gianluigilopardo

anchors_text_theory

Code for the paper "A Sea of Words: An In-Depth Analysis of Anchors for Text Data", AISTATS 2023

anchors_vs_lime_text

Code for the paper "Comparing Feature Importance and Rule Extraction for Interpretability on Text Data", XAIE @ ICPR 2022

AnthropicEconomicIndex

Anthropic Economix Index

attention_meets_xai

Code for the paper "Attention Meets Post-hoc Interpretability: A Mathematical Perspective", ICML 2024

covid-19

Stima giornaliera del valore R(t) del COVID-19 nelle regioni italiane

fred

Faithful and Robust Local Interpretability for Textual Predictions

hacktheact

Hack the Act! is a RAG-based chatbot designed to demystify the European Union AI Act

HELOC-Credit-Approval

This notebook is ispired by the AIX360 HELOC Credit Approval Tutorial, which shows different explainability methods for a credit approval process. Here XGBoost is used for classification, achieving better accuracy than most of the models used in that notebook. Then, feature importance methods are shown, to be compared with the Data Scientist explanations methods provided in the above notebook. The first ones come directly with XGBoost and the other is based on SHAP.

Open-World-Recognition

The project's goal is to get familiar with cutting-edge models capable of acting in an open world, incremental learning approaches in image classification and open set strategies

smace

Code for the paper "SMACE: A New Method for the Interpretability of Composite Decision Systems", ECML 2022

gianluigilopardo/attention_meets_xai

Code for the paper "Attention Meets Post-hoc Interpretability: A Mathematical Perspective", ICML 2024

gianluigilopardo/smace

Code for the paper "SMACE: A New Method for the Interpretability of Composite Decision Systems", ECML 2022

gianluigilopardo/anchors_text_theory

Code for the paper "A Sea of Words: An In-Depth Analysis of Anchors for Text Data", AISTATS 2023

gianluigilopardo/fred

Faithful and Robust Local Interpretability for Textual Predictions

gianluigilopardo/Open-World-Recognition

The project's goal is to get familiar with cutting-edge models capable of acting in an open world, incremental learning approaches in image classification and open set strategies

gianluigilopardo/anchors_vs_lime_text

Code for the paper "Comparing Feature Importance and Rule Extraction for Interpretability on Text Data", XAIE @ ICPR 2022

gianluigilopardo/covid-19

Stima giornaliera del valore R(t) del COVID-19 nelle regioni italiane

gianluigilopardo/HELOC-Credit-Approval

This notebook is ispired by the AIX360 HELOC Credit Approval Tutorial, which shows different explainability methods for a credit approval process. Here XGBoost is used for classification, achieving better accuracy than most of the models used in that notebook. Then, feature importance methods are shown, to be compared with the Data Scientist explanations methods provided in the above notebook. The first ones come directly with XGBoost and the other is based on SHAP.

gianluigilopardo/Absenteeism_prediction

Prediction of absenteeism at work, with machine learning models for classification.

gianluigilopardo/earnings_data_fetch

Python code for fetching and collecting earnings call transcripts and stock values.

gianluigilopardo/hacktheact

Hack the Act! is a RAG-based chatbot designed to demystify the European Union AI Act

gianluigilopardo/Reinforcement-Learning

My solution of a simple Reinforcement learning problem

gianluigilopardo/CBC-ratings_prediction

The purpose of this paper is to analyze how and how much a film's attributes affect its rating, using several regression techniques.

gianluigilopardo/Financial-articles_analysis

(Part of) a project for the Business intelligence class. The goal is to analyze a dataset of financial articles and apply machine learning technique to extract useful information.

gianluigilopardo/Statistical-Models

A series of statistical models applied to different case studies

gianluigilopardo/AnthropicEconomicIndex

Anthropic Economix Index

gianluigilopardo/gianluigilopardo

gianluigilopardo/pcs_project

Scientific programming and computing project carried out in MATLAB.

gianluigilopardo/Pilgrim-dropout_prediction

The purpose of this work is to predict which customers are about to leave the bank. To do this, the main classification algorithms will be used to predict whether a customer from 1999 will still be a customer in 2000 or not.

gianluigilopardo/Transfer-Learning

The aim of this project is to apply and explore Transfer Learning. The dataset used is Caltech101, the neural network used for the first part of the project is AlexNet.

gianluigilopardo/xai_text_practice

gianluigilopardo/etherscan_transaction

Fetch token transaction data from Etherscan

gianluigilopardo/German-Credit-Data_credit_risk

This project is about the analysis of credit risks of German Credit Data. Different classification models and preprocessing methods are used and compared.

gianluigilopardo/positional-and-semantic-attention