ML-Ensemble – high performance ensemble learning
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Updated
Nov 13, 2023 - Python
ML-Ensemble – high performance ensemble learning
Open-source framework for uncertainty and deep learning models in PyTorch 🌱
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning, ICLR 2020
tools for scalable and non-intrusive parameter estimation, uncertainty analysis and sensitivity analysis
Python package for tackling multi-class imbalance problems. http://www.cs.put.poznan.pl/mlango/publications/multiimbalance/
Random Forests in Apache Spark
[NeurIPS'20 Oral] DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
Results of the "Ensembles of offline changepoint detection methods" research to reproduce
Simple but high-performing method for learning a policy of test-time augmentation
SuperLearner guide: fitting models, ensembling, prediction, hyperparameters, parallelization, timing, feature selection, etc.
A repo for RLHF training and BoN over LLMs, with support for reward model ensembles.
Random forests ported to Javascript with WebAssembly and WebWorkers
Concepts used: kNN, SVM, boosting (XGBoost, Gradient boosting, Light GBM, AdaBoost, Random Forests), deep learning (CNN, LSTM), ensembles (model stacking), transfer learning.
Open-source Survival Analysis library
The PyTorch framework developed to enable my MSci thesis project titled: "Evaluating Uncertainty Estimation Methods For Deep Neural Network’s In Inverse Reinforcement Learning"
R package for automatic hyper parameter tuning and ensembles with deep learning, gradient boosting machines, and random forests. Powered by h2o.
Tensorflow slim based model training for ImageCLEF 2016 subfigure classification.
Solution for ENS - Societe Generale Challenge (1st place).
Model stacking for predictive ensembles
Utilities for comparing paleoclimate reconstruction ensembles
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