Jeremias Knoblauch

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Theodoros (Theo) DamoulasProfessor, CS+Stats, University of Warwick - UKRI Turing AI FellowVerified email at warwick.ac.uk
Jack JewsonDepartment of Econometrics and Business Statistics, Monash UniversityVerified email at monash.edu
François-Xavier BriolAssociate Professor in Statistical Science, UCLVerified email at ucl.ac.uk
Hisham HusainGoogle ResearchVerified email at google.com
Takuo MatsubaraWilliam Gordon Seggie Brown FellowVerified email at ed.ac.uk
Chris. J. OatesNewcastle UniversityVerified email at ncl.ac.uk
Tom DietheAstraZeneca; University of BristolVerified email at bristol.ac.uk
Matias AltamiranoUniversity College LondonVerified email at ucl.ac.uk
Juan MaroñasUniversidad Autónoma de MadridVerified email at uam.es
Oliver HamelijnckThe University of Warwick, The Alan Turing InstituteVerified email at turing.ac.uk
Charita (Harita) DellaportaPhD Student, University of WarwickVerified email at warwick.ac.uk
Simo SärkkäProfessor, Aalto UniversityVerified email at aalto.fi
Kimmo KaskiProfessor of Computational Science, Aalto University School of ScienceVerified email at aalto.fi
Sebastian M SchmonSenior Staff AI/ML at Altos LabsVerified email at stats.ox.ac.uk
Patrick CannonAmazon AGIVerified email at amazon.co.uk
Dino SejdinovicProfessor of Statistical Machine Learning, University of AdelaideVerified email at adelaide.edu.au
Veit D. WildUniversity of OxfordVerified email at stats.ox.ac.uk
Sahra GhalebikesabiPhD Candidate, University of OxfordVerified email at univ.ox.ac.uk
David Tyler FrazierMonash University, Department of Econometrics and Business StatisticsVerified email at monash.edu
David DunsonArts & Sciences Professor of Statistical Science & MathematicsVerified email at duke.edu

Jeremias Knoblauch

Assistant professor/lecturer & EPSRC Fellow @ University College London

Verified email at ucl.ac.uk - Homepage

generalised Bayes robust inference Bayesian statistics variational inference statistical machine learning


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An optimization-centric view on Bayes' rule: Reviewing and generalizing variational inference J Knoblauch, J Jewson, T Damoulas Journal of Machine Learning Research 23 (132), 1-109, 2022	212*	2022
Optimal continual learning has perfect memory and is np-hard J Knoblauch, H Husain, T Diethe International Conference on Machine Learning, 5327-5337, 2020	113	2020
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with -Divergences J Knoblauch, JE Jewson, T Damoulas Advances in Neural Information Processing Systems 31, 2018	83	2018
Robust generalised Bayesian inference for intractable likelihoods T Matsubara, J Knoblauch, FX Briol, CJ Oates Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2022	73	2022
Spatio-temporal Bayesian on-line changepoint detection with model selection J Knoblauch, T Damoulas International Conference on Machine Learning, 2718-2727, 2018	66	2018
Transforming Gaussian processes with normalizing flows J Maroñas, O Hamelijnck, J Knoblauch, T Damoulas International Conference on Artificial Intelligence and Statistics, 1081-1089, 2021	39	2021
Robust Bayesian inference for simulator-based models via the MMD posterior bootstrap C Dellaporta, J Knoblauch, T Damoulas, FX Briol International Conference on Artificial Intelligence and Statistics, 943-970, 2022	36	2022
Uncertainty-aware deep learning methods for robust diabetic retinopathy classification J Jaskari, J Sahlsten, T Damoulas, J Knoblauch, S Särkkä, L Kärkkäinen, ... IEEE Access 10, 76669-76681, 2022	32	2022
Generalized posteriors in approximate Bayesian computation SM Schmon, PW Cannon, J Knoblauch arXiv preprint arXiv:2011.08644, 2020	25	2020
Generalized Bayesian inference for discrete intractable likelihood T Matsubara, J Knoblauch, FX Briol, CJ Oates Journal of the American Statistical Association, 1-11, 2023	16	2023
Robust Deep Gaussian Processes J Knoblauch arXiv preprint arXiv:1904.02303, 2019	16	2019
Robust and scalable Bayesian online changepoint detection M Altamirano, FX Briol, J Knoblauch International Conference on Machine Learning, 642-663, 2023	15	2023
A rigorous link between deep ensembles and (variational) Bayesian methods VD Wild, S Ghalebikesabi, D Sejdinovic, J Knoblauch Advances in Neural Information Processing Systems 36, 2024	11	2024
Frequentist consistency of generalized variational inference J Knoblauch arXiv preprint arXiv:1912.04946, 2019	10	2019
Adversarial interpretation of Bayesian inference H Husain, J Knoblauch International Conference on Algorithmic Learning Theory, 553-572, 2022	9	2022
Robust Bayesian inference for discrete outcomes with the total variation distance J Knoblauch, L Vomfell arXiv preprint arXiv:2010.13456, 2020	9	2020
Robust and conjugate Gaussian process regression M Altamirano, FX Briol, J Knoblauch arXiv preprint arXiv:2311.00463, 2023	7	2023
Robustifying likelihoods by optimistically re-weighting data M Dewaskar, C Tosh, J Knoblauch, DB Dunson arXiv preprint arXiv:2303.10525, 2023	5	2023
Outlier-robust Kalman Filtering through Generalised Bayes G Duran-Martin, M Altamirano, AY Shestopaloff, L Sánchez-Betancourt, ... arXiv preprint arXiv:2405.05646, 2024	3	2024
Predictive performance of power posteriors Y McLatchie, E Fong, DT Frazier, J Knoblauch arXiv preprint arXiv:2408.08806, 2024	1	2024

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