I am Diego Antognini, a final-year Ph.D. candidate in Computer Science at Swiss Federal Institute of Technology in Lausanne (EPFL). I work in the Artificial Intelligence Laboratory (LIA) under the supervision of Professor Boi Faltings.
I develop interpretable models that generate personalized and actionable textual explanations. I supervised 30+ B./M.Sc. projects/theses and assessed 50+ student projects. I offer consulting services in natural language processing, recommender systems, and machine learning. You can have a quick overview of myself by downloading my résumé.
Additionnally, I give talks (e.g., NLP Meetup in Zürich, where I presented one of my past work here) and participate to challenges with students. We won a $10k prize at IARPA 2018) and I talk about it (in French) at the radio on Couleur 3.

I am in the job market. I am looking for exciting opportunities in research! If there is an opening in your organization for which I may be a suitable match, please do get in touch.

On this website, I present some publications I've been working on and some (and old) of the most exciting projects. If you have any questions, would like to see others projects (including OpenGL, realistic image synthesis, web frameworks) or you should be unable to find something, feel free to contact me here.


Here are some of my publications. You can also consult my Google scholar profile. Don't hesitate to contact me if you have any questions!

Interacting with Explanations through Critiquing (T-RECS) Paper Paper Video (Chrome only)
Diego Antognini, Claudiu Musat, Boi Faltings
2021, IJCAI (acceptance rate: 13.9%)

TL;DR: How to extract explanations significantly preferred by humans over those produced by state-of-the-art models and make them actionable; users interact with them iteratively to improve the recommendation?
Fast Multi-Step Critiquing for VAE-based Recommender Systems (M&Ms-VAE) Paper Paper Video
Diego Antognini, Boi Faltings
2021, RecSys (acceptance rate: 18.4%)

TL;DR: Fast critiquing generalized for variational autoencoders and up to 26x faster and 20% higher success rate than state-of-the-art models. The key is to model the problem using multi-modal VAE and weak supervision.
Multi-Step Critiquing User Interface for Recommender Systems Paper Paper Video
Diana, Petrescu*, Diego Antognini*, Boi Faltings
2021, RecSys Demo

TL;DR: We propose and demonstrate a new way of interacting with recommender systems.
Multi-Dimensional Explanation of Target Variables from Documents (MTM) Paper Video
Diego Antognini, Claudiu Musat, Boi Faltings
2021, AAAI (acceptance rate: 21%)

TL;DR: One model to extract interpretable, meaningful, and coherent multi-faceted rationales for multi-task text classficiation problems.
Rationalization through Concepts Paper Video
Diego Antognini, Boi Faltings
2021, ACL Findings (acceptance rate: 21.3% (main) + 14.9% (findings))

TL;DR: Generalization of MTM: how to extract interpretable multi-faceted concepts (i.e., rationales) for single-task classification problems.
Addressing Fairness in Classification with a Model-Agnostic Multi-Objective Algorithm Paper Video
Kirtan Padh, Diego Antognini, Emma L. Glaude, Boi Faltings, Claudiu Musat
2021, UAI (acceptance rate: 26.5%)

TL;DR: A novel differentiable relaxation that approximates fairness notions, and a novel model-agnostic multi-objective architecture that optimizes multiple fairness notions and sensitive attributes.
Multi-Gradient Descent for Multi-Objective Recommender Systems Paper
Nikola Milojkovic, Diego Antognini, Giancarlo Bergamin, Boi Faltings, Claudiu Musat
2020, AAAI Workshop on Interactive and Conversational Recommendation Systems (WICRS)

TL;DR: An efficient stochastic multi-gradient descent approach for multi-objective recommender system.
HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset Paper
Diego Antognini, Boi Faltings
2020, LREC

TL;DR: A new dataset with 50 million hotel reviews with meta-attributes, user information, and multiple rated dimensions.
Recommending Burgers based on Pizza Preferences: Addressing Data Sparsity with a Product of Experts Paper
Martin Milenkoski, Diego Antognini, Boi Faltings
2021, CoRR

TL;DR: We tackle data sparsity and create recommendations in domains with limited knowledge about the user preferences.
Modeling Online Behavior in Recommender Systems: The Importance of Temporal Context Paper
Milena Filipovic*, Blagoj Mitrevski*, Diego Antognini, Emma L. Glaude, Boi Faltings, Claudiu Musat
2021, CoRR

TL;DR: Omitting temporal context when evaluating recommender systems leads to false confidence. We propose an evaluation protocol and a training procedure model-agnostic to incorporate temporal context.
Momentum-based Gradient Methods in Multi-objective Recommender Systems Paper
Blagoj Mitrevski*, Milena Filipovic*, Diego Antognini, Emma L. Glaude, Boi Faltings, Claudiu Musat
2021, CoRR

TL;DR: A coordinated multi-objective optimization method where each is optimized via an Adam-like algorithm.
GameWikiSum: a Novel Large Multi-Document Summarization Dataset Paper
Diego Antognini, Boi Faltings
2020, LREC

TL;DR: A non-news domain-specific dataset for multi-document summarization, which is one 100x larger than commonly used datasets.
Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization Paper
Diego Antognini, Boi Faltings
2019, EMNLP Workshop on New Frontiers in Summarization

TL;DR: How to leverage universal and domain-sepcific sentence embeddings using a graph structure for multi-document summarization.
Dataset Construction via Attention for Aspect Term Extraction with Distant Supervision Paper
Athanasios Giannakopoulos*, Diego Antognini*, Claudiu Musat, Andreea Hossmann and Michael Baeriswyl
2017, ICDM Workshop on Sentiment Elicitation from Natural Text for Information Retrieval and Extraction (SENTIRE)

TL;DR: How to use large corpora to better extract aspect terms using distant supervision.


From Relation Extraction to Knowledge Graphs - M.Sc. thesis

My master thesis at Iprova in the domains of machine learning and natural language processing. View more


Scalable decentralized system that aggregates secondary storage devices in a cluster with the aim of supporting parallel scans of data stored across them. View more

Image classification

Classifier that recognizes the object present in an image using advanced models. The objects could be classifying as horse, airplane, car or other. View more

Pattern classification and machine learning project 1

Project about regression and classification using linear models. One dataset per task is given without any information. View more

Optimized flocking algorithm for e-pucks

Implement, test, analyse and optimize a flocking algorithm for e-pucks. The robots should avoid obstacles within the arena while retaining the collective formation. Work in a multidisciplinary team! View more

NeoBrain - B.Sc. thesis

A research project about optimizing neuronal activity maps treatment using massively parallel technologies. View more

Facial recognition among profiles

Detect if a person has sunglasses using a set of profile pictures of different persons. Each one of them has pictures with different head positions, emotions and with/without sunglasses. View more

Recommender System challenge

Third task of the challenge of European Semantic Web Conference on a Top-N recommendation of books (ESWC-14 Challenge). Github Report

Social Recommendation System

Recommender systems for events based on user’s data and Facebook profile. View more


Realisation of a complete Poker Texas Hold'em game with an artificial intelligence. View more

Starfighter 4K

Shoot'em up game using the movement recognitions with Kinect and Wiimotes for the inclination and the shoot of the spaceships. View more


Multiple mini-projects for learning about GPGPU technologies, mainly CUDA. View more


A movie directory with heavy database background using real data from IMDb. View more


Planetarium software showing a current view of the sky at the current location. View more


If you have any questions, or you would like to get in touch with me, feel free to contact me in one of the following ways :