Yarin Gal - RankMe

283 papers A* 64A 4B 1C 3Journal 196Unranked 15

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Boundary Point Jailbreaking of Black-Box LLMs. CoRR Xander Davies, Giorgi Giglemiani, Edmund Lau, Eric Winsor, Geoffrey Irving, Yarin Gal
2026	J	`jnl`	MADE: Benchmark Environments for Closed-Loop Materials Discovery. CoRR Shreshth A. Malik, Tiarnan A. S. Doherty, Panagiotis Tigas, Muhammed Razzak, Stephen J. Roberts, Aron Walsh, Yarin Gal
2026	J	`jnl`	OMNI-LEAK: Orchestrator Multi-Agent Network Induced Data Leakage. CoRR Akshat Naik, Jay Culligan, Yarin Gal, Philip Torr, Rahaf Aljundi, Alasdair Paren, Adel Bibi
2026	J	`jnl`	Open Technical Problems in Open-Weight AI Model Risk Management. Trans. Mach. Learn. Res. Stephen Casper, Kyle O'Brien, Shayne Longpre, Elizabeth Seger, Kevin Klyman, Rishi Bommasani, Aniruddha Nrusimha, Ilia Shumailov, Sören Mindermann, Steven Basart, Frank Rudzicz, Kellin Pelrine, Avijit Ghosh, Andrew Strait, Robert Kirk, Dan Hendrycks, Peter Henderson, J. Zico Kolter, Geoffrey Irving, Yarin Gal, Yoshua Bengio, Dylan Hadfield-Menell
2026	J	`jnl`	Richer Bayesian Last Layers with Subsampled NTK Features. CoRR Sergio Calvo-Ordoñez, Jonathan Plenk, Richard Bergna, Álvaro Cartea, Yarin Gal, José Miguel Hernández-Lobato, Kamil Ciosek
2026	J	`jnl`	Simple Baselines are Competitive with Code Evolution. CoRR Yonatan Gideoni, Sebastian Risi, Yarin Gal
2025	J	`jnl`	A Definition of AGI. CoRR Dan Hendrycks, Dawn Song, Christian Szegedy, Honglak Lee, Yarin Gal, Erik Brynjolfsson, Sharon Li, Andy Zou, Lionel Levine, Bo Han, Jie Fu, Ziwei Liu, Jinwoo Shin, Kimin Lee, Mantas Mazeika, Long Phan, George Ingebretsen, Adam Khoja, Cihang Xie, Olawale Salaudeen, Matthias Hein, Kevin Zhao, Alexander Pan, David Duvenaud, Bo Li, Steve Omohundro, Gabriel Alfour, Max Tegmark, Kevin McGrew, Gary Marcus, Jaan Tallinn, Eric Schmidt, Yoshua Bengio
2025	A*	`conf`	AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents. ICLR 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	J	`jnl`	Attacking Multimodal OS Agents with Malicious Image Patches. CoRR Lukas Aichberger, Alasdair Paren, Yarin Gal, Philip Torr, Adel Bibi
2025	J	`jnl`	BiasBusters: Uncovering and Mitigating Tool Selection Bias in Large Language Models. CoRR Thierry Blankenstein, Jialin Yu, Zixuan Li, Vassilis Plachouras, Sunando Sengupta, Philip Torr, Yarin Gal, Alasdair Paren, Adel Bibi
2025	J	`jnl`	Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs. CoRR Kyle O'Brien, Stephen Casper, Quentin Anthony, Tomek Korbak, Robert Kirk, Xander Davies, Ishan Mishra, Geoffrey Irving, Yarin Gal, Stella Biderman
2025	A*	`conf`	Detecting LLM Hallucination Through Layer-wise Information Deficiency: Analysis of Ambiguous Prompts and Unanswerable Questions. EMNLP Hazel Kim, Tom A. Lamb, Adel Bibi, Philip Torr, Yarin Gal
2025	J	`jnl`	Do Multilingual LLMs Think In English? CoRR Lisa Schut, Yarin Gal, Sebastian Farquhar
2025	J	`jnl`	Evaluating & Reducing Deceptive Dialogue From Language Models with Multi-turn RL. CoRR Marwa Abdulhai, Ryan Cheng, Aryansh Shrivastava, Natasha Jaques, Yarin Gal, Sergey Levine
2025	J	`jnl`	Existing Large Language Model Unlearning Evaluations Are Inconclusive. CoRR Zhili Feng, Yixuan Even Xu, Alexander Robey, Robert Kirk, Xander Davies, Yarin Gal, Avi Schwarzschild, J. Zico Kolter
2025	J	`jnl`	Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors. Trans. Mach. Learn. Res. Angus Nicolson, Lisa Schut, J. Alison Noble, Yarin Gal
2025	J	`jnl`	FindingDory: A Benchmark to Evaluate Memory in Embodied Agents. CoRR Karmesh Yadav, Yusuf Ali, Gunshi Gupta, Yarin Gal, Zsolt Kira
2025	J	`jnl`	Fundamental Limitations in Defending LLM Finetuning APIs. CoRR Xander Davies, Eric Winsor, Tomek Korbak, Alexandra Souly, Robert Kirk, Christian Schröder de Witt, Yarin Gal
2025	J	`jnl`	Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements. CoRR Shreshth A. Malik, Tiarnan A. S. Doherty, Benjamin Colmey, Stephen J. Roberts, Yarin Gal, Paul A. Midgley
2025	J	`jnl`	Iterative Deployment Improves Planning Skills in LLMs. CoRR Augusto B. Corrêa, Yoav Gelberg, Luckeciano C. Melo, Ilia Shumailov, André Grahl Pereira, Yarin Gal
2025	J	`jnl`	Language Models Change Facts Based on the Way You Talk. CoRR Matthew Kearney, Reuben Binns, Yarin Gal
2025	J	`jnl`	Leveraging Deep Learning for Physical Model Bias of Global Air Quality Estimates. CoRR Kelsey Doerksen, Yuliya Marchetti, Kevin Bowman, Steven Lu, James Montgomery, Yarin Gal, Freddie Kalaitzis, Kazuyuki Miyazaki
2025	J	`jnl`	Measuring what Matters: Construct Validity in Large Language Model Benchmarks. CoRR Andrew M. Bean, Ryan Othniel Kearns, Angelika Romanou, Franziska Sofia Hafner, Harry Mayne, Jan Batzner, Negar Foroutan, Chris Schmitz, Karolina Korgul, Hunar Batra, Oishi Deb, Emma Beharry, Cornelius Emde, Thomas Foster, Anna Gausen, María Grandury, Simeng Han, Valentin Hofmann, Lujain Ibrahim, Hazel Kim, Hannah Rose Kirk, Fangru Lin, Gabrielle Kaili-May Liu, Lennart Luettgau, Jabez Magomere, Jonathan Rystrøm, Anna Sotnikova, Yushi Yang, Yilun Zhao, Adel Bibi, Antoine Bosselut, Ronald Clark, Arman Cohan, Jakob N. Foerster, Yarin Gal, Scott A. Hale, Inioluwa Deborah Raji, Christopher Summerfield, Philip H. S. Torr, Cozmin Ududec, Luc Rocher, Adam Mahdi
2025	J	`jnl`	Memo: Training Memory-Efficient Embodied Agents with Reinforcement Learning. CoRR Gunshi Gupta, Karmesh Yadav, Zsolt Kira, Yarin Gal, Rahaf Aljundi
2025	J	`jnl`	Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities. CoRR Zora Che, Stephen Casper, Robert Kirk, Anirudh Satheesh, Stewart Slocum, Lev E. McKinney, Rohit Gandikota, Aidan Ewart, Domenic Rosati, Zichu Wu, Zikui Cai, Bilal Chughtai, Yarin Gal, Furong Huang, Dylan Hadfield-Menell
