan Wang, Jay B. Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Thumbavanam Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicolas Boutry, Alexis Huard, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Élodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Lladó, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas J. Tustison, Craig H. Meyer, Nisarg A. Shah, Sanjay N. Talbar, Marc-André Weber, Abhishek Mahajan, András Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko, Daniel S. Marcus, Aikaterini Kotrotsou, Rivka Colen, John B. Freymann, Justin S. Kirby, Christos Davatzikos, Bjoern H. Menze, Spyridon Bakas, Yarin Gal, Tal Arbel:
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results. CoRR abs/2112.10074 (2021) - [i63]Miroslav Fil, Binxin Ru, Clare Lyle, Yarin Gal:
DARTS without a Validation Set: Optimizing the Marginal Likelihood. CoRR abs/2112.13023 (2021) - 2020
- [j1]Iryna Korshunova, Yarin Gal, Arthur Gretton, Joni Dambre:
Conditional BRUNO: A neural process for exchangeable labelled data. Neurocomputing 416: 305-309 (2020) - [c34]Sebastian Farquhar, Michael A. Osborne, Yarin Gal:
Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning. AISTATS 2020: 1352-1362 - [c33]Binxin Ru, Adam D. Cobb, Arno Blaas, Yarin Gal:
BayesOpt Adversarial Attack. ICLR 2020 - [c32]Luisa M. Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, Shimon Whiteson:
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning. ICLR 2020 - [c31]Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal:
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? ICML 2020: 3145-3153 - [c30]Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal:
Inter-domain Deep Gaussian Processes. ICML 2020: 8286-8294 - [c29]Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal:
Uncertainty Estimation Using a Single Deep Deterministic Neural Network. ICML 2020: 9690-9700 - [c28]Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup:
Invariant Causal Prediction for Block MDPs. ICML 2020: 11214-11224 - [c27]Rhiannon Michelmore, Matthew Wicker, Luca Laurenti, Luca Cardelli, Yarin Gal, Marta Kwiatkowska:
Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control. ICRA 2020: 7344-7350 - [c26]Marc Rußwurm, Mohsin Ali, Xiaoxiang Zhu, Yarin Gal, Marco Körner:
Model and Data Uncertainty for Satellite Time Series Forecasting with Deep Recurrent Models. IGARSS 2020: 7025-7028 - [c25]Sebastian Farquhar, Lewis Smith, Yarin Gal:
Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations. NeurIPS 2020 - [c24]Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal:
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models. NeurIPS 2020 - [c23]Clare Lyle, Lisa Schut, Robin Ru, Yarin Gal, Mark van der Wilk:
A Bayesian Perspective on Training Speed and Model Selection. NeurIPS 2020 - [c22]Mrinank Sharma, Sören Mindermann, Jan Markus Brauner, Gavin Leech, Anna B. Stephenson, Tomas Gavenciak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal:
How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19? NeurIPS 2020 - [d1]Andrés Muñoz-Jaramillo, Xavier Gitiaux, Anna Jungbluth, Shane A. Maloney, Carl Shneider, Paul J. Wright, Michel Deudon, Alfredo Kalaitzis, Atilim Günes Baydin, Yarin Gal:
Converter software to calibrate and super-resolve solar magnetograms. Zenodo, 2020
- [i62]Sebastian Farquhar, Lewis Smith, Yarin Gal:
Try Depth Instead of Weight Correlations: Mean-field is a Less Restrictive Assumption for Deeper Networks. CoRR abs/2002.03704 (2020) - [i61]Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal:
Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network. CoRR abs/2003.02037 (2020) - [i60]Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup:
Invariant Causal Prediction for Block MDPs. CoRR abs/2003.06016 (2020) - [i59]Yarin Gal, Vishnu Jejjala, Damian Kaloni Mayorga Pena, Challenger Mishra:
Baryons from Mesons: A Machine Learning Perspective. CoRR abs/2003.10445 (2020) - [i58]Andreas Kirsch, Clare Lyle, Yarin Gal:
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning. CoRR abs/2003.12537 (2020) - [i57]Lewis Smith, Lisa Schut, Yarin Gal, Mark van der Wilk:
