Yarin gal papers. Machine learning, 37(2):183–233, 1999.
Yarin gal papers On the other hand, epistemic uncertainty accounts for uncertainty in the model - uncertainty which can be explained away given enough data. The Feb 16, 2025 · View a PDF of the paper titled Uncertainty-Aware Step-wise Verification with Generative Reward Models, by Zihuiwen Ye and 5 other authors %0 Conference Paper %T Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning %A Yarin Gal %A Zoubin Ghahramani %B Proceedings of The 33rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Maria Florina Balcan %E Kilian Q. Marks Published in Nature [Nature, pdf] Predicting Twitter Engagement With Deep Language Models Many Authors 2nd Place at RecSys 2020 [pdf] Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers Apr 17, 2023 · View a PDF of the paper titled Prediction-Oriented Bayesian Active Learning, by Freddie Bickford Smith and 5 other authors Mar 7, 2017 · You can change the function the data is drawn from (with two functions, one from the last blog post and one from the appendix in this paper), and the model used (a homoscedastic model or a heteroscedastic model, see section § 4. Philip Torr, Prof. Crucially, without using their more complex methods for estimating Hence, by exposing this flaw in experimental procedure, we highlight the importance of using identical experimental setups to evaluate, compare and benchmark methods in Bayesian Deep Learning. We follow pragmatic approaches to fundamental Yiren Zhao Yarin Gal arXiv:2305. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning by Yarin Gal and Zoubin Ghahramani ICML 2016 Abtin Mogharabin Mar 9, 2021 · We introduce a new framework for sample-efficient model evaluation that we call active testing. Aug 12, 2024 · I am a Quant Researcher at Jane Street, working on quant finance and related machine learning problems. Machine learning, 37(2):183–233, 1999. Jishnu Mukhoti, Pontus Stenetorp, Yarin Gal Bayesian Deep Learning workshop, NIPS, 2018 [Paper] [arXiv] [BibTex] Authors Yarin Gal, Jiri Hron, Alex Kendall Abstract Dropout is used as a practical tool to obtain uncertainty estimates in large vision models and reinforcement learning (RL) tasks. 52202/079017-0283 Abstract Uncertainty quantification in Large Language Models (LLMs) is crucial for applications where safety and reliability are important. A. Jun 22, 2024 · We propose semantic entropy probes (SEPs), a cheap and reliable method for uncertainty quantification in Large Language Models (LLMs). BatchBALD is a greedy linear-time $1 - \\frac{1}{e}$-approximate algorithm amenable to dynamic programming and efficient caching. We come from academia (Oxford, Cambridge, MILA, McGill, U of Amsterdam, U of Toronto, Yale, and others) and industry (Google, DeepMind, Twitter, Qualcomm, and startups). Gomez, Kelly Brock, Yarin Gal, and Debora S. This preference data can be used to fine-tune or guide other LMs and/or AI systems. van der Ouderaa, Mark van der Wilk and Yarin Gal received the top award in the Proceedings track of the ICML Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM). Yarin Gal 2025 pdf bib abs Detecting LLM Hallucination Through Layer-wise Information Deficiency: Analysis of Ambiguous Prompts and Unanswerable Questions Hazel Kim | Tom A. 24384-24394 Abstract Feb 23, 2021 · Reliable uncertainty from deterministic single-forward pass models is sought after because conventional methods of uncertainty quantification are computationally expensive. Torr, Yarin Gal; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. [15] Yarin Gal and Zoubin Ghahramani. Jan 9, 2025 · In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety, particularly in managing dual-use knowledge in sensitive domains like cybersecurity and chemical, biological, radiological, and nuclear (CBRN) safety. 