2025	J	`jnl`	Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities. Trans. Mach. Learn. Res. Zora Che, Stephen Casper, Robert Kirk, Anirudh Satheesh, Stewart Slocum, Lev E. McKinney, Rohit Gandikota, Aidan Ewart, Domenic Rosati, Zichu Wu, Zikui Cai, Bilal Chughtai, Yarin Gal, Furong Huang, Dylan Hadfield-Menell
2025	J	`jnl`	Open Problems in Machine Unlearning for AI Safety. CoRR Fazl Barez, Tingchen Fu, Ameya Prabhu, Stephen Casper, Amartya Sanyal, Adel Bibi, Aidan O'Gara, Robert Kirk, Ben Bucknall, Timothy Fist, Luke Ong, Philip Torr, Kwok-Yan Lam, Robert Trager, David Krueger, Sören Mindermann, José Hernández-Orallo, Mor Geva, Yarin Gal
2025	J	`jnl`	Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samples. CoRR Alexandra Souly, Javier Rando, Ed Chapman, Xander Davies, Burak Hasircioglu, Ezzeldin Shereen, Carlos Mougan, Vasilios Mavroudis, Erik Jones, Chris Hicks, Nicholas Carlini, Yarin Gal, Robert Kirk
2025	A*	`conf`	Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction. ICML Ruben Weitzman, Peter Mørch Groth, Lood van Niekerk, Aoi Otani, Yarin Gal, Debora Susan Marks, Pascal Notin
2025	J	`jnl`	Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction. CoRR Ruben Weitzman, Peter Mørch Groth, Lood van Niekerk, Aoi Otani, Yarin Gal, Debora S. Marks, Pascal Notin
2025	J	`jnl`	Reasoning Introduces New Poisoning Attacks Yet Makes Them More Complicated. CoRR Hanna Foerster, Ilia Shumailov, Yiren Zhao, Harsh Chaudhari, Jamie Hayes, Robert Mullins, Yarin Gal
2025	J	`jnl`	RedCodeAgent: Automatic Red-teaming Agent against Diverse Code Agents. CoRR Chengquan Guo, Chulin Xie, Yu Yang, Zhaorun Chen, Zinan Lin, Xander Davies, Yarin Gal, Dawn Song, Bo Li
2025	J	`jnl`	Reducing Large Language Model Safety Risks in Women's Health using Semantic Entropy. CoRR Jahan C. Penny-Dimri, Magdalena Bachmann, William R. Cooke, Sam Mathewlynn, Samuel Dockree, John Tolladay, Jannik Kossen, Lin Li, Yarin Gal, Gabriel Davis Jones
2025	J	`jnl`	Scaling Up Active Testing to Large Language Models. CoRR Gabrielle Berrada, Jannik Kossen, Muhammed Razzak, Freddie Bickford Smith, Yarin Gal, Tom Rainforth
2025	J	`jnl`	Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition. CoRR Andy Zou, Maxwell Lin, Eliot Krzysztof Jones, Micha Nowak, Mateusz Dziemian, Nick Winter, Alexander Grattan, Valent Nathanael, Ayla Croft, Xander Davies, Jai Patel, Robert Kirk, Nate Burnikell, Yarin Gal, Dan Hendrycks, J. Zico Kolter, Matt Fredrikson
2025	—	`conf`	Simple Factuality Probes Detect Hallucinations in Long-Form Natural Language Generation. EMNLP (Findings) Jiatong Han, Neil Band, Muhammed Razzak, Jannik Kossen, Tim G. J. Rudner, Yarin Gal
2025	J	`jnl`	Stabilizing Policy Gradients for Sample-Efficient Reinforcement Learning in LLM Reasoning. CoRR Luckeciano C. Melo, Alessandro Abate, Yarin Gal
2025	J	`jnl`	The human factor in explainable artificial intelligence: clinician variability in trust, reliance, and performance. npj Digit. Medicine Angus Nicolson, Elizabeth Bradburn, Yarin Gal, Aris T. Papageorghiou, J. Alison Noble
2025	J	`jnl`	ToolTweak: An Attack on Tool Selection in LLM-based Agents. CoRR Jonathan Sneh, Ruomei Yan, Jialin Yu, Philip Torr, Yarin Gal, Sunando Sengupta, Eric Sommerlade, Alasdair Paren, Adel Bibi
2025	J	`jnl`	Uncertainty Quantification for Surface Ozone Emulators using Deep Learning. CoRR Kelsey Doerksen, Yuliya Marchetti, Steven Lu, Kevin Bowman, James Montgomery, Kazuyuki Miyazaki, Yarin Gal, Freddie Kalaitzis
2025	J	`jnl`	Uncertainty-Aware Step-wise Verification with Generative Reward Models. CoRR Zihuiwen Ye, Luckeciano Carvalho Melo, Younesse Kaddar, Phil Blunsom, Sam Staton, Yarin Gal
2025	J	`jnl`	Weighted Conditional Flow Matching. CoRR Sergio Calvo-Ordoñez, Matthieu Meunier, Álvaro Cartea, Christoph Reisinger, Yarin Gal, José Miguel Hernández-Lobato
2024	J	`jnl`	AI models collapse when trained on recursively generated data. Nat. Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross J. Anderson, Yarin Gal
2024	J	`jnl`	AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents. CoRR 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
2024	J	`jnl`	Bayesian Preference Elicitation with Language Models. CoRR Kunal Handa, Yarin Gal, Ellie Pavlick, Noah D. Goodman, Jacob Andreas, Alex Tamkin, Belinda Z. Li
2024	A*	`conf`	Challenges and Considerations in the Evaluation of Bayesian Causal Discovery. ICML Amir Mohammad Karimi-Mamaghan, Panagiotis Tigas, Karl Henrik Johansson, Yarin Gal, Yashas Annadani, Stefan Bauer
2024	J	`jnl`	Challenges and Considerations in the Evaluation of Bayesian Causal Discovery. CoRR Amir Mohammad Karimi-Mamaghan, Panagiotis Tigas, Karl Henrik Johansson, Yarin Gal, Yashas Annadani, Stefan Bauer
2024	A*	`conf`	Deep Bayesian Active Learning for Preference Modeling in Large Language Models. NeurIPS Luckeciano Carvalho Melo, Panagiotis Tigas, Alessandro Abate, Yarin Gal
2024	J	`jnl`	Deep Bayesian Active Learning for Preference Modeling in Large Language Models. CoRR Luckeciano C. Melo, Panagiotis Tigas, Alessandro Abate, Yarin Gal
2024	J	`jnl`	Detecting LLM Hallucination Through Layer-wise Information Deficiency: Analysis of Unanswerable Questions and Ambiguous Prompts. CoRR Hazel Kim, Adel Bibi, Philip Torr, Yarin Gal
2024	J	`jnl`	Detecting hallucinations in large language models using semantic entropy. Nat. Sebastian Farquhar, Jannik Kossen, Lorenz Kuhn, Yarin Gal
2024	A*	`conf`	Estimating the Hallucination Rate of Generative AI. NeurIPS Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. Cunningham, David M. Blei
2024	J	`jnl`	Estimating the Hallucination Rate of Generative AI. CoRR Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. Cunningham, David M. Blei
2024	J	`jnl`	Explaining Explainability: Understanding Concept Activation Vectors. CoRR Angus Nicolson, Lisa Schut, J. Alison Noble, Yarin Gal
2024	J	`jnl`	Fine-Tuning Large Language Models to Appropriately Abstain with Semantic Entropy. CoRR Benedict Aaron Tjandra, Muhammed Razzak, Jannik Kossen, Kunal Handa, Yarin Gal
