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Found 9 papers, 3 papers with code
Approximately Equivariant Neural Processes
Our approach is agnostic to both the choice of symmetry group and model architecture, making it widely applicable.
Challenges and Pitfalls of Bayesian Unlearning
Machine unlearning refers to the task of removing a subset of training data, thereby removing its contributions to a trained model.
How Tight Can PAC-Bayes be in the Small Data Regime?
Interestingly, this lower bound recovers the Chernoff test set bound if the posterior is equal to the prior.
Practical Conditional Neural Process Via Tractable Dependent Predictions
Existing approaches which model output dependencies, such as Neural Processes (NPs; Garnelo et al., 2018) or the FullConvGNP (Bruinsma et al., 2021), are either complicated to train or prohibitively expensive.
Efficient Gaussian Neural Processes for Regression
Conditional Neural Processes (CNP; Garnelo et al., 2018) are an attractive family of meta-learning models which produce well-calibrated predictions, enable fast inference at test time, and are trainable via a simple maximum likelihood procedure.
The Gaussian Process Latent Autoregressive Model
Many real-world prediction problems involve modelling the dependencies between multiple different outputs across the input space.
GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models
A simple and widely adopted approach to extend Gaussian processes (GPs) to multiple outputs is to model each output as a linear combination of a collection of shared, unobserved latent GPs.
Learning Causally-Generated Stationary Time Series
We present the Causal Gaussian Process Convolution Model (CGPCM), a doubly nonparametric model for causal, spectrally complex dynamical phenomena.
The Gaussian Process Autoregressive Regression Model (GPAR)
Multi-output regression models must exploit dependencies between outputs to maximise predictive performance.
