A ProM Operational Support Provider for Predictive Monitoring of Business Processes. - Marco Federici; Williams Rizzi; Chiara Di Francescomarino; Marlon Dumas; Chiara Ghidini; Fabrizio Maria Maggi; Irene Teinemaa (BPM (Demos), 2015)

Predictive Business Process Monitoring Framework with Hyperparameter Optimization. - Chiara Di Francescomarino; Marlon Dumas; Marco Federici; Chiara Ghidini; Fabrizio Maria Maggi; Williams Rizzi (CAiSE, 2016)

Towards Unsupervised Approaches For Aspects Extraction. - Marco Federici; Mauro Dragoni (EMSA-RMed@ESWC, 2016)

A Knowledge-Based Approach for Aspect-Based Opinion Mining. - Marco Federici; Mauro Dragoni (SemWebEval@ESWC, 2016)

A Branching Strategy For Unsupervised Aspect-based Sentiment Analysis. - Marco Federici; Mauro Dragoni (EMSASW@ESWC, 2017)

Aspect-Based Opinion Mining Using Knowledge Bases. - Marco Federici; Mauro Dragoni (SemWebEval@ESWC, 2017)

The KABSA System at ESWC-2018 Challenge on Semantic Sentiment Analysis. - Marco Federici; Mauro Dragoni (SemWebEval@ESWC, 2018)

Genetic algorithms for hyperparameter optimization in predictive business process monitoring. - Chiara Di Francescomarino; Marlon Dumas; Marco Federici; Chiara Ghidini; Fabrizio Maria Maggi; Williams Rizzi; Luca Simonetto (Inf. Syst., 2018)

ReUS: a Real-time Unsupervised System For Monitoring Opinion Streams. - Mauro Dragoni; Marco Federici; Andi Rexha (Cogn. Comput., 2019)

An unsupervised aspect extraction strategy for monitoring real-time reviews stream. - Mauro Dragoni; Marco Federici; Andi Rexha (Inf. Process. Manag., 2019)

Learning Robust Representations via Multi-View Information Bottleneck. - Marco Federici; Anjan Dutta 001; Patrick Forré; Nate Kushman; Zeynep Akata (ICLR, 2020)

An Information-theoretic Approach to Distribution Shifts. - Marco Federici; Ryota Tomioka; Patrick Forré (NeurIPS, 2021)

A Bayesian Approach to Invariant Deep Neural Networks. - Nikolaos Mourdoukoutas; Marco Federici; Georges Pantalos; Mark van der Wilk; Vincent Fortuin (CoRR, 2021)

Towards Lightweight Controllable Audio Synthesis with Conditional Implicit Neural Representations. - Jan Zuiderveld; Marco Federici; Erik J. Bekkers (CoRR, 2021)

Compositional Mixture Representations for Vision and Text. - Stephan Alaniz; Marco Federici; Zeynep Akata (CVPR Workshops, 2022)

Bridge the Inference Gaps of Neural Processes via Expectation Maximization. - Qi Wang; Marco Federici; Herke van Hoof (ICLR, 2023)

Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics. - Marloes Arts; Victor Garcia Satorras; Chin-Wei Huang; Daniel Zügner; Marco Federici; Cecilia Clementi; Frank Noé; Robert Pinsler; Rianne van den Berg (CoRR, 2023)

On the Effectiveness of Hybrid Mutual Information Estimation. - Marco Federici; David Ruhe; Patrick Forré (CoRR, 2023)

Simulation-based Inference with the Generalized Kullback-Leibler Divergence. - Benjamin Kurt Miller; Marco Federici; Christoph Weniger; Patrick Forré (CoRR, 2023)

Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck. - Marco Federici; Patrick Forré; Ryota Tomioka; Bastiaan S. Veeling (ICLR, 2024)

Efficient LLM Inference using Dynamic Input Pruning and Cache-Aware Masking. - Marco Federici; Davide Belli; Mart van Baalen; Amir Jalalirad; Andrii Skliar; Bence Major; Markus Nagel; Paul N. Whatmough (CoRR, 2024)

HadaNorm: Diffusion Transformer Quantization through Mean-Centered Transformations. - Marco Federici; Riccardo Del Chiaro; Boris van Breugel; Paul N. Whatmough; Markus Nagel (CoRR, 2025)