2024	J	`jnl`	Fine-tuning can cripple your foundation model; preserving features may be the solution. Trans. Mach. Learn. Res. Jishnu Mukhoti, Yarin Gal, Philip Torr, Puneet K. Dokania
2024	J	`jnl`	Generating Physically-Consistent Satellite Imagery for Climate Visualizations. IEEE Trans. Geosci. Remote. Sens. Björn Lütjens, Brandon Leshchinskiy, Océane Boulais, Farrukh Chishtie, Natalia Díaz Rodríguez, Margaux Masson-Forsythe, Ana Mata-Payerro, Christian Requena-Mesa, Aruna Sankaranarayanan, Aaron Piña, Yarin Gal, Chedy Raïssi, Alexander Lavin, Dava J. Newman
2024	A*	`conf`	How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions. ICLR Lorenzo Pacchiardi, Alex James Chan, Sören Mindermann, Ilan Moscovitz, Alexa Y. Pan, Yarin Gal, Owain Evans, Jan Markus Brauner
2024	A*	`conf`	In-Context Learning Learns Label Relationships but Is Not Conventional Learning. ICLR Jannik Kossen, Yarin Gal, Tom Rainforth
2024	A*	`conf`	Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities. NeurIPS Alexander Nikitin, Jannik Kossen, Yarin Gal, Pekka Marttinen
2024	J	`jnl`	Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities. CoRR Alexander Nikitin, Jannik Kossen, Yarin Gal, Pekka Marttinen
2024	A*	`conf`	Position: Fundamental Limitations of LLM Censorship Necessitate New Approaches. ICML David Glukhov, Ilia Shumailov, Yarin Gal, Nicolas Papernot, Vardan Papyan
2024	A*	`conf`	Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control. NeurIPS Gunshi Gupta, Karmesh Yadav, Yarin Gal, Dhruv Batra, Zsolt Kira, Cong Lu, Tim G. J. Rudner
2024	J	`jnl`	Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control. CoRR Gunshi Gupta, Karmesh Yadav, Yarin Gal, Dhruv Batra, Zsolt Kira, Cong Lu, Tim G. J. Rudner
2024	A*	`conf`	ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages. ICML Andrew Jesson, Chris Lu, Gunshi Gupta, Nicolas Beltran-Velez, Angelos Filos, Jakob Nicolaus Foerster, Yarin Gal
2024	J	`jnl`	Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs. CoRR Jannik Kossen, Jiatong Han, Muhammed Razzak, Lisa Schut, Shreshth A. Malik, Yarin Gal
2024	J	`jnl`	Temporal-Difference Variational Continual Learning. CoRR Luckeciano C. Melo, Alessandro Abate, Yarin Gal
2024	—	`conf`	TextCAVs: Debugging Vision Models Using Text. ISIC/iMIMIC/EARTH/DeCaF@MICCAI Angus Nicolson, Yarin Gal, J. Alison Noble
2024	J	`jnl`	TextCAVs: Debugging vision models using text. CoRR Angus Nicolson, Yarin Gal, J. Alison Noble
2024	J	`jnl`	The Benefits and Risks of Transductive Approaches for AI Fairness. CoRR Muhammed Razzak, Andreas Kirsch, Yarin Gal
2024	—	`conf`	Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks. GRaM Yoav Gelberg, Tycho F. A. van der Ouderaa, Mark van der Wilk, Yarin Gal
2024	J	`jnl`	Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks. CoRR Yoav Gelberg, Tycho F. A. van der Ouderaa, Mark van der Wilk, Yarin Gal
2023	J	`jnl`	BatchGFN: Generative Flow Networks for Batch Active Learning. CoRR Shreshth A. Malik, Salem Lahlou, Andrew Jesson, Moksh Jain, Nikolay Malkin, Tristan Deleu, Yoshua Bengio, Yarin Gal
2023	—	`conf`	Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? CLeaR Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal
2023	J	`jnl`	Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? CoRR Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal
2023	J	`jnl`	Continual Learning via Sequential Function-Space Variational Inference. CoRR Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal
2023	A*	`conf`	Deep Deterministic Uncertainty: A New Simple Baseline. CVPR Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal
2023	A*	`conf`	Differentiable Multi-Target Causal Bayesian Experimental Design. ICML Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer
2023	J	`jnl`	Differentiable Multi-Target Causal Bayesian Experimental Design. CoRR Yashas Annadani, Panagiotis Tigas, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer
2023	J	`jnl`	DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design. CoRR Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab
2023	A*	`conf`	DiscoBAX: Discovery of optimal intervention sets in genomic experiment design. ICML Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab
2023	J	`jnl`	Fine-tuning can cripple your foundation model; preserving features may be the solution. CoRR Jishnu Mukhoti, Yarin Gal, Philip H. S. Torr, Puneet K. Dokania
2023	J	`jnl`	Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirements. CoRR Peter A. Zachares, Vahan Hovhannisyan, Alan Mosca, Yarin Gal
2023	J	`jnl`	How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions. CoRR Lorenzo Pacchiardi, Alex J. Chan, Sören Mindermann, Ilan Moscovitz, Alexa Y. Pan, Yarin Gal, Owain Evans, Jan Brauner
2023	J	`jnl`	In-Context Learning in Large Language Models Learns Label Relationships but Is Not Conventional Learning. CoRR Jannik Kossen, Tom Rainforth, Yarin Gal
2023	J	`jnl`	LLM Censorship: A Machine Learning Challenge or a Computer Security Problem? CoRR David Glukhov, Ilia Shumailov, Yarin Gal, Nicolas Papernot, Vardan Papyan
2023	C	`conf`	Precipitation-Triggered Landslide Prediction in Nepal Using Machine Learning and Deep Learning. IGARSS Kelsey Doerksen, Yarin Gal, Freddie Kalaitzis, Cristian Rossi, David Petit, Sihan Li, Simon J. Dadson
2023	A	`conf`	Prediction-Oriented Bayesian Active Learning. AISTATS Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth
2023	J	`jnl`	Prediction-Oriented Bayesian Active Learning. CoRR Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth
2023	A*	`conf`	ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design. NeurIPS 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
2023	A*	`conf`	ProteinNPT: Improving protein property prediction and design with non-parametric transformers. NeurIPS Pascal Notin, Ruben Weitzman, Debora S. Marks, Yarin Gal
