Paper-Conference

Conservative neural posterior estimation via distributionally robust training
Simulation-based inference with neural posterior estimation (NPE) often yields overconfident and unreliable posteriors under limited …
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Multi-Output Robust and Conjugate Gaussian Processes
Multi-output Gaussian process (MOGP) regression allows modelling dependencies among multiple correlated response variables. Similarly …
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Concept frustration: Aligning human concepts and machine representations
Aligning human-interpretable concepts with the internal representations learned by modern machine learning systems remains a central …
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Robust Bayesian Optimisation with Unbounded Corruptions
Bayesian Optimization is critically vulnerable to extreme outliers. Existing provably robust methods typically assume a bounded …
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Thinned Mean Field Langevin Dynamics
Several important learning tasks can be formulated as minimizing an entropy-regularized objective over an appropriate space of …
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TabMGP: Martingale posterior with TabPFN
Bayesian inference provides principled uncertainty quantification but is often limited by challenges of prior and likelihood …
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Multilevel neural simulation-based inference
Neural simulation-based inference (SBI) is a popular set of methods for Bayesian inference when models are only available in the form …
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Stationary MMD Points
Approximation of a target probability distribution using a finite set of points is a problem of fundamental importance in numerical …
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Kernel Quantile Embeddings and Associated Probability Metrics
Embedding probability distributions into reproducing kernel Hilbert spaces (RKHS) has enabled powerful nonparametric methods such as …
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Prediction-Centric Uncertainty Quantification via MMD
Deterministic mathematical models, such as those specified via differential equations, are a powerful tool to communicate scientific …
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