Tool for smooth git handover.

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

A repo for RLHF training and BoN over LLMs, with support for reward model ensembles.

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.

Tree-based survival analysis from scratch

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.

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.

Tool to analyze two-photon calcium imaging videos, extract neuronal activity, and identify neuronal ensembles (ONsembles and OFFsembles).

Solution for ENS - Societe Generale Challenge (1st place).