2023	J	`jnl`	ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages. CoRR Andrew Jesson, Chris Lu, Gunshi Gupta, Angelos Filos, Jakob Nicolaus Foerster, Yarin Gal
2023	—	`conf`	Revisiting Automated Prompting: Are We Actually Doing Better? ACL (2) Yulin Zhou, Yiren Zhao, Ilia Shumailov, Robert Mullins, Yarin Gal
2023	J	`jnl`	Revisiting Automated Prompting: Are We Actually Doing Better? CoRR Yulin Zhou, Yiren Zhao, Ilia Shumailov, Robert Mullins, Yarin Gal
2023	A*	`conf`	Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation. ICLR Lorenz Kuhn, Yarin Gal, Sebastian Farquhar
2023	J	`jnl`	Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation. CoRR Lorenz Kuhn, Yarin Gal, Sebastian Farquhar
2023	J	`jnl`	Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning. Trans. Mach. Learn. Res. Andreas Kirsch, Sebastian Farquhar, Parmida Atighehchian, Andrew Jesson, Frédéric Branchaud-Charron, Yarin Gal
2023	J	`jnl`	The Curse of Recursion: Training on Generated Data Makes Models Forget. CoRR Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, Ross J. Anderson
2023	J	`jnl`	Tractable Function-Space Variational Inference in Bayesian Neural Networks. CoRR Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal
2023	J	`jnl`	Using uncertainty-aware machine learning models to study aerosol-cloud interactions. CoRR Maëlys Solal, Andrew Jesson, Yarin Gal, Alyson Douglas
2022	J	`jnl`	A Note on "Assessing Generalization of SGD via Disagreement". CoRR Andreas Kirsch, Yarin Gal
2022	J	`jnl`	A Note on "Assessing Generalization of SGD via Disagreement". Trans. Mach. Learn. Res. Andreas Kirsch, Yarin Gal
2022	A*	`conf`	Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation. NeurIPS Jannik Kossen, Sebastian Farquhar, Yarin Gal, Thomas Rainforth
2022	J	`jnl`	Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation. CoRR Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth
2022	J	`jnl`	Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. CoRR Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W. Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal
2022	J	`jnl`	CLAM: Selective Clarification for Ambiguous Questions with Large Language Models. CoRR Lorenz Kuhn, Yarin Gal, Sebastian Farquhar
2022	A*	`conf`	Continual Learning via Sequential Function-Space Variational Inference. ICML Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal
2022	J	`jnl`	Discovering Long-period Exoplanets using Deep Learning with Citizen Science Labels. CoRR Shreshth A. Malik, Nora L. Eisner, Chris J. Lintott, Yarin Gal
2022	J	`jnl`	Exploring Low Rank Training of Deep Neural Networks. CoRR Siddhartha Rao Kamalakara, Acyr Locatelli, Bharat Venkitesh, Jimmy Ba, Yarin Gal, Aidan N. Gomez
2022	J	`jnl`	Exploring the Limits of Synthetic Creation of Solar EUV Images via Image-to-Image Translation. CoRR Valentina Salvatelli, Luiz F. G. dos Santos, Souvik Bose, Brad Neuberg, Mark C. M. Cheung, Miho Janvier, Meng Jin, Yarin Gal, Atilim Gunes Baydin
2022	A*	`conf`	GeneDisco: A Benchmark for Experimental Design in Drug Discovery. ICLR Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab
2022	J	`jnl`	Global geomagnetic perturbation forecasting using Deep Learning. CoRR Vishal Upendran, Panagiotis Tigas, Banafsheh Ferdousi, Téo Bloch, Mark C. M. Cheung, Siddha Ganju, Asti Bhatt, Ryan M. McGranaghan, Yarin Gal
2022	J	`jnl`	Interlocking Backpropagation: Improving depthwise model-parallelism. J. Mach. Learn. Res. Aidan N. Gomez, Oscar Key, Kuba Perlin, Stephen Gou, Nick Frosst, Jeff Dean, Yarin Gal
2022	A*	`conf`	Interventions, Where and How? Experimental Design for Causal Models at Scale. NeurIPS Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer
2022	J	`jnl`	Interventions, Where and How? Experimental Design for Causal Models at Scale. CoRR Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer
2022	A*	`conf`	KL Guided Domain Adaptation. ICLR A. Tuan Nguyen, Toan Tran, Yarin Gal, Philip H. S. Torr, Atilim Gunes Baydin
2022	A*	`conf`	Learning Dynamics and Generalization in Deep Reinforcement Learning. ICML Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal
2022	J	`jnl`	Learning Dynamics and Generalization in Reinforcement Learning. CoRR Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal
2022	J	`jnl`	Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling. CoRR Andreas Kirsch, Jannik Kossen, Yarin Gal
2022	J	`jnl`	Mixtures of large-scale dynamic functional brain network modes. NeuroImage 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
2022	J	`jnl`	On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations. CoRR Tim G. J. Rudner, Cong Lu, Michael A. Osborne, Yarin Gal, Yee Whye Teh
2022	J	`jnl`	Plex: Towards Reliability using Pretrained Large Model Extensions. CoRR 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
2022	J	`jnl`	Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt. CoRR Sören Mindermann, Jan Markus Brauner, Muhammed Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N. Gomez, Adrien Morisot, Sebastian Farquhar, Yarin Gal
2022	A*	`conf`	Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt. ICML Sören Mindermann, Jan Markus Brauner, Muhammed Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N. Gomez, Adrien Morisot, Sebastian Farquhar, Yarin Gal
2022	J	`jnl`	Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference. IEEE Trans. Medical Imaging Raghav Mehta, Thomas Christinck, Tanya Nair, Aurélie Bussy, Swapna Premasiri, Manuela Costantino, M. Mallar Chakravarthy, Douglas L. Arnold, Yarin Gal, Tal Arbel
2022	A*	`conf`	Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients. ICLR Milad Alizadeh, Shyam A. Tailor, Luisa M. Zintgraf, Joost van Amersfoort, Sebastian Farquhar, Nicholas Donald Lane, Yarin Gal