52202/079017-0982 Abstract This paper presents a method for estimating the hallucination rate for in-context learning (ICL) with generative AI. press/v48/gal16 Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics Alex Kendall, Yarin Gal, Roberto Cipolla; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models. Dec 15, 2022 · View a PDF of the paper titled CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models, by Lorenz Kuhn and 2 other authors The Oxford Applied and Theoretical Machine Learning Group (OATML) is a research group within the Department of Computer Science of the University of Oxford led by Prof Yarin Gal. Li ArXiv Preprint Eliciting Human Preferences with Language Models Belinda Z. Log plot of average reward obtained by both epsilon greedy (in green) and our approach (in blue), as a function of the number of batches. Location: Hall 4 #6 Date: 28 Apr 2025 (Monday)HOME SPEAKERS AND PANELISTS CALL FOR PAPERS WORKSHOP SCHEDULE Jun 22, 2021 · View a PDF of the paper titled Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers, by Andreas Kirsch and 2 other authors Authors Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. %0 Conference Paper %T Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning %A Yarin Gal %A Zoubin Ghahramani %B Proceedings of The 33rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Maria Florina Balcan %E Kilian Q. mlr. Mar 4, 2020 · We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. press/v48/gal16 May 22, 2017 · View a PDF of the paper titled Concrete Dropout, by Yarin Gal and 2 other authors Bibtex Metadata Paper Reviews Supplemental Authors Alex Kendall, Yarin Gal Abstract There are two major types of uncertainty one can model. 7482-7491 Abstract Bibtex Metadata Paper Reviews Supplemental Authors Yarin Gal, Zoubin Ghahramani Abstract Recurrent neural networks (RNNs) stand at the forefront of many recent developments in deep learning. Recently, language models (LMs) have been used to gather information about the preferences of human users. Recent work by Farquhar et al. Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning. Yarin Gal ‘Hallucinations’ are a critical problem9 for natural language genera-tion systems using large language models (LLMs), such as ChatGPT1 or Gemini2, because users cannot trust that any given Yarin Gal is supported by the Google European Fellowship in Machine Learning. I obtained a PhD (DPhil) in Machine Learning from the University of Oxford. Aug 6, 2017 · Gal, Yarin and Ghahramani, Zoubin. Bibtex Paper Authors Pascal Notin, Ruben Weitzman, Debora Marks, Yarin Gal Abstract Protein design holds immense potential for optimizing naturally occurring proteins, with broad applications in drug discovery, material design, and sustainability. Weinberger %F pmlr-v48-gal16 %I PMLR %P 1050--1059 %U https://proceedings. 00802, 2019. Group members are also co-organizing the Workshop on Computational Biology, and the Oxford Wom*n Social. [16] Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul. However, LMs have been shown to struggle with crucial aspects of preference learning Jul 17, 2022 · OATML group members and collaborators are proud to present 11 papers at the ICML 2022 main conference and workshops. Previously Andreas Kirsch, Joost van Amersfoort, and Yarin Gal. We take two complex single-forward-pass uncertainty approaches, DUQ and SNGP, and examine whether they mainly rely on a well-regularized feature space. Professor of Machine Learning, University of Oxford - Cited by 57,659 - Machine Learning - Artificial Intelligence - Probability Theory - Statistics This paper is a short version of the appendix of "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning". Lamb | Adel Bibi | Philip Torr | Yarin Gal Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing pdf bib abs The Oxford Applied and Theoretical Machine Learning Group (OATML) is a research group within the Department of Computer Science of the University of Oxford led by Prof Yarin Gal. Aug 22, 2022 · Yarin Gal The field of Bayesian deep learning has experienced a boom in research in the past few years, with various tools developed to estimate different types of uncertainty, each with its own Mar 8, 2024 · Aligning AI systems to users' interests requires understanding and incorporating humans' complex values and preferences. An introduction to variational methods for graphical models. arXiv preprint arXiv:1903. Cunningham, David