Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead. - Luca Biggio; Iason Kastanis (Frontiers Artif. Intell., 2020)

Neural Symbolic Regression that scales. - Luca Biggio; Tommaso Bendinelli; Alexander Neitz; Aurélien Lucchi; Giambattista Parascandolo (ICML, 2021)

Self-supervised pre-training on industrial time-series. - Luca Biggio; Iason Kastanis (SDS, 2021)

Uncertainty-Aware Prognosis via Deep Gaussian Process. - Luca Biggio; Alexander Wieland; Manuel Arias Chao; Iason Kastanis; Olga Fink (IEEE Access, 2021)

Uncertainty-aware Remaining Useful Life predictor. - Luca Biggio; Alexander Wieland; Manuel Arias Chao; Iason Kastanis; Olga Fink (CoRR, 2021)

Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse. - Lorenzo Noci; Sotiris Anagnostidis; Luca Biggio; Antonio Orvieto; Sidak Pal Singh; Aurélien Lucchi (NeurIPS, 2022)

Randomized Signature Layers for Signal Extraction in Time Series Data. - Enea Monzio Compagnoni; Luca Biggio; Antonio Orvieto; Thomas Hofmann 001; Josef Teichmann (CoRR, 2022)

FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control. - Dimitri von Rütte; Luca Biggio; Yannic Kilcher; Thomas Hoffman (CoRR, 2022)

Dynaformer: A Deep Learning Model for Ageing-aware Battery Discharge Prediction. - Luca Biggio; Tommaso Bendinelli; Chetan S. Kulkarni; Olga Fink (CoRR, 2022)

Cosmology from Galaxy Redshift Surveys with PointNet. - Sotiris Anagnostidis; Arne Thomsen; Tomasz Kacprzak; Tilman Tröster; Luca Biggio; Alexandre Refregier; Thomas Hofmann 001 (CoRR, 2022)

FIGARO: Controllable Music Generation using Learned and Expert Features. - Dimitri von Rütte; Luca Biggio; Yannic Kilcher; Thomas Hofmann 001 (ICLR, 2023)

Controllable Neural Symbolic Regression. - Tommaso Bendinelli; Luca Biggio; Pierre-Alexandre Kamienny (ICML, 2023)

An SDE for Modeling SAM: Theory and Insights. - Enea Monzio Compagnoni; Luca Biggio; Antonio Orvieto; Frank Norbert Proske; Hans Kersting; Aurélien Lucchi (ICML, 2023)

On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics. - Enea Monzio Compagnoni; Anna Scampicchio; Luca Biggio; Antonio Orvieto; Thomas Hofmann 001; Josef Teichmann (IJCNN, 2023)

Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers. - Sotiris Anagnostidis; Dario Pavllo; Luca Biggio; Lorenzo Noci; Aurélien Lucchi; Thomas Hofmann 001 (NeurIPS, 2023)

Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial. - Venkat Nemani; Luca Biggio; Xun Huan; Zhen Hu; Olga Fink; Anh Tran; Yan Wang; Xiaoping Du; Xiaoge Zhang 001; Chao Hu (CoRR, 2023)

Gemtelligence: Accelerating Gemstone classification with Deep Learning. - Tommaso Bendinelli; Luca Biggio; Daniel Nyfeler; Abhigyan Ghosh; Peter Tollan; Moritz Alexander Kirschmann; Olga Fink (CoRR, 2023)

Harnessing Synthetic Datasets: The Role of Shape Bias in Deep Neural Network Generalization. - Elior Benarous; Sotiris Anagnostidis; Luca Biggio; Thomas Hofmann 001 (CoRR, 2023)

Transformers beyond natural language: a theoretical and empirical investigation. - Luca Biggio (2023)

Understanding Counting in Small Transformers: The Interplay between Attention and Feed-Forward Layers. - Freya Behrens; Luca Biggio; Lenka Zdeborová (CoRR, 2024)

Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-dimensional Tokens. - Vittorio Erba; Emanuele Troiani; Luca Biggio; Antoine Maillard; Lenka Zdeborová (CoRR, 2024)

On the Bias of Next-Token Predictors Toward Systematically Inefficient Reasoning: A Shortest-Path Case Study. - Riccardo Alberghi; Elizaveta Demyanenko; Luca Biggio; Luca Saglietti (CoRR, 2025)