Shen-Lab / Bayesian-L2OLinks
[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
☆14Updated 3 years ago
Alternatives and similar repositories for Bayesian-L2O
Users that are interested in Bayesian-L2O are comparing it to the libraries listed below
Sorting:
- ☆15Updated 3 years ago
- Code for Accelerated Linearized Laplace Approximation for Bayesian Deep Learning (ELLA, NeurIPS 22')☆16Updated 2 years ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆20Updated 3 years ago
- Bayesian Optimization with Density-Ratio Estimation☆24Updated 2 years ago
- [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)☆22Updated 4 years ago
- Pytorch (PyG) and Tensorflow (Keras/Spektral) implementation of Total Variation Graph Neural Network (TVGNN), as presented at ICML 2023.☆20Updated 6 months ago
- Refining continuous-in-depth neural networks☆42Updated 3 years ago
- NOMU: Neural Optimization-based Model Uncertainty☆10Updated 2 years ago
- Dynamic causal Bayesian optimisation☆40Updated 2 years ago
- ☆10Updated 3 years ago
- Repository for Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification (NeurIPS 2024)☆43Updated 9 months ago
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆34Updated 3 years ago
- PyTorch implementation of the NCDSSM models presented in the ICML '23 paper "Neural Continuous-Discrete State Space Models for Irregularl…☆25Updated 2 years ago
- Code for A General Recipe for Likelihood-free Bayesian Optimization, ICML 2022☆44Updated 3 years ago
- Bayesian Attention Modules☆35Updated 4 years ago
- Official implementation of Transformer Neural Processes☆78Updated 3 years ago
- Distributional and Outlier Robust Optimization (ICML 2021)☆27Updated 4 years ago
- Repo for our paper "Repulsive deep ensembles are Bayesian"☆19Updated 3 years ago
- Gradient Estimation with Discrete Stein Operators (NeurIPS 2022)☆17Updated last year
- Code for "Decision-Focused Learning without Differentiable Optimization: Learning Locally Optimized Decision Losses"☆28Updated last year
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 3 years ago
- Laplace Redux -- Effortless Bayesian Deep Learning☆42Updated 3 months ago
- This is the official implementation for COSMOS: a method to learn Pareto fronts that scales to large datasets and deep models.☆38Updated 4 years ago
- NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks☆37Updated 2 years ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 4 years ago
- Experiments from the paper "On Second Order Behaviour in Augmented Neural ODEs"☆60Updated 11 months ago
- The Wasserstein Distance and Optimal Transport Map of Gaussian Processes☆53Updated 5 years ago
- MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".☆18Updated 2 years ago
- An elegant adaptive importance sampling algorithms for simulations of multi-modal distributions (NeurIPS'20)☆42Updated 3 years ago
- Codes for "Understanding and Accelerating Particle-Based Variational Inference" (ICML-19)☆22Updated 5 years ago