Publications

Matias Altamirano, Francois-Xavier Briol, Jeremias Knoblauch (2023). Robust and Scalable Bayesian Online Changepoint Detection. ICML.

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Matias Altamirano, Francois-Xavier Briol, Jeremias Knoblauch (2023). Robust and Conjugate Gaussian Process Regression. arXiv.

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Veit Wild, Sahra Ghalebikesabi, Dino Sejdinovic, Jeremias Knoblauch (2023). A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods. NeurIPS.

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Miheer Dewaskar, Chris Tosh, Jeremias Knoblauch, David Dunson (2023). Robustifying likelihoods by optimistically re-weighting data. arXiv.

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Takuo Matsubara, Jeremias Knoblauch, Francois-Xavier Briol, Chris Oates (2023). Generalized Bayesian Inference for Discrete Intractable Likelihood. JASA.

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Charita Dellaporta, Jeremias Knoblauch, Theo Damoulas, Francois-Xavier Briol (2022). Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap. AISTATS 2022.

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Hisham Husain, Jeremias Knoblauch (2022). Adversarial Interpretation of Bayesian Inference. ALT 2022.

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Joel Jaskari, Jaakko Sahlsten, Theodoros Damoulas, Jeremias Knoblauch, Simo Särkkä, Leo Kärkkäinen, Kustaa Hietala, Kimmo Kaski (2022). Uncertainty-aware deep learning methods for robust diabetic retinopathy classification. IEEE Access.

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Jeremias Knoblauch, Jack Jewson, Theodoros Damoulas (2021). An Optimization-centric View on Bayes' Rule: Reviewing and Generalizing Variational Inference. JMLR.

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Takuo Matsubara, Jeremias Knoblauch, Francois-Xavier Briol, Chris Oates (2021). Robust generalised Bayesian inference for intractable likelihoods. JRSS-B.

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Juan Maronas, Oliver Hamelijnck, Jeremias Knoblauch, Theodoros Damoulas (2021). Transforming Gaussian processes with normalizing flows. AISTATS (2021).

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Sebastian Schmon, Patrick Cannon, Jeremias Knoblauch (2021). Generalized Posteriors in Approximate Bayesian Computation. AABI.

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Jeremias Knoblauch, Lara Vomfell (2020). Robust Bayesian Inference for Discrete Outcomes with the Total Variation Distance. ArXiv.

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Jeremias Knoblauch, Hisham Husain, Tom Diethe (2020). Optimal Continual Learning has Perfect Memory and is NP-hard. ICML (2020).

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Jeremias Knoblauch (2019). Frequentist Consistency of Generalized Variational Inference. ArXiv.

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Jeremias Knoblauch (2019). Robust Deep Gaussian Processes. ArXiv.

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Jeremias Knoblauch, Jack Jewson, Theodoros Damoulas (2018). Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with β-Divergences. NIPS (2018).

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Jeremias Knoblauch, Theodoros Damoulas (2018). Spatio-Temporal Bayesian On-line Changepoint Detection with Model Selection. In International Conference on Machine Learning (ICML).

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