A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.

Microarchitectural exploitation and other hardware attacks.

State-of-the-art neural cardinality estimators for join queries

TIP2022 Adaptive Boosting (AdaBoost) for Domain Adaptation ? 🤷‍♀️ Why not ! 🙆‍♀️

Reinforced Data Sampling

Implémentation d'un modèle de scoring (OpenClassrooms | Data Scientist | Projet 7)

Adaptive data sampling and transmission in a wireless sensor node as a function of energy reserves

Generating realistic test data or simulating load with authentic, dynamic data using the Gatling framework and JavaFaker

Some nlp utils file I write to reuse

A Python package for flexible subset selection for data visualization.

This project aims to analyze the citation network of arXiv papers. We use Python to clean the data and create a Neo4j network to visualize and analyze the citation relationships between arXiv papers.

Here is Task 5: Credit card fraud detection using machine learning, for my data science internship with Codsoft

Process of data preparaton in R.

Code and Data for paper: Variation across Scales: Measurement Fidelity under Twitter Data Sampling (ICWSM '20)

This repository contains experiments on data wrangling techniques, focusing on methods for handling missing values, filtering, aggregation, and more.

A method for sampling a balanced dataset from biased signals by leveraging statistical distributions derived from the data.

Here is Task 5: Credit card fraud detection using machine learning, for my data science internship with Codsoft