josephsalmon / OrganizationFilesLinks
☆13Updated 4 months ago
Alternatives and similar repositories for OrganizationFiles
Users that are interested in OrganizationFiles are comparing it to the libraries listed below
Sorting:
- Fast hyperparameter settings for non-smooth estimators:☆40Updated 2 years ago
- This code is no longer maintained. The codebase has been moved to https://github.com/scikit-learn-contrib/skglm. This repository only ser…☆18Updated 2 years ago
- Python package to fetch data from the LIBSVM website.☆20Updated last month
- El0ps: An Exact L0-Problem Solver☆10Updated 2 weeks ago
- Benchopt benchmark for Lasso☆14Updated last year
- ☆26Updated 5 months ago
- A Julia implementation of sparse Gaussian processes via path-wise doubly stochastic variational inference.☆33Updated 4 years ago
- Making your benchmark of optimization algorithms simple and open☆263Updated last month
- A generic library for linear and non-linear Gaussian smoothing problems. The code leverages JAX and implements several linearization algo…☆12Updated 6 months ago
- Source code for my PhD thesis: Backpropagation Beyond the Gradient☆20Updated 2 years ago
- Proximal optimization in pure python☆118Updated 3 years ago
- Public repo for course material on Bayesian machine learning at ENS Paris-Saclay and Univ Lille☆88Updated 3 months ago
- An ultra-lightweight JAX implementation of sparse Gaussian processes via pathwise sampling.☆22Updated 4 years ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 6 years ago
- Chirp instantaneous frequency estimation using stochastic differential equation Gaussian processes☆12Updated 7 months ago
- A Fast sketching based solver for large scale ridge regression☆17Updated last year
- PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.☆88Updated 5 months ago
- Lightweight library of stochastic gradient MCMC algorithms written in JAX.☆104Updated last year
- Gaussian Processes for Sequential Data☆18Updated 4 years ago
- Notebooks from DS3 course on practical optimization☆15Updated 4 years ago
- ☆15Updated 2 months ago
- Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design☆34Updated 4 years ago
- Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods☆22Updated last year
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 6 years ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆19Updated 3 years ago
- Differentiable and numerically stable implementation of the matrix exponential☆33Updated 4 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 6 years ago
- Code for the paper "Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluations"☆29Updated 8 months ago
- This is a collection of code samples aimed at illustrating temporal parallelization methods for sequential data.☆32Updated last year
- Riemannian Optimization Using JAX☆49Updated last year