2022	J	`jnl`	Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients. CoRR Milad Alizadeh, Shyam A. Tailor, Luisa M. Zintgraf, Joost van Amersfoort, Sebastian Farquhar, Nicholas Donald Lane, Yarin Gal
2022	A*	`conf`	Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions. NeurIPS Andrew Jesson, Alyson Douglas, Peter Manshausen, Maëlys Solal, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit
2022	J	`jnl`	Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions. CoRR Andrew Jesson, Alyson Douglas, Peter Manshausen, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit
2022	A*	`conf`	Tractable Function-Space Variational Inference in Bayesian Neural Networks. NeurIPS Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal
2022	A*	`conf`	Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena-Hurtado, Aidan N. Gomez, Debora S. Marks, Yarin Gal
2022	J	`jnl`	Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval. CoRR Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena-Hurtado, Aidan N. Gomez, Debora S. Marks, Yarin Gal
2022	J	`jnl`	Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities. Trans. Mach. Learn. Res. Andreas Kirsch, Yarin Gal
2022	J	`jnl`	Unifying Approaches in Data Subset Selection via Fisher Information and Information-Theoretic Quantities. CoRR Andreas Kirsch, Yarin Gal
2021	J	`jnl`	A Practical & Unified Notation for Information-Theoretic Quantities in ML. CoRR Andreas Kirsch, Yarin Gal
2021	J	`jnl`	A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions. CoRR Andreas Kirsch, Sebastian Farquhar, Yarin Gal
2021	J	`jnl`	Active Learning under Pool Set Distribution Shift and Noisy Data. CoRR Andreas Kirsch, Tom Rainforth, Yarin Gal
2021	A*	`conf`	Active Testing: Sample-Efficient Model Evaluation. ICML Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth
2021	J	`jnl`	Active Testing: Sample-Efficient Model Evaluation. CoRR Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth
2021	—	`conf`	Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. NeurIPS Datasets and Benchmarks Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Mike Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal
2021	J	`jnl`	Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective. CoRR Lewis Smith, Joost van Amersfoort, Haiwen Huang, Stephen J. Roberts, Yarin Gal
2021	A*	`conf`	Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. NeurIPS Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
2021	J	`jnl`	Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. CoRR Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
2021	J	`jnl`	Contrastive Representation Learning with Trainable Augmentation Channel. CoRR Masanori Koyama, Kentaro Minami, Takeru Miyato, Yarin Gal
2021	J	`jnl`	DARTS without a Validation Set: Optimizing the Marginal Likelihood. CoRR Miroslav Fil, Binxin Ru, Clare Lyle, Yarin Gal
2021	J	`jnl`	DeDUCE: Generating Counterfactual Explanations Efficiently. CoRR Benedikt Höltgen, Lisa Schut, Jan Markus Brauner, Yarin Gal
2021	J	`jnl`	Decomposing Representations for Deterministic Uncertainty Estimation. CoRR Haiwen Huang, Joost van Amersfoort, Yarin Gal
2021	J	`jnl`	Deep Deterministic Uncertainty for Semantic Segmentation. CoRR Jishnu Mukhoti, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal
2021	J	`jnl`	Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty. CoRR Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal
2021	A*	`conf`	Domain Invariant Representation Learning with Domain Density Transformations. NeurIPS A. Tuan Nguyen, Toan Tran, Yarin Gal, Atilim Gunes Baydin
2021	J	`jnl`	Domain Invariant Representation Learning with Domain Density Transformations. CoRR A. Tuan Nguyen, Toan Tran, Yarin Gal, Atilim Günes Baydin
2021	—	`conf`	Evaluating Approximate Inference in Bayesian Deep Learning. NeurIPS (Competition and Demos) 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
2021	J	`jnl`	GeneDisco: A Benchmark for Experimental Design in Drug Discovery. CoRR Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab
2021	A	`conf`	Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties. AISTATS Lisa Schut, Oscar Key, Rory McGrath, Luca Costabello, Bogdan Sacaleanu, Medb Corcoran, Yarin Gal
2021	J	`jnl`	Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties. CoRR Lisa Schut, Oscar Key, Rory McGrath, Luca Costabello, Bogdan Sacaleanu, Medb Corcoran, Yarin Gal
2021	J	`jnl`	Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition. CoRR Panagiotis Tigas, Téo Bloch, Vishal Upendran, Banafsheh Ferdoushi, Mark C. M. Cheung, Siddha Ganju, Ryan M. McGranaghan, Yarin Gal, Asti Bhatt
2021	J	`jnl`	Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression. CoRR Joost van Amersfoort, Lewis Smith, Andrew Jesson, Oscar Key, Yarin Gal
2021	A*	`conf`	Improving black-box optimization in VAE latent space using decoder uncertainty. NeurIPS Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal
2021	J	`jnl`	Improving black-box optimization in VAE latent space using decoder uncertainty. CoRR Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal
2021	J	`jnl`	KL Guided Domain Adaptation. CoRR A. Tuan Nguyen, Toan Tran, Yarin Gal, Philip H. S. Torr, Atilim Günes Baydin
2021	A*	`conf`	Learning Invariant Representations for Reinforcement Learning without Reconstruction. ICLR Amy Zhang, Rowan Thomas McAllister, Roberto Calandra, Yarin Gal, Sergey Levine
2021	J	`jnl`	Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation. CoRR Muhammed Razzak, Gonzalo Mateo-Garcia, Luis Gómez-Chova, Yarin Gal, Freddie Kalaitzis
2021	A*	`conf`	On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations. NeurIPS Tim G. J. Rudner, Cong Lu, Michael A. Osborne, Yarin Gal, Yee Whye Teh
2021	A*	`conf`	On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. ICML Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth
2021	J	`jnl`	On Statistical Bias In Active Learning: How and When To Fix It. CoRR Sebastian Farquhar, Yarin Gal, Tom Rainforth
