Scalable Bounding of Predictive Uncertainty in Regression Problems with SLAC. - Arno Blaas; Adam D. Cobb; Jan-Peter Calliess; Stephen J. Roberts (SUM, 2018)

Localised Kinky Inference. - Arno Blaas; José María Manzano; Daniel Limón; Jan Calliess (ECC, 2019)

Robustness Quantification for Classification with Gaussian Processes. - Arno Blaas; Luca Laurenti; Andrea Patane; Luca Cardelli; Marta Kwiatkowska; Stephen J. Roberts (CoRR, 2019)

Adversarial Robustness Guarantees for Classification with Gaussian Processes. - Arno Blaas; Andrea Patane; Luca Laurenti; Luca Cardelli; Marta Kwiatkowska; Stephen J. Roberts (AISTATS, 2020)

BayesOpt Adversarial Attack. - Binxin Ru; Adam D. Cobb; Arno Blaas; Yarin Gal (ICLR, 2020)

Challenges of Adversarial Image Augmentations. - Arno Blaas; Xavier Suau; Jason Ramapuram; Nicholas Apostoloff; Luca Zappella (ICBINB@NeurIPS, 2021)

Prototyping CRISP: A Causal Relation and Inference Search Platform applied to Colorectal Cancer Data. - Samuel Budd; Arno Blaas; Adrienne Hoarfrost; Kia Khezeli; Krittika D'Silva; Frank Soboczenski; Graham Mackintosh; Nicholas Chia; John Kalantari (LifeTech, 2021)

Adversarial Attacks on Graph Classifiers via Bayesian Optimisation. - Xingchen Wan; Henry Kenlay; Robin Ru; Arno Blaas; Michael A. Osborne; Xiaowen Dong 001 (NeurIPS, 2021)

The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks. - Arno Blaas; Stephen J. Roberts (CoRR, 2021)

On Invariance Penalties for Risk Minimization. - Kia Khezeli; Arno Blaas; Frank Soboczenski; Nicholas Chia; John Kalantari (CoRR, 2021)

Adversarial Attacks on Graph Classification via Bayesian Optimisation. - Xingchen Wan; Henry Kenlay; Binxin Ru; Arno Blaas; Michael A. Osborne; Xiaowen Dong 001 (CoRR, 2021)

Adversarial Robustness Guarantees for Gaussian Processes. - Andrea Patane; Arno Blaas; Luca Laurenti; Luca Cardelli; Stephen Roberts 001; Marta Kwiatkowska (J. Mach. Learn. Res., 2022)

The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning. - Borja Rodríguez Gálvez; Arno Blaas; Pau Rodríguez; Adam Golinski; Xavier Suau; Jason Ramapuram; Dan Busbridge; Luca Zappella (ICML, 2023)

DUET: 2D Structured and Approximately Equivariant Representations. - Xavier Suau; Federico Danieli; T. Anderson Keller; Arno Blaas; Chen Huang 001; Jason Ramapuram; Dan Busbridge; Luca Zappella (ICML, 2023)

Robust multimodal models have outlier features and encode more concepts. - Jonathan Crabbé; Pau Rodríguez; Vaishaal Shankar; Luca Zappella; Arno Blaas (CoRR, 2023)

Considerations for Distribution Shift Robustness of Diagnostic Models in Healthcare. - Arno Blaas; Adam Golinski; Andrew C. Miller; Luca Zappella; Jörn-Henrik Jacobsen; Christina Heinze-Deml (CoRR, 2024)

Interpreting CLIP: Insights on the Robustness to ImageNet Distribution Shifts. - Jonathan Crabbé; Pau Rodríguez; Vaishaal Shankar; Luca Zappella; Arno Blaas (Trans. Mach. Learn. Res., 2024)

Controlling Language and Diffusion Models by Transporting Activations. - Pau Rodríguez; Arno Blaas; Michal Klein; Luca Zappella; Nicholas Apostoloff; Marco Cuturi; Xavier Suau (ICLR, 2025)

End-to-end Learning of Sparse Interventions on Activations to Steer Generation. - Pau Rodríguez; Michal Klein; Eleonora Gualdoni; Arno Blaas; Luca Zappella; Marco Cuturi; Xavier Suau (CoRR, 2025)

Revisiting Uncertainty Quantification Evaluation in Language Models: Spurious Interactions with Response Length Bias Results. - Andrea Santilli; Adam Golinski; Michael Kirchhof; Federico Danieli; Arno Blaas; Miao Xiong; Luca Zappella; Sinead Williamson (CoRR, 2025)

Self-reflective Uncertainties: Do LLMs Know Their Internal Answer Distribution? - Michael Kirchhof; Luca Füger; Adam Golinski; Eeshan Gunesh Dhekane; Arno Blaas; Sinead Williamson (CoRR, 2025)