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
- Bayesian Optimization with Density-Ratio Estimation☆24Updated 3 years ago
- Code for Accelerated Linearized Laplace Approximation for Bayesian Deep Learning (ELLA, NeurIPS 22')☆17Updated 3 years ago
- ☆10Updated 3 years ago
- Distributional and Outlier Robust Optimization (ICML 2021)☆28Updated 4 years ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆21Updated 4 years ago
- MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".☆18Updated 2 years ago
- Code for A General Recipe for Likelihood-free Bayesian Optimization, ICML 2022☆46Updated 3 years ago
- Dynamic causal Bayesian optimisation☆40Updated 2 years ago
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆36Updated 4 years ago
- The code for our NeurIPS 2021 paper "Kernelized Heterogeneous Risk Minimization".☆13Updated 4 years ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 4 years ago
- Refining continuous-in-depth neural networks☆42Updated 4 years ago
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 4 years ago
- Repository for Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification (NeurIPS 2024)☆44Updated last year
- Explanation Optimization☆13Updated 5 years ago
- [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)☆23Updated 5 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 6 years ago
- Accompanying code for our NeurIPS 2019 paper☆12Updated 6 years ago
- Pytorch (PyG) and Tensorflow (Keras/Spektral) implementation of Total Variation Graph Neural Network (TVGNN), as presented at ICML 2023.☆20Updated 10 months ago
- The Wasserstein Distance and Optimal Transport Map of Gaussian Processes☆52Updated 5 years ago
- Energy Based Models are a quite novel technique for density estimation. In this university project I explore this new research topic and …☆16Updated 4 years ago
- ☆34Updated 2 years ago
- Re-Examining Linear Embeddings for High-dimensional Bayesian Optimization☆42Updated 4 years ago
- Contains code for the NeurIPS 2020 paper by Pan et al., "Continual Deep Learning by FunctionalRegularisation of Memorable Past"☆44Updated 5 years ago
- NOMU: Neural Optimization-based Model Uncertainty☆10Updated 2 years ago
- Pytorch implementation of neural processes and variants☆29Updated last year
- Experiments from the paper "On Second Order Behaviour in Augmented Neural ODEs"☆61Updated last year
- Featurized Density Ratio Estimation☆20Updated 4 years ago