2021	A*	`conf`	On Statistical Bias In Active Learning: How and When to Fix It. ICLR Sebastian Farquhar, Yarin Gal, Tom Rainforth
2021	A*	`conf`	Outcome-Driven Reinforcement Learning via Variational Inference. NeurIPS Tim G. J. Rudner, Vitchyr Pong, Rowan McAllister, Yarin Gal, Sergey Levine
2021	J	`jnl`	Outcome-Driven Reinforcement Learning via Variational Inference. CoRR Tim G. J. Rudner, Vitchyr H. Pong, Rowan McAllister, Yarin Gal, Sergey Levine
2021	J	`jnl`	Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization. CoRR Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa, Farrukh Chishtie, Natalia Díaz Rodríguez, Océane Boulais, Aruna Sankaranarayanan, Aaron Piña, Yarin Gal, Chedy Raïssi, Alexander Lavin, Dava J. Newman
2021	J	`jnl`	Prioritized training on points that are learnable, worth learning, and not yet learned. CoRR Sören Mindermann, Muhammed Razzak, Winnie Xu, Andreas Kirsch, Mrinank Sharma, Adrien Morisot, Aidan N. Gomez, Sebastian Farquhar, Jan Markus Brauner, Yarin Gal
2021	A*	`conf`	PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning. ICML Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar
2021	J	`jnl`	PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning. CoRR Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar
2021	J	`jnl`	QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results. CoRR 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
2021	A*	`conf`	Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. ICML Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit
2021	J	`jnl`	Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. CoRR Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit
2021	J	`jnl`	Quantifying Uncertainty for Machine Learning Based Diagnostic. CoRR Owen Convery, Lewis Smith, Yarin Gal, Adi Hanuka
2021	J	`jnl`	Robustness to Pruning Predicts Generalization in Deep Neural Networks. CoRR Lorenz Kuhn, Clare Lyle, Aidan N. Gomez, Jonas Rothfuss, Yarin Gal
2021	A*	`conf`	Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning. NeurIPS Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Thomas Rainforth, Yarin Gal
2021	J	`jnl`	Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning. CoRR Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Tom Rainforth, Yarin Gal
2021	—	`conf`	Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks. NeurIPS Datasets and Benchmarks 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
2021	J	`jnl`	Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks. CoRR 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
2021	A*	`conf`	Speedy Performance Estimation for Neural Architecture Search. NeurIPS Robin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal
2021	J	`jnl`	Technology Readiness Levels for Machine Learning Systems. CoRR Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic, Siddha Ganju, Dava J. Newman, Sujoy Ganguly, Danny Lange, Atilim Günes Baydin, Amit Sharma, Adam Gibson, Yarin Gal, Eric P. Xing, Chris Mattmann, James Parr
2021	J	`jnl`	Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR 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
2021	J	`jnl`	Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific. CoRR Andrew Jesson, Peter Manshausen, Alyson Douglas, Duncan Watson-Parris, Yarin Gal, Philip Stier
2021	J	`jnl`	VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning. J. Mach. Learn. Res. Luisa M. Zintgraf, Sebastian Schulze, Cong Lu, Leo Feng, Maximilian Igl, Kyriacos Shiarlis, Yarin Gal, Katja Hofmann, Shimon Whiteson
2020	A*	`conf`	A Bayesian Perspective on Training Speed and Model Selection. NeurIPS Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk
2020	J	`jnl`	A Bayesian Perspective on Training Speed and Model Selection. CoRR Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk
2020	J	`jnl`	Baryons from Mesons: A Machine Learning Perspective. CoRR Yarin Gal, Vishnu Jejjala, Damian Kaloni Mayorga Pena, Challenger Mishra
2020	A*	`conf`	BayesOpt Adversarial Attack. ICLR Binxin Ru, Adam D. Cobb, Arno Blaas, Yarin Gal
2020	A*	`conf`	Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? ICML Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal
2020	J	`jnl`	Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? CoRR Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal
2020	J	`jnl`	Capsule Networks - A Probabilistic Perspective. CoRR Lewis Smith, Lisa Schut, Yarin Gal, Mark van der Wilk
2020	J	`jnl`	Conditional BRUNO: A neural process for exchangeable labelled data. Neurocomputing Iryna Korshunova, Yarin Gal, Arthur Gretton, Joni Dambre
2020	A*	`conf`	How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19? NeurIPS Mrinank Sharma, Sören Mindermann, Jan Markus Brauner, Gavin Leech, Anna B. Stephenson, Tomas Gavenciak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal
2020	J	`jnl`	Identifying Causal Effect Inference Failure with Uncertainty-Aware Models. CoRR Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal
2020	A*	`conf`	Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models. NeurIPS Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal
2020	A*	`conf`	Inter-domain Deep Gaussian Processes. ICML Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal
2020	J	`jnl`	Inter-domain Deep Gaussian Processes. CoRR Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal
2020	J	`jnl`	Interlocking Backpropagation: Improving depthwise model-parallelism. CoRR Aidan N. Gomez, Oscar Key, Stephen Gou, Nick Frosst, Jeff Dean, Yarin Gal
2020	A*	`conf`	Invariant Causal Prediction for Block MDPs. ICML Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup
2020	J	`jnl`	Invariant Causal Prediction for Block MDPs. CoRR Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup
2020	J	`jnl`	Learning Invariant Representations for Reinforcement Learning without Reconstruction. CoRR Amy Zhang, Rowan McAllister, Roberto Calandra, Yarin Gal, Sergey Levine
