VirgiAgl / DAG-GPLinks
Code for the paper Multi-task Causal Learning with Gaussian Processes (https://arxiv.org/pdf/2009.12821.pdf)
☆12Updated 4 years ago
Alternatives and similar repositories for DAG-GP
Users that are interested in DAG-GP are comparing it to the libraries listed below
Sorting:
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆82Updated last year
- ☆32Updated 7 years ago
- 🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0☆26Updated last year
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆89Updated 4 years ago
- Code for the paper "Bayesian Neural Network Priors Revisited"☆57Updated 4 years ago
- Implementation of the MIWAE method for deep generative modelling of incomplete data sets.☆41Updated last year
- Code repo for "Function-Space Distributions over Kernels"☆32Updated 4 years ago
- General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.☆38Updated 3 years ago
- Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"☆26Updated 6 years ago
- Pytorch version of "Deep Convolutional Networks as shallow Gaussian Processes" by Adrià Garriga-Alonso, Carl Rasmussen and Laurence Aitch…☆32Updated 5 years ago
- This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).☆50Updated 4 years ago
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆33Updated 3 years ago
- ☆25Updated 2 years ago
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 3 years ago
- Repository for Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification (NeurIPS 2024)☆43Updated 8 months ago
- ☆43Updated 6 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆86Updated 5 years ago
- Official code for the ICLR 2021 paper Neural ODE Processes☆73Updated 3 years ago
- Code for the ICML 2019 paper: Distribution Calibration for Regression☆22Updated last year
- Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective.☆51Updated 3 years ago
- Code for paper: SDE-Net: Equipping Deep Neural network with Uncertainty Estimates☆114Updated 4 years ago
- Adaptive and Reliable Classification: efficient conformity scores for multi-class classification problems☆31Updated 2 years ago
- Bayesianize: A Bayesian neural network wrapper in pytorch☆88Updated last year
- Time-Contrastive Learning☆65Updated 7 years ago
- Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)☆42Updated 3 years ago
- Code for the Causal Bayesian Optimization algorithm (http://proceedings.mlr.press/v108/aglietti20a/aglietti20a.pdf)☆29Updated 4 years ago
- Code for Randomly Projected Additive Gaussian Processes☆25Updated 5 years ago
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 2 years ago
- Code for: "Neural Controlled Differential Equations for Online Prediction Tasks"☆38Updated 2 years ago