StephanLorenzen / MajorityVoteBoundsLinks
A framework for majority vote classifiers allowing for computation of PAC Bayesian risk bounds.
☆14Updated 2 years ago
Alternatives and similar repositories for MajorityVoteBounds
Users that are interested in MajorityVoteBounds are comparing it to the libraries listed below
Sorting:
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆82Updated last year
- Code for Knowledge-Adaptation Priors based on the NeurIPS 2021 paper by Khan and Swaroop.☆16Updated 3 years ago
- ☆37Updated last year
- Github for the NIPS 2020 paper "Learning outside the black-box: at the pursuit of interpretable models"☆15Updated 2 years ago
- csl: PyTorch-based Constrained Learning☆12Updated 3 years ago
- ☆15Updated 2 years ago
- MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".☆18Updated 2 years ago
- Optimizing PAC-Bayes bounds for Stochastic Neural Networks with Gaussian weights☆27Updated 4 years ago
- Laplace Redux -- Effortless Bayesian Deep Learning☆42Updated last month
- PyTorch implementation of efficient algorithms for DRO with CVaR and Chi-Square uncertainty sets☆60Updated 2 years ago
- PAC-Bayes with Backprop - Tighter risk certificates for neural networks☆24Updated 4 years ago
- A community repository for benchmarking Bayesian methods☆11Updated 2 years ago
- Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control☆67Updated 8 months ago
- Implementation of Nonparametric Hamiltonian Monte Carlo☆12Updated 2 years ago
- General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.☆38Updated 3 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- ☆54Updated 11 months ago
- Official codebase for "Distribution-Free, Risk-Controlling Prediction Sets"☆85Updated last year
- CHOP: An optimization library based on PyTorch, with applications to adversarial examples and structured neural network training.☆77Updated last year
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆20Updated 3 years ago
- This repository contains PyTorch implementations of various random feature maps for dot product kernels.☆21Updated last year
- Code for the paper "Bayesian Neural Network Priors Revisited"☆57Updated 4 years ago
- ☆15Updated 5 years ago
- ☆10Updated 3 years ago
- Code accompanying the paper "Probabilistic Selection of Inducing Points in Sparse Gaussian Processes".☆25Updated 2 years ago
- Code for the paper: "Tensor Programs II: Neural Tangent Kernel for Any Architecture"☆105Updated 4 years ago
- [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)☆22Updated 4 years ago
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 3 years ago
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 2 years ago
- Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)☆42Updated 3 years ago