2020	A*	`conf`	Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations. NeurIPS Sebastian Farquhar, Lewis Smith, Yarin Gal
2020	C	`conf`	Model and Data Uncertainty for Satellite Time Series Forecasting with Deep Recurrent Models. IGARSS Marc Rußwurm, Mohsin Ali, Xiaoxiang Zhu, Yarin Gal, Marco Körner
2020	J	`jnl`	Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning. CoRR 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
2020	J	`jnl`	On Batch Normalisation for Approximate Bayesian Inference. CoRR Jishnu Mukhoti, Puneet K. Dokania, Philip H. S. Torr, Yarin Gal
2020	J	`jnl`	On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. CoRR Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth
2020	J	`jnl`	On the Benefits of Invariance in Neural Networks. CoRR Clare Lyle, Mark van der Wilk, Marta Kwiatkowska, Yarin Gal, Benjamin Bloem-Reddy
2020	J	`jnl`	On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission. CoRR Mrinank Sharma, Sören Mindermann, Jan Markus Brauner, Gavin Leech, Anna B. Stephenson, Tomas Gavenciak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal
2020	J	`jnl`	Physics-informed GANs for Coastal Flood Visualization. CoRR Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa, Farrukh Chishtie, Natalia Díaz Rodríguez, Océane Boulais, Aaron Piña, Dava J. Newman, Alexander Lavin, Yarin Gal, Chedy Raïssi
2020	A	`conf`	Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning. AISTATS Sebastian Farquhar, Michael A. Osborne, Yarin Gal
2020	J	`jnl`	Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search. CoRR Binxin Ru, Clare Lyle, Lisa Schut, Mark van der Wilk, Yarin Gal
2020	J	`jnl`	Semi-supervised Learning of Galaxy Morphology using Equivariant Transformer Variational Autoencoders. CoRR Mizu Nishikawa-Toomey, Lewis Smith, Yarin Gal
2020	J	`jnl`	Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network. CoRR Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal
2020	J	`jnl`	Single Shot Structured Pruning Before Training. CoRR Joost van Amersfoort, Milad Alizadeh, Sebastian Farquhar, Nicholas D. Lane, Yarin Gal
2020	J	`jnl`	SliceOut: Training Transformers and CNNs faster while using less memory. CoRR Pascal Notin, Aidan N. Gomez, Joanna Yoo, Yarin Gal
2020	J	`jnl`	Try Depth Instead of Weight Correlations: Mean-field is a Less Restrictive Assumption for Deeper Networks. CoRR Sebastian Farquhar, Lewis Smith, Yarin Gal
2020	A*	`conf`	Uncertainty Estimation Using a Single Deep Deterministic Neural Network. ICML Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal
2020	J	`jnl`	Uncertainty Evaluation Metric for Brain Tumour Segmentation. CoRR Raghav Mehta, Angelos Filos, Yarin Gal, Tal Arbel
2020	A*	`conf`	Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control. ICRA Rhiannon Michelmore, Matthew Wicker, Luca Laurenti, Luca Cardelli, Yarin Gal, Marta Kwiatkowska
2020	J	`jnl`	Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning. CoRR Andreas Kirsch, Clare Lyle, Yarin Gal
2020	A*	`conf`	VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning. ICLR Luisa M. Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, Shimon Whiteson
2020	J	`jnl`	Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers. CoRR Tim Z. Xiao, Aidan N. Gomez, Yarin Gal
2019	J	`jnl`	A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks. CoRR Angelos Filos, Sebastian Farquhar, Aidan N. Gomez, Tim G. J. Rudner, Zachary Kenton, Lewis Smith, Milad Alizadeh, Arnoud de Kroon, Yarin Gal
2019	J	`jnl`	A Unifying Bayesian View of Continual Learning. CoRR Sebastian Farquhar, Yarin Gal
2019	J	`jnl`	Adversarial recovery of agent rewards from latent spaces of the limit order book. CoRR Jacobo Roa-Vicens, Yuanbo Wang, Virgile Mison, Yarin Gal, Ricardo Silva
2019	—	`conf`	An Empirical study of Binary Neural Networks' Optimisation. ICLR (Poster) Milad Alizadeh, Javier Fernández-Marqués, Nicholas D. Lane, Yarin Gal
2019	J	`jnl`	An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval. CoRR 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
2019	J	`jnl`	Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning. CoRR Brad Neuberg, Souvik Bose, Valentina Salvatelli, Luiz F. G. dos Santos, Mark C. M. Cheung, Miho Janvier, Atilim Gunes Baydin, Yarin Gal, Meng Jin
2019	A*	`conf`	BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning. NeurIPS Andreas Kirsch, Joost van Amersfoort, Yarin Gal
2019	J	`jnl`	BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning. CoRR Andreas Kirsch, Joost van Amersfoort, Yarin Gal
2019	B	`conf`	Conditional BRUNO: a neural process for exchangeable labelled data. ESANN Iryna Korshunova, Yarin Gal, Arthur Gretton, Joni Dambre
2019	J	`jnl`	Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder. CoRR Kara Lamb, Garima Malhotra, Athanasios Vlontzos, Edward Wagstaff, Atilim Günes Baydin, Anahita Bhiwandiwalla, Yarin Gal, Alfredo Kalaitzis, Anthony Reina, Asti Bhatt
2019	J	`jnl`	Differentially Private Continual Learning. CoRR Sebastian Farquhar, Yarin Gal
2019	J	`jnl`	Flood Detection On Low Cost Orbital Hardware. CoRR Gonzalo Mateo-Garcia, Silviu Oprea, Lewis Smith, Josh Veitch-Michaelis, Guy Schumann, Yarin Gal, Atilim Günes Baydin, Dietmar Backes
2019	J	`jnl`	Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning. CoRR 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
2019	J	`jnl`	Generalizing from a few environments in safety-critical reinforcement learning. CoRR Zachary Kenton, Angelos Filos, Owain Evans, Yarin Gal
2019	J	`jnl`	Learning Sparse Networks Using Targeted Dropout. CoRR Aidan N. Gomez, Ivan Zhang, Kevin Swersky, Yarin Gal, Geoffrey E. Hinton
2019	J	`jnl`	Machine Learning for Generalizable Prediction of Flood Susceptibility. CoRR Chelsea Sidrane, Dylan J. Fitzpatrick, Andrew Annex, Diane O'Donoghue, Yarin Gal, Piotr Bilinski
