QPyTorch’s documentation¶
Examples:
- Basic Usage
- Exact QEPs (Regression)
- Exact QEPs with Scalable (GPU) Inference
- Multitask/Multioutput QEPs with Exact Inference
- QEP Latent Variable Models
- Variational and Approximate QEPs
- Deep QEP and Deep Sigma Point Processes
- PyTorch NN Integration (Deep Kernel Learning)
- QEP Modeling with Derivatives
- Pyro Integration
- Advanced Usage
Package Reference
Settings and Beta Features
Advanced Package Reference
Indices and tables¶
Projects¶
Q-Exponential Process [Q-EXP]
Regularization of Latent Representation [Reg_Rep]
Deep Q-Exponential Processes [DeepQEP]
Solving PDE with Q-Exponential Processes [Diff_QEP]
Research references¶
Li, Shuyi, Michael O’Connor, and Shiwei Lan. “Bayesian Learning via Q-Exponential Process.” In Advances in NIPS (2023).
Obite, Chukwudi P., Zhi Chang, Keyan Wu, and Shiwei Lan. “Bayesian Regularization on Latent Representation.” In ICLR (2025).
Chang, Zhi, Chukwudi P. Obite, Shuang Zhou, and Shiwei Lan. “Deep Q-Exponential Processes. “ In AABI (2025).
Yu, Guangting and Shiwei Lan. “Solving and Learning Partial Differential Equations with Variational Q-Exponential Processes.” In NIPS (2025).