Blei Digital Object Identifier (DOI) 10. This creates a disconnect to real applications, where test labels Lorenz was a DPhil student in Computer Science working with Prof. Yarin Gal at the University of Oxford. Our approach, deterministic uncertainty quantification (DUQ), builds upon ideas of RBF networks. We propose a new dropout Kunal Handa, Yarin Gal, Ellie Pavlick, Noah Goodman, Jacob Andreas, Alex Tamkin, Belinda Z. He is an Associate Professor of Machine Learning at the Computer Science department, University of Oxford. Li, Alex Tamkin, Noah Goodman, Jacob Andreas ICLR, 2025 Implicit Representations of Meaning in Neural Language Models Belinda Z. LG] 31 May 2023 THE CURSE OF RECURSION: TRAINING ON GENERATED DATA MAKES MODELS FORGET {Jonathan Frazer, Pascal Notin, Mafalda Dias}, Aidan N. During my time at Oxford, I was fortunate to have worked with Prof. Bibtex Paper Authors Alexander Nikitin, Jannik Kossen, Yarin Gal, Pekka Marttinen Digital Object Identifier (DOI) 10. Uncertainty in Deep Learning Yarin Gal Department of Engineering University of Cambridge This dissertation is submitted for the degree of Aug 12, 2025 · Our study makes a significant stride in this direction. We compare Feb 19, 2023 · View a PDF of the paper titled Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation, by Lorenz Kuhn and 2 other authors May 19, 2017 · View a PDF of the paper titled Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics, by Alex Kendall and Yarin Gal and Roberto Cipolla Jun 9, 2025 · A paper by Yoav Gelberg, Tycho F. Lorenz is a recipient of a FHI DPhil Scholarship and holds a MSc in Computer Science from ETH Zurich. 6 in the thesis for example or this blog post). Aviral Kumar and Sunita Sarawagi. Yarin leads the Oxford Applied and Theoretical Machine Learning (OATML) group. We follow pragmatic approaches to fundamental Dec 16, 2015 · View a PDF of the paper titled A Theoretically Grounded Application of Dropout in Recurrent Neural Networks, by Yarin Gal and 1 other authors Bayesian Deep Learning NeurIPS 2021 Workshop Tuesday, December 14, 2021, Virtual Schedule & Accepted Papers Abstract Call for participation Topics Organisers 2016 Mar 15, 2017 · View a PDF of the paper titled What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?, by Alex Kendall and Yarin Gal Publications page has been updated with new NIPS papers. Aleatoric uncertainty captures noise inherent in the observations. May 27, 2023 · View a PDF of the paper titled The Curse of Recursion: Training on Generated Data Makes Models Forget, by Ilia Shumailov and 5 other authors Jun 6, 2015 · View a PDF of the paper titled Dropout as a Bayesian Approximation: Appendix, by Yarin Gal and 1 other authors Bibtex Metadata Paper Reviews Supplemental Authors Alex Kendall, Yarin Gal Abstract There are two major types of uncertainty one can model. Their availability promotes red teaming, mitigates market concentration, and accelerates scientific progress. Hallucinations, which are plausible-sounding but factually incorrect and arbitrary model generations, present a major challenge to the practical adoption of LLMs. Jun 19, 2019 · We develop BatchBALD, a tractable approximation to the mutual information between a batch of points and model parameters, which we use as an acquisition function to select multiple informative points jointly for the task of deep Bayesian active learning. By enforcing Deep Deterministic Uncertainty: A New Simple Baseline Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. Marks Published in Nature [Nature, pdf] Predicting Twitter Engagement With Deep Language Models Many Authors 2nd Place at RecSys 2020 [pdf] Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers Cet article explore les modèles de langage pour l'annotation et la génération de données synthétiques, en mettant l'accent sur les méthodes et les défis associés. NIPS, 2019. ICLR workshop track, 2016a. We're also organising the Third Bayesian Deep Learning workshop at NIPS 2018. Yarin Gal, Zoubin Ghahramani Deep Learning Workshop, ICML, 2015 [PDF] [Poster] [BibTex] Jun 6, 2015 · View a PDF of the paper