Popular GitHub Repositories for Optimisation

Must read research papers and links to tools and datasets that are related to using machine learning for compilers and systems optimisation

Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.

Nonlinear optimisation (root-finding, least squares, ...) in JAX+Equinox. https://docs.kidger.site/optimistix/

State-of-the art Automated Machine Learning python library for Tabular Data

Multi-Agent Constrained Policy Optimisation (MACPO; MAPPO-L).

A unified end-to-end learning and control framework that is able to learn a (neural) control objective function, dynamics equation, control policy, or/and optimal trajectory in a control system.

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

opinionated bayesian optimisation for stable diffusion models block merge

CS F425 Deep Learning course at BITS Pilani (Goa Campus)

Purely functional genetic algorithms for multi-objective optimisation

Real-time explainable machine learning for business optimisation

Differentiable optical models as parameterised neural networks in Jax using Zodiax

Open Source Embeddings Optimisation and Eval Framework for RAG/LLM Applications. Documentations at https://docs.vectorboard.ai/introduction

The Flatland Framework is a multi-purpose environment to tackle problems around resilient resource allocation under uncertainty. It is designed to be a flexible and method agnostic to solve a wide range of problems in the field of operations research and reinforcement learning.

NuCS is a Python constraint programming library for solving Constraint Satisfaction and Optimization Problems over finite domains

Framework for meta-optimisation in AutoML tasks

A Python package for efficient optimisation of real-space renormalization group transformations using Tensorflow.

Auto-Scikit-Longitudinal (Auto-Sklong) is an automated machine learning (AutoML) library designed to analyse longitudinal data (Classification tasks focussed as of today) using various search methods. Namely, Bayesian Optimisation via SMAC3, Asynchronous Successive Halving, Evolutionary Algorithms, and Random Search via GAMA

Predicting Indian stock prices using Stacked LSTM model. Analysing Reliance, Tata Steel, HDFC Bank, Infosys data. Data prep, EDA, hyperparameter tuning.

Python and MATLAB code for Stein Variational sampling methods