Shen-Lab / Bayesian-L2O
[ICLR 2022] "Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How" by Yuning You, Yue Cao, Tianlong Chen, Zhangyang Wang, Yang Shen
☆13Updated 2 years ago
Alternatives and similar repositories for Bayesian-L2O:
Users that are interested in Bayesian-L2O are comparing it to the libraries listed below
- ☆15Updated 2 years ago
- Code for Accelerated Linearized Laplace Approximation for Bayesian Deep Learning (ELLA, NeurIPS 22')☆16Updated 2 years ago
- Distributional and Outlier Robust Optimization (ICML 2021)☆26Updated 3 years ago
- Code for "Function Space Particle Optimization for Bayesian Neural Networks"☆17Updated 2 years ago
- Post-processing for fair classification☆12Updated 2 months ago
- Bayesian Optimization with Density-Ratio Estimation☆23Updated 2 years ago
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆30Updated 3 years ago
- implementations sde-net☆14Updated 4 years ago
- Mini Bayesian Optimization package for ACML2020 Tutorial on Bayesian Optimization☆15Updated 2 years ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆27Updated 3 years ago
- The Wasserstein Distance and Optimal Transport Map of Gaussian Processes☆51Updated 4 years ago
- Dynamic causal Bayesian optimisation☆35Updated last year
- Code associated with paper "High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization"☆15Updated 4 years ago
- Codes for "Understanding and Accelerating Particle-Based Variational Inference" (ICML-19)☆22Updated 5 years ago
- Release code for ICML2020 Knowing The What But Not The Where in Bayesian Optimization☆15Updated last year
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 3 years ago
- Code for paper: End-to-end Stochastic Optimization with Energy-based Model☆16Updated last year
- Gradient Estimation with Discrete Stein Operators (NeurIPS 2022)☆17Updated last year
- Code for "Maximizing Acquisition Functions for Bayesian Optimization"☆12Updated 6 years ago
- ☆10Updated 2 years ago
- Accompanying code for our NeurIPS 2019 paper☆12Updated 5 years ago
- Re-Examining Linear Embeddings for High-dimensional Bayesian Optimization☆41Updated 3 years ago
- Explanation Optimization☆13Updated 4 years ago
- ☆10Updated 10 months ago
- The code for our NeurIPS 2021 paper "Kernelized Heterogeneous Risk Minimization".☆12Updated 3 years ago
- This is the code for our paper: Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces (Leonard Papenmeier…☆15Updated last year
- Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks☆9Updated 2 years ago
- MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".☆18Updated last year
- This is the companion code for the paper Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization by Lukas P. Fröhlich et al…☆11Updated 4 years ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆17Updated 3 years ago