2019	J	`jnl`	Prediction of GNSS Phase Scintillations: A Machine Learning Approach. CoRR Kara Lamb, Garima Malhotra, Athanasios Vlontzos, Edward Wagstaff, Atilim Günes Baydin, Anahita Bhiwandiwalla, Yarin Gal, Alfredo Kalaitzis, Anthony Reina, Asti Bhatt
2019	J	`jnl`	Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties. CoRR Xavier Gitiaux, Shane A. Maloney, Anna Jungbluth, Carl Shneider, Paul J. Wright, Atilim Günes Baydin, Michel Deudon, Yarin Gal, Alfredo Kalaitzis, Andrés Muñoz-Jaramillo
2019	J	`jnl`	Radial Bayesian Neural Networks: Robust Variational Inference In Big Models. CoRR Sebastian Farquhar, Michael A. Osborne, Yarin Gal
2019	J	`jnl`	Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses. CoRR Anna Jungbluth, Xavier Gitiaux, Shane A. Maloney, Carl Shneider, Paul J. Wright, Alfredo Kalaitzis, Michel Deudon, Atilim Günes Baydin, Yarin Gal, Andrés Muñoz-Jaramillo
2019	J	`jnl`	Towards Inverse Reinforcement Learning for Limit Order Book Dynamics. CoRR Jacobo Roa-Vicens, Cyrine Chtourou, Angelos Filos, Francisco Rullan, Yarin Gal, Ricardo Silva
2019	J	`jnl`	Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control. CoRR Rhiannon Michelmore, Matthew Wicker, Luca Laurenti, Luca Cardelli, Yarin Gal, Marta Kwiatkowska
2019	J	`jnl`	Using U-Nets to Create High-Fidelity Virtual Observations of the Solar Corona. CoRR Valentina Salvatelli, Souvik Bose, Brad Neuberg, Luiz F. G. dos Santos, Mark C. M. Cheung, Miho Janvier, Atilim Gunes Baydin, Yarin Gal, Meng Jin
2019	J	`jnl`	VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning. CoRR Luisa M. Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, Shimon Whiteson
2018	A*	`conf`	BRUNO: A Deep Recurrent Model for Exchangeable Data. NeurIPS Iryna Korshunova, Jonas Degrave, Ferenc Huszar, Yarin Gal, Arthur Gretton, Joni Dambre
2018	J	`jnl`	Evaluating Bayesian Deep Learning Methods for Semantic Segmentation. CoRR Jishnu Mukhoti, Yarin Gal
2018	J	`jnl`	Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control. CoRR Rhiannon Michelmore, Marta Kwiatkowska, Yarin Gal
2018	A*	`conf`	Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. ICML Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava
2018	J	`jnl`	Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. CoRR Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava
2018	J	`jnl`	Idealised Bayesian Neural Networks Cannot Have Adversarial Examples: Theoretical and Empirical Study. CoRR Yarin Gal, Lewis Smith
2018	J	`jnl`	Loss-Calibrated Approximate Inference in Bayesian Neural Networks. CoRR Adam D. Cobb, Stephen J. Roberts, Yarin Gal
2018	A*	`conf`	Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics. CVPR Alex Kendall, Yarin Gal, Roberto Cipolla
2018	J	`jnl`	On the Importance of Strong Baselines in Bayesian Deep Learning. CoRR Jishnu Mukhoti, Pontus Stenetorp, Yarin Gal
2018	J	`jnl`	Towards Robust Evaluations of Continual Learning. CoRR Sebastian Farquhar, Yarin Gal
2018	A	`conf`	Understanding Measures of Uncertainty for Adversarial Example Detection. UAI Lewis Smith, Yarin Gal
2018	J	`jnl`	Understanding Measures of Uncertainty for Adversarial Example Detection. CoRR Lewis Smith, Yarin Gal
2017	—	`conf`	Concrete Dropout. NIPS Yarin Gal, Jiri Hron, Alex Kendall
2017	A*	`conf`	Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning. IJCAI Rowan McAllister, Yarin Gal, Alex Kendall, Mark van der Wilk, Amar Shah, Roberto Cipolla, Adrian Weller
2017	A*	`conf`	Deep Bayesian Active Learning with Image Data. ICML Yarin Gal, Riashat Islam, Zoubin Ghahramani
2017	J	`jnl`	Deep Bayesian Active Learning with Image Data. CoRR Yarin Gal, Riashat Islam, Zoubin Ghahramani
2017	A*	`conf`	Dropout Inference in Bayesian Neural Networks with Alpha-divergences. ICML Yingzhen Li, Yarin Gal
2017	J	`jnl`	Dropout Inference in Bayesian Neural Networks with Alpha-divergences. CoRR Yingzhen Li, Yarin Gal
2017	J	`jnl`	Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics. CoRR Alex Kendall, Yarin Gal, Roberto Cipolla
2017	—	`conf`	Real Time Image Saliency for Black Box Classifiers. NIPS Piotr Dabkowski, Yarin Gal
2017	J	`jnl`	Vprop: Variational Inference using RMSprop. CoRR Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt, Yarin Gal
2017	—	`conf`	What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? NIPS Alex Kendall, Yarin Gal
2017	J	`jnl`	What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? CoRR Alex Kendall, Yarin Gal
2016	—	`conf`	A Theoretically Grounded Application of Dropout in Recurrent Neural Networks. NIPS Yarin Gal, Zoubin Ghahramani
2016	A*	`conf`	Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. ICML Yarin Gal, Zoubin Ghahramani
2015	J	`jnl`	Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference. CoRR Yarin Gal, Zoubin Ghahramani
2015	J	`jnl`	Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. CoRR Yarin Gal, Zoubin Ghahramani
2015	A*	`conf`	Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs. ICML Yarin Gal, Richard E. Turner
2015	A*	`conf`	Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data. ICML Yarin Gal, Yutian Chen, Zoubin Ghahramani
2014	—	`conf`	Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models. NIPS Yarin Gal, Mark van der Wilk, Carl E. Rasmussen
2014	J	`jnl`	Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models. CoRR Yarin Gal, Mark van der Wilk, Carl E. Rasmussen
2014	A*	`conf`	Pitfalls in the use of Parallel Inference for the Dirichlet Process. ICML Yarin Gal, Zoubin Ghahramani
2014	J	`jnl`	Semantics, Modelling, and the Problem of Representation of Meaning - a Brief Survey of Recent Literature. CoRR Yarin Gal
2013	—	`conf`	A Systematic Bayesian Treatment of the IBM Alignment Models. HLT-NAACL Yarin Gal, Phil Blunsom
2010	C	`conf`	Overcoming Alpha-Beta Limitations Using Evolved Artificial Neural Networks. ICMLA Yarin Gal, Mireille Avigal