Capsule Networks - A Probabilistic Perspective. CoRR abs/2004.03553 (2020) - [i56]Clare Lyle, Mark van der Wilk, Marta Kwiatkowska, Yarin Gal, Benjamin Bloem-Reddy:
On the Benefits of Invariance in Neural Networks. CoRR abs/2005.00178 (2020) - [i55]Raghav Mehta, Angelos Filos, Yarin Gal, Tal Arbel:
Uncertainty Evaluation Metric for Brain Tumour Segmentation. CoRR abs/2005.14262 (2020) - [i54]Binxin Ru, Clare Lyle, Lisa Schut, Mark van der Wilk, Yarin Gal:
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search. CoRR abs/2006.04492 (2020) - [i53]Tim Z. Xiao, Aidan N. Gomez, Yarin Gal:
Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers. CoRR abs/2006.08344 (2020) - [i52]Amy Zhang, Rowan McAllister, Roberto Calandra, Yarin Gal, Sergey Levine:
Learning Invariant Representations for Reinforcement Learning without Reconstruction. CoRR abs/2006.10742 (2020) - [i51]Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal:
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? CoRR abs/2006.14911 (2020) - [i50]Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal:
Identifying Causal Effect Inference Failure with Uncertainty-Aware Models. CoRR abs/2007.00163 (2020) - [i49]Joost van Amersfoort, Milad Alizadeh, Sebastian Farquhar, Nicholas D. Lane, Yarin Gal:
Single Shot Structured Pruning Before Training. CoRR abs/2007.00389 (2020) - [i48]Pascal Notin, Aidan N. Gomez, Joanna Yoo, Yarin Gal:
SliceOut: Training Transformers and CNNs faster while using less memory. CoRR abs/2007.10909 (2020) - [i47]Mrinank Sharma, Sören Mindermann, Jan Markus Brauner, Gavin Leech, Anna B. Stephenson, Tomas Gavenciak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal:
On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission. CoRR abs/2007.13454 (2020) - [i46]Aidan N. Gomez, Oscar Key, Stephen Gou, Nick Frosst, Jeff Dean, Yarin Gal:
Interlocking Backpropagation: Improving depthwise model-parallelism. CoRR abs/2010.04116 (2020) - [i45]Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa, Farrukh Chishtie, Natalia Díaz Rodríguez, Océane Boulais, Aaron Piña, Dava Newman, Alexander Lavin, Yarin Gal, Chedy Raïssi:
Physics-informed GANs for Coastal Flood Visualization. CoRR abs/2010.08103 (2020) - [i44]Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk:
A Bayesian Perspective on Training Speed and Model Selection. CoRR abs/2010.14499 (2020) - [i43]Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal:
Inter-domain Deep Gaussian Processes. CoRR abs/2011.00415 (2020) - [i42]Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth:
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. CoRR abs/2011.00515 (2020) - [i41]Mizu Nishikawa-Toomey, Lewis Smith, Yarin Gal:
Semi-supervised Learning of Galaxy Morphology using Equivariant Transformer Variational Autoencoders. CoRR abs/2011.08714 (2020) - [i40]Jishnu Mukhoti, Puneet K. Dokania, Philip H. S. Torr, Yarin Gal:
On Batch Normalisation for Approximate Bayesian Inference. CoRR abs/2012.13220 (2020) - [i39]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:
Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning. CoRR abs/2012.14023 (2020)
2010 – 2019
- 2019
- [c21]Iryna Korshunova, Yarin Gal, Arthur Gretton, Joni Dambre:
Conditional BRUNO: a neural process for exchangeable labelled data. ESANN 2019 - [c20]Milad Alizadeh, Javier Fernández-Marqués, Nicholas D. Lane, Yarin Gal:
An Empirical study of Binary Neural Networks' Optimisation. ICLR (Poster) 2019 - [c19]Andreas Kirsch, Joost van Amersfoort, Yarin Gal:
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning. NeurIPS 2019: 7024-7035 - [i38]Sebastian Farquhar, Yarin Gal:
A Unifying Bayesian View of Continual Learning. CoRR abs/1902.06494 (2019) - [i37]Sebastian Farquhar, Yarin Gal:
Differentially Private Continual Learning. CoRR abs/1902.06497 (2019) - [i36]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, Darryl Wright:
Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning. CoRR abs/1905.07424 (2019) - [i35]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:
An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval. CoRR abs/1905.10659 (2019) - [i34]Aidan N. Gomez, Ivan Zhang, Kevin Swersky, Yarin Gal, Geoffrey E. Hinton:
