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Found 14 papers, 8 papers with code
Tighter sparse variational Gaussian processes
Extensive experiments on regression benchmarks, classification, and latent variable models demonstrate that the proposed approximation consistently matches or outperforms standard sparse variational GPs while maintaining the same computational cost.
A Meta-Learning Approach to Bayesian Causal Discovery
To address these limitations, we propose a Bayesian meta learning model that allows for sampling causal structures from the posterior and encodes these key properties.
Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data
Recently, transformer-based approaches have shown great promise in a range of weather forecasting problems.
In-Context In-Context Learning with Transformer Neural Processes
Standard NP architectures, such as the convolutional conditional NP (ConvCNP) or the family of transformer neural processes (TNPs), are not capable of in-context in-context learning, as they are only able to condition on a single dataset.
Approximately Equivariant Neural Processes
Our approach is agnostic to both the choice of symmetry group and model architecture, making it widely applicable.
Translation Equivariant Transformer Neural Processes
Notably, the posterior prediction maps for data that are stationary -- a common assumption in spatio-temporal modelling -- exhibit translation equivariance.
Noise-Aware Differentially Private Regression via Meta-Learning
One approach to mitigating this issue is pre-training models on simulated data before DP learning on the private data.
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning
The global inducing point variational approximation for BNNs is based on using a set of inducing inputs to construct a series of conditional distributions that accurately approximate the conditionals of the true posterior distribution.
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
In this work, we further extend the existing body of work and develop a novel gradient-based approach to learning an ADMG with non-linear functional relations from observational data.
Differentially private partitioned variational inference
In this paper, we present differentially private partitioned variational inference, the first general framework for learning a variational approximation to a Bayesian posterior distribution in the federated learning setting while minimising the number of communication rounds and providing differential privacy guarantees for data subjects.
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
Variational inference (VI) has become the method of choice for fitting many modern probabilistic models.
Do Concept Bottleneck Models Learn as Intended?
Concept bottleneck models map from raw inputs to concepts, and then from concepts to targets.
Scalable Gaussian Process Variational Autoencoders
Conventional variational autoencoders fail in modeling correlations between data points due to their use of factorized priors.
Sparse Gaussian Process Variational Autoencoders
Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering.