titled Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning, by Yarin Gal and 1 other authors Jul 24, 2024 · AI models collapse when trained on recursively generated data Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross Anderson & Yarin Gal Nature 631, 755–759 (2024) Cite this article Yarin Gal's 161 research works with 10,502 citations and 12,955 reads, including: Learning from prepandemic data to forecast viral escape Oct 31, 2025 · Yarin Gal authored at least 205 papers between 2010 and 2025. Bayesian convolutional neural networks with Bernoulli approximate variational inference. Many research papers have already shown that BDL is crucial for applications that include medical scanners, robotics, science, autonomous driving, and more. While approaches like active learning reduce the number of labels needed for model training, existing literature largely ignores the cost of labeling test data, typically unrealistically assuming large test sets for model evaluation. Myle Ott, Sergey Edunov, David Grangier, and Michael . Calibration of encoder decoder models for neural machine translation. S. Dijkstra number of four. 17493v2 [cs. Erdős number of three. (2024) proposes semantic entropy (SE), which can detect {Jonathan Frazer, Pascal Notin, Mafalda Dias}, Aidan N. In ICL, a conditional generative model (CGM) is prompted with a dataset and a This paper presents a theoretical framework for interpreting dropout training in neural networks as approximate Bayesian inference in deep Gaussian processes. Figure 6. Gal, a researcher and professor at Oxford University, UK, has also collaborated with NASA on 3D asteroid modeling and exoplanet detection. But to obtain well-calibrated uncertainty estimates, a grid-search over the dropout probabilities is necessary—a prohibitive operation with large models, and an impossible one with RL. ICLR workshop track, 2016. Senior author Associate Professor Yarin Gal, Department of Computer Science Open-weight models are a cornerstone of transparent, collaborative AI research. His main research interests include improving our theoretical understanding of deep learning, as well as making deep learning safer and more reliable for real-world use cases. Prior to that, I was a Research Scientist at Meta, working on large multi-modal language model for the search problems. Li, Maxwell Nye, Jacob Andreas ACL, 2021. In this paper we combine recent advances in Bayesian deep learning into the active learning framework in a practical way. We develop an active learning framework for high dimensional data, a task which has been extremely challenging so far, with very sparse existing literature. The Oxford Applied and Theoretical Machine Learning Group (OATML) is a research group within the Department of Computer Science of the University of Oxford led by Prof Yarin Gal. He is also the Tutorial Fellow in Computer Science at Christ Church, Oxford, a Turing AI Fellow at the Alan Turing Institute, and Director of Research at the AI Safety Institute (AISI). We follow pragmatic approaches to fundamental Mar 8, 2017 · View a PDF of the paper titled Deep Bayesian Active Learning with Image Data, by Yarin Gal and Riashat Islam and Zoubin Ghahramani Jun 19, 2024 · Hallucinations (confabulations) in large language model systems can be tackled by measuring uncertainty about the meanings of generated responses rather than the text itself to improve Yarin Gal Number of Papers:- 2 Number of Citations:- 4 First ACL Paper:- 2013 Latest ACL Paper:- 2023 Venues:- ACL Apr 22, 2025 · Awards Committee: Cordelia Schmid, Guy Van der Broek, Jun Zhu, Katerina Fragkiadaki, Lihong Li, Luke Zettlemoyer, Natasha Jaques, Tao Yu, Yarin Gal Selection Process The ICLR 2025 Outstanding Paper Committee went through a two-stage selection process to identify a collection of outstanding papers and honorable mentions that showcase excellent research presented at this conference. Yet a major difficulty with these models is their tendency to overfit, with dropout shown to fail when applied to recurrent layers. ukje tse vpxbk ttaz xejzj hzt eopcid iatjr vbx hlnapbk fbny czwq xnwyxf igss mdrwrml