torcpy: Supporting task parallelism in Python. - Panagiotis E. Hadjidoukas; Andrea Bartezzaghi; Florian Scheidegger; Roxana Istrate; Costas Bekas; A. Cristiano I. Malossi (SoftwareX, 2020)

Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE). - Jason Tsay; Andrea Bartezzaghi; Aleke Nolte; A. Cristiano I. Malossi (CoRR, 2022)

Improving Low-Resource Question Answering using Active Learning in Multiple Stages. - Maximilian Schmidt; Andrea Bartezzaghi; Jasmina Bogojeska; A. Cristiano I. Malossi; Thang Vu (CoRR, 2022)

Prompting-based Synthetic Data Generation for Few-Shot Question Answering. - Maximilian Schmidt; Andrea Bartezzaghi; Ngoc Thang Vu (LREC/COLING, 2024)

Combining Data Generation and Active Learning for Low-Resource Question Answering. - Maximilian Kimmich; Andrea Bartezzaghi; Jasmina Bogojeska; Cristiano Malossi; Ngoc Thang Vu (ICANN (7), 2024)

Probabilistic Feature Matching for Fast Scalable Visual Prompting. - Thomas Frick; Cezary Skura; Filip Janicki; Roy Assaf; Niccolò Avogaro; Daniel Caraballo; Yagmur Gizem Cinar; Brown Ebouky; Ioana Giurgiu; Takayuki Katsuki; Piotr Kluska; Cristiano Malossi; et al. (IJCAI, 2024)

Show or Tell? Effectively prompting Vision-Language Models for semantic segmentation. - Niccolò Avogaro; Thomas Frick; Mattia Rigotti; Andrea Bartezzaghi; Filip Janicki; A. Cristiano I. Malossi; Konrad Schindler; Roy Assaf (Trans. Mach. Learn. Res., 2025)

VP Lab: a PEFT-Enabled Visual Prompting Laboratory for Semantic Segmentation. - Niccolò Avogaro; Thomas Frick; Yagmur Gizem Cinar; Daniel Caraballo; Cezary Skura; Filip Janicki; Piotr Kluska; Brown Ebouky; Nicola Farronato; Florian Scheidegger; Cristiano Malossi; Konrad Schindler; et al. (CoRR, 2025)

Eliciting Reasoning in Language Models with Cognitive Tools. - Brown Ebouky; Andrea Bartezzaghi; Mattia Rigotti (CoRR, 2025)