Internal Representation of Task Rules by Recurrent Dynamics: The Importance of the Diversity of Neural Responses. - Mattia Rigotti; Daniel Ben Dayan Rubin; Xiao-Jing Wang; Stefano Fusi (Frontiers Comput. Neurosci., 2010)

Attractor concretion as a mechanism for the formation of context representations. - Mattia Rigotti; Daniel Ben Dayan Rubin; Sara E. Morrison; C. Daniel Salzman; Stefano Fusi (NeuroImage, 2010)

A Simple Derivation of a Bound on the Perceptron Margin Using Singular Value Decomposition. - Omri Barak; Mattia Rigotti (Neural Comput., 2011)

The importance of mixed selectivity in complex cognitive tasks. - Mattia Rigotti; Omri Barak; Melissa R. Warden; Xiao-Jing Wang; Nathaniel D. Daw; Earl K. Miller; Stefano Fusi (Nat., 2013)

Energy-Efficient Neuromorphic Classifiers. - Daniel Martí; Mattia Rigotti; Mingoo Seok; Stefano Fusi (Neural Comput., 2016)

Efficient ConvNets for Analog Arrays. - Malte J. Rasch; Tayfun Gokmen; Mattia Rigotti; Wilfried Haensch (CoRR, 2018)

Beyond Backprop: Online Alternating Minimization with Auxiliary Variables. - Anna Choromanska; Benjamin Cowen; Sadhana Kumaravel; Ronny Luss; Mattia Rigotti; Irina Rish; Paolo Diachille; Viatcheslav Gurev; Brian Kingsbury; Ravi Tejwani; Djallel Bouneffouf 001 (ICML, 2019)

Sobolev Independence Criterion. - Youssef Mroueh; Tom Sercu; Mattia Rigotti; Inkit Padhi; Cícero Nogueira dos Santos (NeurIPS, 2019)

Unbalanced Sobolev Descent. - Youssef Mroueh; Mattia Rigotti (NeurIPS, 2020)

Alleviating Noisy Data in Image Captioning with Cooperative Distillation. - Pierre L. Dognin; Igor Melnyk; Youssef Mroueh; Inkit Padhi; Mattia Rigotti; Jarret Ross; Yair Schiff (CoRR, 2020)

Self-correcting Q-learning. - Rong Zhu; Mattia Rigotti (AAAI, 2021)

Tabular Transformers for Modeling Multivariate Time Series. - Inkit Padhi; Yair Schiff; Igor Melnyk; Mattia Rigotti; Youssef Mroueh; Pierre L. Dognin; Jerret Ross; Ravi Nair; Erik R. Altman (ICASSP, 2021)

Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks. - Rong Zhu; Mattia Rigotti (NeurIPS, 2021)

Active Learning for Imbalanced Civil Infrastructure Data. - Thomas Frick; Diego Antognini; Mattia Rigotti; Ioana Giurgiu; Benjamin F. Grewe; Cristiano Malossi (ECCV Workshops (7), 2022)

Model-Assisted Labeling via Explainability for Visual Inspection of Civil Infrastructures. - Klára Janousková; Mattia Rigotti; Ioana Giurgiu; Cristiano Malossi (ECCV Workshops (7), 2022)

Attention-based Interpretability with Concept Transformers. - Mattia Rigotti; Christoph Miksovic; Ioana Giurgiu; Thomas Gschwind; Paolo Scotton (ICLR, 2022)

Compositional generalization through abstract representations in human and artificial neural networks. - Takuya Ito; Tim Klinger; Douglas Schultz; John Murray; Michael W. Cole; Mattia Rigotti (NeurIPS, 2022)

Cloud-Based Real-Time Molecular Screening Platform with MolFormer. - Brian Belgodere; Vijil Chenthamarakshan; Payel Das; Pierre L. Dognin; Toby Kurien; Igor Melnyk; Youssef Mroueh; Inkit Padhi; Mattia Rigotti; Jarret Ross; Yair Schiff; Richard A. Young (ECML/PKDD (6), 2022)

Estimating the Adversarial Robustness of Attributions in Text with Transformers. - Adam Ivankay; Mattia Rigotti; Ivan Girardi; Chiara Marchiori; Pascal Frossard (CoRR, 2022)

Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge. - Pierre L. Dognin; Igor Melnyk; Youssef Mroueh; Inkit Padhi; Mattia Rigotti; Jarret Ross; Yair Schiff; Richard A. Young; Brian Belgodere (J. Artif. Intell. Res., 2022)

DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications. - Adam Ivankay; Mattia Rigotti; Pascal Frossard (ACL (1), 2023)

Adaptive Conformal Regression with Jackknife+ Rescaled Scores. - Nicolas Deutschmann; Mattia Rigotti; María Rodríguez Martínez (CoRR, 2023)

Risk Assessment and Statistical Significance in the Age of Foundation Models. - Apoorva Nitsure; Youssef Mroueh; Mattia Rigotti; Kristjan H. Greenewald; Brian Belgodere; Mikhail Yurochkin; Jirí Navrátil 001; Igor Melnyk; Jerret Ross (CoRR, 2023)

Unraveling the Key Components of OOD Generalization via Diversification. - Harold Benoit; Liangze Jiang; Andrei Atanov; Oguzhan Fatih Kar; Mattia Rigotti; Amir Zamir (ICLR, 2024)

On the generalization capacity of neural networks during generic multimodal reasoning. - Takuya Ito; Soham Dan; Mattia Rigotti; James R. Kozloski; Murray Campbell (ICLR, 2024)

Risk Aware Benchmarking of Large Language Models. - Apoorva Nitsure; Youssef Mroueh; Mattia Rigotti; Kristjan H. Greenewald; Brian Belgodere; Mikhail Yurochkin; Jirí Navrátil 001; Igor Melnyk; Jarret Ross (ICML, 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)

Distributional Preference Alignment of LLMs via Optimal Transport. - Igor Melnyk; Youssef Mroueh; Brian Belgodere; Mattia Rigotti; Apoorva Nitsure; Mikhail Yurochkin; Kristjan H. Greenewald; Jirí Navrátil 001; Jarret Ross (NeurIPS, 2024)

Multivariate Stochastic Dominance via Optimal Transport and Applications to Models Benchmarking. - Gabriel Rioux; Apoorva Nitsure; Mattia Rigotti; Kristjan H. Greenewald; Youssef Mroueh (NeurIPS, 2024)

Outline-Guided Object Inpainting with Diffusion Models. - Markus Pobitzer; Filip Janicki; Mattia Rigotti; Cristiano Malossi (CoRR, 2024)

Distributional Preference Alignment of LLMs via Optimal Transport. - Igor Melnyk; Youssef Mroueh; Brian Belgodere; Mattia Rigotti; Apoorva Nitsure; Mikhail Yurochkin; Kristjan H. Greenewald; Jirí Navrátil 001; Jerret Ross (CoRR, 2024)

Adaptive Conformal Regression with Split-Jackknife+ Scores. - Nicolas Deutschmann; Mattia Rigotti; María Rodríguez Martínez (Trans. Mach. Learn. Res., 2024)

MC Layer Normalization for calibrated uncertainty in Deep Learning. - Thomas Frick; Diego Antognini; Ioana Giurgiu; Benjamin F. Grewe; Cristiano Malossi; Rong J. B. Zhu; Mattia Rigotti (Trans. Mach. Learn. Res., 2024)

Auditing and Generating Synthetic Data With Controllable Trust Trade-Offs. - Brian Belgodere; Pierre L. Dognin; Adam Ivankay; Igor Melnyk; Youssef Mroueh; Aleksandra Mojsilovic; Jirí Navrátil 001; Apoorva Nitsure; Inkit Padhi; Mattia Rigotti; Jerret Ross; Yair Schiff; et al. (IEEE J. Emerg. Sel. Topics Circuits Syst., 2024)

Towards Reliable Conversational Data Analytics. - Sihem Amer-Yahia; Jasmina Bogojeska; Roberta Facchinetti; Valeria Franceschi; Aristides Gionis; Katja Hose; Georgia Koutrika; Roger Kouyos; Matteo Lissandrini; Silviu Maniu; Katsiaryna Mirylenka; Davide Mottin; et al. (EDBT, 2025)

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)

Revisiting Group Relative Policy Optimization: Insights into On-Policy and Off-Policy Training. - Youssef Mroueh; Nicolas Dupuis; Brian Belgodere; Apoorva Nitsure; Mattia Rigotti; Kristjan H. Greenewald; Jirí Navrátil 001; Jerret Ross; Jesus Rios (CoRR, 2025)

Q-SAM2: Accurate Quantization for Segment Anything Model 2. - Nicola Farronato; Florian Scheidegger; Mattia Rigotti; Cristiano Malossi; Michele Magno; Haotong Qin (CoRR, 2025)

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