Publications

Alessandro Barp, Carl-Johann Simon-Gabriel, Mark Girolami, Lester Mackey (2024). Targeted Separation and Convergence with Kernel Discrepancies.

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Zonghao (Hudson) Chen, Aratrika Mustaf, Pierre Glaser, Anna Korba, Arthur Gretton, Bharath K. Sriperumbudur (2024). (De)-regularized Maximum Mean Discrepancy Gradient Flow.

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Yann McLatchie, Edwin Fong, David T. Frazier, Jeremias Knoblauch (2024). Predictive performance of power posteriors.

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Xing Liu, François-Xavier Briol (2024). On the Robustness of Kernel Goodness-of-Fit Tests.

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Zonghao (Hudson) Chen, Masha Naslidnyk, Arthur Gretton, François-Xavier Briol (2024). Conditional Bayesian Quadrature. In UAI.

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Zonghao (Hudson) Chen, Ruocheng Guo, Jean-François Ton, Yang Liu (2024). Conformal Counterfactual Inference under Hidden Confounding. in KDD 2024.

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

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

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David T. Frazier, Jeremias Knoblauch, Christopher Drovandi (2024). The Impact of Loss Estimation on Gibbs Measures. In arXiv.

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Oscar Key, So Takao, Daniel Giles, Marc Peter Deisenroth (2024). Scalable Data Assimilation with Message Passing. In Climate Informatics.

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Veit Wild, Sahra Ghalebikesabi, Dino Sejdinovic, Jeremias Knoblauch (2024). A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods. In NeurIPS.

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Zhuo Sun, Alessandro Barp, François-Xavier Briol (2023). Vector-Valued Control Variates. In ICML.

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

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Jean Kaddour, Oscar Key, Piotr Nawrot, Pasquale Minervini, Matt J. Kusner (2023). No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models. In NeurIPS.

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Kaiyu Li, Daniel Giles, Toni Karvonen, Serge Guillas, François-Xavier Briol (2023). Multilevel Bayesian Quadrature. In AISTATS.

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Zhuo Sun, Chris J Oates, François-Xavier Briol (2023). Meta-learning Control Variates: Variance Reduction with Limited Data. In UAI.

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Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol (2023). Bayesian Numerical Integration with Neural Networks. In UAI.

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Masha Naslidnyk, Motonobu Kanagawa, Toni Karvonen, Maren Mahsereci (2023). Comparing Scale Parameter Estimators for Gaussian Process Regression: Cross Validation and Maximum Likelihood. In SIAM JUQ.

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

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Ayush Bharti, Masha Naslidnyk, Oscar Key, Samuel Kaski, François-Xavier Briol (2023). Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference. In ICML.

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Ziang Niu, Johanna Meier, François-Xavier Briol (2023). Discrepancy-based Inference for Intractable Generative Models using Quasi-Monte Carlo. In EJS.

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Takuo Matsubara, Jeremias Knoblauch, François-Xavier Briol, Chris Oates (2023). Robust Generalised Bayesian Inference for Intractable Likelihoods. In JRSS.

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

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Andreas Anastasiou, Alessandro Barp, François-Xavier Briol, Bruno Ebner, Robert E. Gaunt, Fatemeh Ghaderinezhad, Jackson Gorham, Arthur Gretton, Christophe Ley, Qiang Liu, Lester Mackey, Chris. J. Oates, Gesine Reinert, Yvik Swan (2022). Stein's Method Meets Computational Statistics: A Review of Some Recent Developments. In Statistical Science.

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Ayush Bharti, François-Xavier Briol, Troels Pedersen (2022). A General Method for Calibrating Stochastic Radio Channel Models with Kernels. In IEEE Transactions on Antennas and Propagation.

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Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, François-Xavier Briol (2022). Towards Healing the Blindness of Score Matching. In Workshop on Score-Based Methods NeurIPS 2022.

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Aidan N. Gomez, Oscar Key, Kuba Perlin, Stephen Gou, Nick Frosst, Jeff Dean, Yarin Gal (2022). Interlocking Backpropagation: Improving depthwise model-parallelism. In JMLR.

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Harita Dellaporta, Jeremias Knoblauch, Theo Damoulas, François-Xavier Briol (2022). Robust Bayesian inference for simulator-based models via the MMD posterior bootstrap. In AISTATS.

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Oscar Key, Arthur Gretton, François-Xavier Briol, Tamara Fernandez (2021). Composite Goodness-of-fit Tests with Kernels. In arXiv.

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Shijing Si, Chris. J. Oates, Andrew B. Duncan, Lawrence Carin, François-Xavier Briol (2021). Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization. In MCQMC.

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George Wynne, François-Xavier Briol, Mark Girolami (2021). Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness. In JMLR.

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Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth Flaxman, François-Xavier Briol (2020). Bayesian Probabilistic Numerical Integration with Tree-Based Models. In NeurIPS.

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Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark Girolami,, Lester Mackey (2019). Minimum Stein Discrepancy Estimators. In NeurIPS.

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