Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection. - Jeremias Knoblauch; Theodoros Damoulas (ICML, 2018)

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

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

Generalized Variational Inference. - Jeremias Knoblauch; Jack Jewson; Theodoros Damoulas (CoRR, 2019)

Robust Deep Gaussian Processes. - Jeremias Knoblauch (CoRR, 2019)

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

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

Transforming Gaussian Processes With Normalizing Flows. - Juan Maroñas; Oliver Hamelijnck; Jeremias Knoblauch; Theodoros Damoulas (AISTATS, 2021)

Optimization-centric generalizations of Bayesian inference. - Jeremias Knoblauch (2021)

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

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

Uncertainty-Aware Deep Learning Methods for Robust Diabetic Retinopathy Classification. - Joel Jaskari; Jaakko Sahlsten; Theodoros Damoulas; Jeremias Knoblauch; Simo Särkkä; Leo Kärkkäinen; Kustaa Hietala; Kimmo K. Kaski (IEEE Access, 2022)

An Optimization-centric View on Bayes' Rule: Reviewing and Generalizing Variational Inference. - Jeremias Knoblauch; Jack Jewson; Theodoros Damoulas (J. Mach. Learn. Res., 2022)

Robust and Scalable Bayesian Online Changepoint Detection. - Matías Altamirano; François-Xavier Briol; Jeremias Knoblauch (ICML, 2023)

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

Robust and Conjugate Gaussian Process Regression. - Matías Altamirano; François-Xavier Briol; Jeremias Knoblauch (ICML, 2024)

Outlier-robust Kalman Filtering through Generalised Bayes. - Gerardo Duran-Martin; Matías Altamirano; Alexander Y. Shestopaloff; Leandro Sánchez-Betancourt; Jeremias Knoblauch; Matt Jones 002; François-Xavier Briol; Kevin Patrick Murphy (ICML, 2024)

Prediction-Centric Uncertainty Quantification via MMD. - Zheyang Shen; Jeremias Knoblauch; Samuel Power; Chris J. Oates (AISTATS, 2025)

Near-Optimal Approximations for Bayesian Inference in Function Space. - Veit Wild; James Wu; Dino Sejdinovic; Jeremias Knoblauch (CoRR, 2025)