Learning Sparse Networks Using Targeted Dropout. CoRR abs/1905.13678 (2019) - [i33]Jacobo Roa-Vicens, Cyrine Chtourou, Angelos Filos, Francisco Rullan, Yarin Gal, Ricardo Silva:
Towards Inverse Reinforcement Learning for Limit Order Book Dynamics. CoRR abs/1906.04813 (2019) - [i32]Andreas Kirsch, Joost van Amersfoort, Yarin Gal:
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning. CoRR abs/1906.08158 (2019) - [i31]Sebastian Farquhar, Michael A. Osborne, Yarin Gal:
Radial Bayesian Neural Networks: Robust Variational Inference In Big Models. CoRR abs/1907.00865 (2019) - [i30]Zachary Kenton, Angelos Filos, Owain Evans, Yarin Gal:
Generalizing from a few environments in safety-critical reinforcement learning. CoRR abs/1907.01475 (2019) - [i29]Rhiannon Michelmore, Matthew Wicker, Luca Laurenti, Luca Cardelli, Yarin Gal, Marta Kwiatkowska:
Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control. CoRR abs/1909.09884 (2019) - [i28]Kara Lamb, Garima Malhotra, Athanasios Vlontzos, Edward Wagstaff, Atilim Günes Baydin, Anahita Bhiwandiwalla, Yarin Gal, Alfredo Kalaitzis, Anthony Reina, Asti Bhatt:
Prediction of GNSS Phase Scintillations: A Machine Learning Approach. CoRR abs/1910.01570 (2019) - [i27]Gonzalo Mateo-Garcia, Silviu Oprea, Lewis Smith, Josh Veitch-Michaelis, Guy Schumann, Yarin Gal, Atilim Günes Baydin, Dietmar Backes:
Flood Detection On Low Cost Orbital Hardware. CoRR abs/1910.03019 (2019) - [i26]Kara Lamb, Garima Malhotra, Athanasios Vlontzos, Edward Wagstaff, Atilim Günes Baydin, Anahita Bhiwandiwalla, Yarin Gal, Alfredo Kalaitzis, Anthony Reina, Asti Bhatt:
Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder. CoRR abs/1910.03085 (2019) - [i25]Chelsea Sidrane, Dylan J. Fitzpatrick, Andrew Annex, Diane O'Donoghue, Yarin Gal, Piotr Bilinski:
Machine Learning for Generalizable Prediction of Flood Susceptibility. CoRR abs/1910.06521 (2019) - [i24]Luisa M. Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, Shimon Whiteson:
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning. CoRR abs/1910.08348 (2019) - [i23]Xavier Gitiaux, Shane A. Maloney, Anna Jungbluth, Carl Shneider, Paul J. Wright, Atilim Günes Baydin, Michel Deudon, Yarin Gal, Alfredo Kalaitzis, Andrés Muñoz-Jaramillo:
Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties. CoRR abs/1911.01486 (2019) - [i22]Anna Jungbluth, Xavier Gitiaux, Shane A. Maloney, Carl Shneider, Paul J. Wright, Alfredo Kalaitzis, Michel Deudon, Atilim Günes Baydin, Yarin Gal, Andrés Muñoz-Jaramillo:
Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses. CoRR abs/1911.01490 (2019) - [i21]Valentina Salvatelli, Souvik Bose, Brad Neuberg, Luiz F. G. dos Santos, Mark C. M. Cheung, Miho Janvier, Atilim Gunes Baydin, Yarin Gal, Meng Jin:
Using U-Nets to Create High-Fidelity Virtual Observations of the Solar Corona. CoRR abs/1911.04006 (2019) - [i20]Brad Neuberg, Souvik Bose, Valentina Salvatelli, Luiz F. G. dos Santos, Mark C. M. Cheung, Miho Janvier, Atilim Gunes Baydin, Yarin Gal, Meng Jin:
Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning. CoRR abs/1911.04008 (2019) - [i19]Jacobo Roa-Vicens, Yuanbo Wang, Virgile Mison, Yarin Gal, Ricardo Silva:
Adversarial recovery of agent rewards from latent spaces of the limit order book. CoRR abs/1912.04242 (2019) - [i18]Angelos Filos, Sebastian Farquhar, Aidan N. Gomez, Tim G. J. Rudner, Zachary Kenton, Lewis Smith, Milad Alizadeh, Arnoud de Kroon, Yarin Gal:
A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks. CoRR abs/1912.10481 (2019) - 2018
- [c18]Alex Kendall, Yarin Gal, Roberto Cipolla:
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics. CVPR 2018: 7482-7491 - [c17]Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava:
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. ICML 2018: 2616-2625 - [c16]Iryna Korshunova, Jonas Degrave, Ferenc Huszar, Yarin Gal, Arthur Gretton, Joni Dambre:
BRUNO: A Deep Recurrent Model for Exchangeable Data. NeurIPS 2018: 7190-7198 - [c15]Lewis Smith, Yarin Gal:
Understanding Measures of Uncertainty for Adversarial Example Detection. UAI 2018: 560-569 - [i17]Lewis Smith, Yarin Gal:
Understanding Measures of Uncertainty for Adversarial Example Detection. CoRR abs/1803.08533 (2018) - [i16]Adam D. Cobb, Stephen J. Roberts, Yarin Gal:
