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stat.ML2023
Practical Equivariances via Relational Conditional Neural Processes
Daolang Huang, Manuel Haussmann, Ulpu Remes +5
Conditional Neural Processes (CNPs) are a class of metalearning models popular for combining the runtime efficiency of amortized inference with reliable uncertainty quantification.…
stat.ML2023
Fast post-process Bayesian inference with Variational Sparse Bayesian Quadrature
Chengkun Li, Grégoire Clarté, Martin Jørgensen +1
In applied Bayesian inference scenarios, users may have access to a large number of pre-existing model evaluations, for example from maximum-a-posteriori (MAP) optimization runs. H…