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2020 – today
- 2023
[c9]Borja Rodríguez Gálvez, Arno Blaas, Pau Rodríguez, Adam Golinski, Xavier Suau, Jason Ramapuram, Dan Busbridge, Luca Zappella:
The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning. ICML 2023: 29143-29160- [c8]Xavier Suau, Federico Danieli, T. Anderson Keller, Arno Blaas, Chen Huang, Jason Ramapuram, Dan Busbridge, Luca Zappella:
DUET: 2D Structured and Approximately Equivariant Representations. ICML 2023: 32749-32769 - [e2]Javier Antorán, Arno Blaas, Kelly Buchanan, Fan Feng, Vincent Fortuin, Sahra Ghalebikesabi, Andreas Kriegler, Ian Mason, David Rohde, Francisco J. R. Ruiz, Tobias Uelwer, Yubin Xie, Rui Yang:
Proceedings on "I Can't Believe It's Not Better: Failure Modes in the Age of Foundation Models" at NeurIPS 2023 Workshops, 16 December 2023, New Orleans, Louisiana, USA. Proceedings of Machine Learning Research 239, PMLR 2023 [contents] - [i9]Xavier Suau, Federico Danieli, T. Anderson Keller, Arno Blaas, Chen Huang, Jason Ramapuram, Dan Busbridge, Luca Zappella:
DUET: 2D Structured and Approximately Equivariant Representations. CoRR abs/2306.16058 (2023) - [i8]Borja Rodríguez Gálvez, Arno Blaas, Pau Rodríguez, Adam Golinski, Xavier Suau, Jason Ramapuram, Dan Busbridge, Luca Zappella:
The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning. CoRR abs/2307.10907 (2023) - [i7]Jonathan Crabbé, Pau Rodríguez, Vaishaal Shankar, Luca Zappella, Arno Blaas:
Robust multimodal models have outlier features and encode more concepts. CoRR abs/2310.13040 (2023) - 2022
- [j1]Andrea Patane, Arno Blaas, Luca Laurenti, Luca Cardelli, Stephen Roberts, Marta Kwiatkowska:
Adversarial Robustness Guarantees for Gaussian Processes. J. Mach. Learn. Res. 23: 146:1-146:55 (2022) - [e1]Javier Antorán, Arno Blaas, Fan Feng, Sahra Ghalebikesabi, Ian Mason, Melanie F. Pradier, David Rohde, Francisco J. R. Ruiz, Aaron Schein:
Proceedings on "I Can't Believe It's Not Better! - Understanding Deep Learning Through Empirical Falsification" at NeurIPS 2022 Workshops, 03 December 2022, New Orleans, Louisiana, USA. Proceedings of Machine Learning Research 187, PMLR 2022 [contents] - 2021
- [c7]Arno Blaas, Xavier Suau, Jason Ramapuram, Nicholas Apostoloff, Luca Zappella:
Challenges of Adversarial Image Augmentations. ICBINB@NeurIPS 2021: 9-14 - [c6]Samuel Budd, Arno Blaas, Adrienne Hoarfrost, Kia Khezeli
, Krittika D'Silva, Frank Soboczenski, Graham Mackintosh, Nicholas Chia, John Kalantari:
Prototyping CRISP: A Causal Relation and Inference Search Platform applied to Colorectal Cancer Data. LifeTech 2021: 517-521 - [c5]Xingchen Wan, Henry Kenlay, Robin Ru, Arno Blaas, Michael A. Osborne, Xiaowen Dong:
Adversarial Attacks on Graph Classifiers via Bayesian Optimisation. NeurIPS 2021: 6983-6996 - [i6]Arno Blaas, Stephen J. Roberts:
The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks. CoRR abs/2101.02689 (2021) - [i5]Andrea Patane, Arno Blaas, Luca Laurenti, Luca Cardelli, Stephen J. Roberts, Marta Kwiatkowska:
Adversarial Robustness Guarantees for Gaussian Processes. CoRR abs/2104.03180 (2021) - [i4]Kia Khezeli, Arno Blaas, Frank Soboczenski, Nicholas Chia, John Kalantari:
On Invariance Penalties for Risk Minimization. CoRR abs/2106.09777 (2021) - [i3]Xingchen Wan, Henry Kenlay, Binxin Ru, Arno Blaas, Michael A. Osborne, Xiaowen Dong:
Adversarial Attacks on Graph Classification via Bayesian Optimisation. CoRR abs/2111.02842 (2021) - [i2]Arno Blaas, Xavier Suau, Jason Ramapuram, Nicholas Apostoloff, Luca Zappella:
Challenges of Adversarial Image Augmentations. CoRR abs/2111.12427 (2021) - 2020
- [c4]Arno Blaas, Andrea Patane, Luca Laurenti, Luca Cardelli, Marta Kwiatkowska, Stephen J. Roberts:
Adversarial Robustness Guarantees for Classification with Gaussian Processes. AISTATS 2020: 3372-3382 - [c3]Binxin Ru, Adam D. Cobb, Arno Blaas, Yarin Gal:
BayesOpt Adversarial Attack. ICLR 2020
2010 – 2019
- 2019
- [c2]Arno Blaas, José María Manzano, Daniel Limón, Jan Calliess:
Localised Kinky Inference. ECC 2019: 985-992 - [i1]Arno Blaas, Luca Laurenti, Andrea Patane, Luca Cardelli, Marta Kwiatkowska, Stephen J. Roberts:
Robustness Quantification for Classification with Gaussian Processes. CoRR abs/1905.11876 (2019) - 2018
- [c1]Arno Blaas, Adam D. Cobb, Jan-Peter Calliess, Stephen J. Roberts:
Scalable Bounding of Predictive Uncertainty in Regression Problems with SLAC. SUM 2018: 373-379
Coauthor Index
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