Loss-Calibrated Approximate Inference in Bayesian Neural Networks. CoRR abs/1805.03901 (2018) - [i15]Sebastian Farquhar, Yarin Gal:
Towards Robust Evaluations of Continual Learning. CoRR abs/1805.09733 (2018) - [i14]Yarin Gal, Lewis Smith:
Idealised Bayesian Neural Networks Cannot Have Adversarial Examples: Theoretical and Empirical Study. CoRR abs/1806.00667 (2018) - [i13]Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava:
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. CoRR abs/1806.04854 (2018) - [i12]Rhiannon Michelmore, Marta Kwiatkowska, Yarin Gal:
Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control. CoRR abs/1811.06817 (2018) - [i11]Jishnu Mukhoti, Pontus Stenetorp, Yarin Gal:
On the Importance of Strong Baselines in Bayesian Deep Learning. CoRR abs/1811.09385 (2018) - [i10]Jishnu Mukhoti, Yarin Gal:
Evaluating Bayesian Deep Learning Methods for Semantic Segmentation. CoRR abs/1811.12709 (2018) - 2017
- [c14]Yarin Gal, Riashat Islam, Zoubin Ghahramani:
Deep Bayesian Active Learning with Image Data. ICML 2017: 1183-1192 - [c13]Yingzhen Li, Yarin Gal:
Dropout Inference in Bayesian Neural Networks with Alpha-divergences. ICML 2017: 2052-2061 - [c12]Rowan McAllister, Yarin Gal, Alex Kendall, Mark van der Wilk, Amar Shah, Roberto Cipolla, Adrian Weller:
Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning. IJCAI 2017: 4745-4753 - [c11]Yarin Gal, Jiri Hron, Alex Kendall:
Concrete Dropout. NIPS 2017: 3581-3590 - [c10]Alex Kendall, Yarin Gal:
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? NIPS 2017: 5574-5584 - [c9]Piotr Dabkowski, Yarin Gal:
Real Time Image Saliency for Black Box Classifiers. NIPS 2017: 6967-6976 - [i9]Yarin Gal, Riashat Islam, Zoubin Ghahramani:
Deep Bayesian Active Learning with Image Data. CoRR abs/1703.02910 (2017) - [i8]Yingzhen Li, Yarin Gal:
Dropout Inference in Bayesian Neural Networks with Alpha-divergences. CoRR abs/1703.02914 (2017) - [i7]Alex Kendall, Yarin Gal:
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? CoRR abs/1703.04977 (2017) - [i6]Alex Kendall, Yarin Gal, Roberto Cipolla:
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics. CoRR abs/1705.07115 (2017) - [i5]Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt, Yarin Gal:
Vprop: Variational Inference using RMSprop. CoRR abs/1712.01038 (2017) - 2016
- [c8]Yarin Gal, Zoubin Ghahramani:
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. ICML 2016: 1050-1059 - [c7]Yarin Gal, Zoubin Ghahramani:
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks. NIPS 2016: 1019-1027 - 2015
- [c6]Yarin Gal, Yutian Chen, Zoubin Ghahramani:
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data. ICML 2015: 645-654 - [c5]Yarin Gal, Richard E. Turner:
Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs. ICML 2015: 655-664 - [i4]Yarin Gal, Zoubin Ghahramani:
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. CoRR abs/1506.02142 (2015) - [i3]Yarin Gal, Zoubin Ghahramani:
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference. CoRR abs/1506.02158 (2015) - 2014
- [c4]Yarin Gal, Zoubin Ghahramani:
Pitfalls in the use of Parallel Inference for the Dirichlet Process. ICML 2014: 208-216 - [c3]Yarin Gal, Mark van der Wilk, Carl E. Rasmussen:
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models. NIPS 2014: 3257-3265 - [i2]Yarin Gal, Mark van der Wilk, Carl E. Rasmussen:
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models. CoRR abs/1402.1389 (2014) - [i1]Yarin Gal:
Semantics, Modelling, and the Problem of Representation of Meaning - a Brief Survey of Recent Literature. CoRR abs/1402.7265 (2014) - 2013
- [c2]Yarin Gal, Phil Blunsom:
A Systematic Bayesian Treatment of the IBM Alignment Models. HLT-NAACL 2013: 969-977 - 2010
- [c1]Yarin Gal, Mireille Avigal:
Overcoming Alpha-Beta Limitations Using Evolved Artificial Neural Networks. ICMLA 2010: 813-818
Coauthor Index
Atilim Günes Baydin
aka: Atilim Gunes Baydin
aka: Atilim Gunes Baydin
Rowan McAllister
aka: Rowan Thomas McAllister
aka: Rowan Thomas McAllister
Tom Rainforth
aka: Thomas Rainforth
aka: Thomas Rainforth
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-28 20:34 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
