@inproceedings{sucholutsky2023onc,

title = {On the Informativeness of Supervision Signals},

author = {Ilia Sucholutsky and Ruairidh McLennan Battleday and Katherine M. Collins and Raja Marjieh and Joshua Peterson and Pulkit Singh and Umang Bhatt and Nori Jacoby and Adrian Weller and Thomas L. Griffiths},

url = {https://openreview.net/forum?id=JHcj9gcks3},

year  = {2023},

date = {2023-01-01},

booktitle = {The 39th Conference on Uncertainty in Artificial Intelligence},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rahimian2023private,

title = {Private and Collaborative Kaplan-Meier Estimators},

author = {Shadi Rahimian and Raouf Kerkouche and Ina Kurth and Mario Fritz},

year  = {2023},

date = {2023-01-01},

booktitle = {Proceedings of the 23rd Workshop on Privacy in the Electronic Society},

pages = {212–241},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{fahes2023poda,

title = {PØDA: Prompt-driven Zero-shot Domain Adaptation},

author = {Mohammad Fahes and Tuan-Hung Vu and Andrei Bursuc and Patrick Pérez and Raoul Charette},

year  = {2023},

date = {2023-01-01},

booktitle = {ICCV},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-031-43148-7_10,

title = {SynthCap: Augmenting Transformers with Synthetic Data for Image Captioning},

author = {Davide Caffagni and Manuele Barraco and Marcella Cornia and Lorenzo Baraldi and Rita Cucchiara},

url = {https://doi.org/10.1007/978-3-031-43148-7_10},

doi = {10.1007/978-3-031-43148-7_10},

isbn = {978-3-031-43147-0},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Image Analysis and Processing – ICIAP 2023: 22nd International Conference, ICIAP 2023, Udine, Italy, September 11–15, 2023, Proceedings, Part I},

pages = {112–123},

publisher = {Springer-Verlag},

address = {Udine, Italy},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{souibgui2023text,

title = {Text-DIAE: a self-supervised degradation invariant autoencoder for text recognition and document enhancement},

author = {Mohamed Ali Souibgui and Sanket Biswas and Andres Mafla and Ali Furkan Biten and Alicia Fornés and Yousri Kessentini and Josep Lladós and Lluis Gomez and Dimosthenis Karatzas},

year  = {2023},

date = {2023-01-01},

booktitle = {proceedings of the AAAI conference on artificial intelligence},

volume = {37},

number = {2},

pages = {2330–2338},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{biswas2023tight,

title = {Tight Differential Privacy Guarantees for the Shuffle Model with k-Randomized Response},

author = {Sayan Biswas and Kangsoo Jung and Catuscia Palamidessi},

year  = {2023},

date = {2023-01-01},

booktitle = {International Symposium on Foundations and Practice of Security},

pages = {440–458},

organization = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{oneto2023towards,

title = {Towards Randomized Algorithms and Models that We Can Trust: a Theoretical Perspective},

author = {Luca Oneto and Sandro Ridella and Davide Anguita},

year  = {2023},

date = {2023-01-01},

booktitle = {31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN},

pages = {4–6},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{277098,

title = {ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models},

author = {Yugeng Liu and Rui Wen and Xinlei He and Ahmed Salem and Zhikun Zhang and Michael Backes and Emiliano De Cristofaro and Mario Fritz and Yang Zhang},

url = {https://www.usenix.org/conference/usenixsecurity22/presentation/liu-yugeng},

isbn = {978-1-939133-31-1},

year  = {2022},

date = {2022-08-01},

booktitle = {31st USENIX Security Symposium (USENIX Security 22)},

pages = {4525–4542},

publisher = {USENIX Association},

address = {Boston, MA},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{pmlr-v162-poklukar22a,

title = {Geometric Multimodal Contrastive Representation Learning},

author = {Petra Poklukar and Miguel Vasco and Hang Yin and Francisco S. Melo and Ana Paiva and Danica Kragic},

editor = {Kamalika Chaudhuri and Stefanie Jegelka and Le Song and Csaba Szepesvari and Gang Niu and Sivan Sabato},

url = {https://proceedings.mlr.press/v162/poklukar22a.html},

year  = {2022},

date = {2022-07-01},

booktitle = {Proceedings of the 39th International Conference on Machine Learning},

volume = {162},

pages = {17782–17800},

publisher = {PMLR},

series = {Proceedings of Machine Learning Research},

abstract = {Learning representations of multimodal data that are both informative and robust to missing modalities at test time remains a challenging problem due to the inherent heterogeneity of data obtained from different channels. To address it, we present a novel Geometric Multimodal Contrastive (GMC) representation learning method consisting of two main components: i) a two-level architecture consisting of modality-specific base encoders, allowing to process an arbitrary number of modalities to an intermediate representation of fixed dimensionality, and a shared projection head, mapping the intermediate representations to a latent representation space; ii) a multimodal contrastive loss function that encourages the geometric alignment of the learned representations. We experimentally demonstrate that GMC representations are semantically rich and achieve state-of-the-art performance with missing modality information on three different learning problems including prediction and reinforcement learning tasks.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{9897813,

title = {Transient-Fault-Aware Design and Training to Enhance DNNs Reliability with Zero-Overhead},

author = {Niccolò Cavagnero and Fernando Dos Santos and Marco Ciccone and Giuseppe Averta and Tatiana Tommasi and Paolo Rech},

doi = {10.1109/IOLTS56730.2022.9897813},

year  = {2022},

date = {2022-01-01},

booktitle = {2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS)},

pages = {1-7},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{NEURIPS2022_85b6841e,

title = {3DOS: Towards 3D Open Set Learning - Benchmarking and Understanding Semantic Novelty Detection on Point Clouds},

author = {Antonio Alliegro and Francesco Cappio Borlino and Tatiana Tommasi},

editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},

url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/85b6841eaf79327b1777f9e64af3835d-Paper-Datasets_and_Benchmarks.pdf},

year  = {2022},

date = {2022-01-01},

booktitle = {Advances in Neural Information Processing Systems},

volume = {35},

pages = {21228–21240},

publisher = {Curran Associates, Inc.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{NEURIPS2022_9a1dab89,

title = {Group Meritocratic Fairness in Linear Contextual Bandits},

author = {Riccardo Grazzi and Arya Akhavan and John IF Falk and Leonardo Cella and Massimiliano Pontil},

editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},

url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/9a1dab894ce96cb8339c2fadd85a100b-Paper-Conference.pdf},

year  = {2022},

date = {2022-01-01},

booktitle = {Advances in Neural Information Processing Systems},

volume = {35},

pages = {24392–24404},

publisher = {Curran Associates, Inc.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{NEURIPS2022_91ffdc5e,

title = {Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples},

author = {Maura Pintor and Luca Demetrio and Angelo Sotgiu and Ambra Demontis and Nicholas Carlini and Battista Biggio and Fabio Roli},

editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},

url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/91ffdc5e2f12436d99914418e38d0a09-Paper-Conference.pdf},

year  = {2022},

date = {2022-01-01},

booktitle = {Advances in Neural Information Processing Systems},

volume = {35},

pages = {23063–23076},

publisher = {Curran Associates, Inc.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}