Publications
You can also find my articles on my Google Scholar profile.
Published in The Proceedings of ICLR, 2024
In this paper, we show how deep generative models for tabular data can be constrained such that their generated samples are guaranteed to be compliant with given constraints. This is achieved by automatically parsing the constraints and transforming them into a Constraint Layer seamlessly integrated with the model.
Recommended citation: Mihaela C. Stoian, Salijona Dyrmishi, Maxime Cordy, Thomas Lukasiewicz, Eleonora Giunchiglia. How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data. In Proceedings of International Conference on Learning Representations (ICLR) 2024. https://doi.org/10.48550/arXiv.2402.04823
Published in The Proceedings of IJCAI, 2024
In this paper, we introduce PiShield, the first package ever allowing for the integration of (propositional or linear) requirements into the neural networks’ topology. PiShield guarantees compliance with these requirements, regardless of input.
Recommended citation: Mihaela C. Stoian, Alex Tatomir, Thomas Lukasiewicz, Eleonora Giunchiglia. PiShield: A PyTorch Package for Learning with Requirements. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 2024. https://doi.org/10.24963/ijcai.2024/1037
Published in The Proceedings of International Conference on Machine Learning and Cybernetics, 2024
In this paper, we adapt popular tabular deep generative models (DGMs) into adversarial DGMs and evaluate their effectiveness in generating realistic adversarial examples that conform to domain constraints.
Recommended citation: Salijona Dyrmishi, Mihaela C. Stoian, Eleonora Giunchiglia, Maxime Cordy. Deep generative models as an adversarial attack strategy for tabular machine learning. In Proceedings of International Conference on Machine Learning and Cybernetics 2024. https://doi.org/10.48550/arXiv.2409.12642
Published in International Journal of Approximate Reasoning, 2024
In this paper, we propose a novel neuro-symbolic framework able to make any neural network compliant by design to a given set of requirements over the output space expressed in full propositional logic. This framework, called CCN+, integrates the requirements into the output layer of the neural network by applying multiple inference rules that ensure compliance with the requirements and adapts the standard binary cross-entropy loss function to the requirement output layer.
Recommended citation: Eleonora Giunchiglia‚ Alex Tatomir‚ Mihaela C. Stoian, Thomas Lukasiewicz. CCN+: A neuro-symbolic framework for deep learning with requirements. International Journal of Approximate Reasoning, 171, 109-124 (2024). https://doi.org/10.1016/j.ijar.2024.109124
Published in The Proceedings of IJCAI, 2024
In this position paper, I discuss my research on integrating requirements into neural networks to guide the learning process and ultimately produce outputs that ensure the requirements’ satisfaction for two real-world applications: tabular data generation and autonomous driving.
Recommended citation: Mihaela C. Stoian. Deep Learning with Requirements in the Real World. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 2024, Doctoral Consortium. https://doi.org/10.24963/ijcai.2024/969
arXiv preprint, 2024
In this paper, we introduce ROAD-Waymo, an extensive dataset for the development and benchmarking of techniques for agent, action, location and event detection in road scenes, provided as a layer upon the (US) Waymo Open dataset. As ROAD-Waymo is compatible with the original (UK) ROAD dataset, it provides the opportunity to tackle domain adaptation between real-world road scenarios in different countries within a novel benchmark: ROAD++.
Recommended citation: Salman Khan, Izzeddin Teeti, Reza Javanmard Alitappeh, Mihaela C. Stoian, Eleonora Giunchiglia, Gurkirt Singh, Andrew Bradley, Fabio Cuzzolin. ROAD-Waymo: Action Awareness at Scale for Autonomous Driving. arXiv preprint 2411.01683, 2024 https://doi.org/10.48550/arXiv.2411.01683
Published in The Proceedings of NeSy, 2023
In this paper, we show how it is possible to define memory-efficient t-norm-based losses, allowing for exploiting t-norms for the task of event detection in autonomous driving.
Recommended citation: Mihaela C. Stoian, Eleonora Giunchiglia, Thomas Lukasiewicz. Exploiting T-norms for Deep Learning in Autonomous Driving. In Proceedings of the 17th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy), 2023. https://doi.org/10.48550/arXiv.2402.11362
Published in Machine Learning, 2023
In this paper, we introduce the ROad event Awareness Dataset with logical Requirements (ROAD-R), the first publicly available dataset for autonomous driving with requirements expressed as logical constraints. The paper was presented at IJCLR 2022, where it received the Best Student Paper Prize, and at IJCAI 2022 Workshop on Artificial Intelligence for Autonomous Driving, where it received the Best Paper Award.
Recommended citation: Eleonora Giunchiglia, Mihaela C. Stoian, Salman Khan, Fabio Cuzzolin, Thomas Lukasiewicz. ROAD-R: The Autonomous Driving Dataset with Logical Requirements. Machine Learning, 112, 3261–3291 (2023). https://doi.org/10.1007/s10994-023-06322-z
Published in The Proceedings of IJCAI, Survey Track, 2022
In this survey, we retrace works which exploit logically specified background knowledge in order to obtain neural models (i) with a better performance, (ii) able to learn from less data, and/or (iii) guaranteed to be compliant with the background knowledge itself, e.g., for safety-critical applications.
Recommended citation: Eleonora Giunchiglia, Mihaela C. Stoian, Thomas Lukasiewicz. Deep Learning with Logical Constraints. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 2022. https://doi.org/10.24963/ijcai.2022/767
Published in CVPR 2021 Workshop on 3D Vision and Robotics, 2021
In this paper we present a novel method to estimate planar reflective symmetries that efficiently handles 3D inputs by slicing the data along the height dimension and passing it sequentially to a 2D convolutional recurrent regression scheme.
Recommended citation: Mihaela C. Stoian, Tommaso Cavallari. Recurrently Estimating Reflective Symmetry Planes from Partial Pointclouds. In Conference on Computer Vision and Pattern Recognition (CVPR) Workshop on 3D Vision and Robotics, 2021. https://doi.org/10.48550/arXiv.2106.16129
Published in The Proceedings of ICASSP, 2020
In this paper we explore what factors help pretraining for low-resource automatic speech-to-text translation (AST). We show that the word error rate (WER) of the pre-trained automatic speech recognition (ASR) models is likely the best direct predictor of AST performance. Additionally, our analysis suggests that the models with better WER are transparently encoding more language-universal phonetic information in the later RNN layers, and this appears to help with AST.
Recommended citation: Mihaela C. Stoian, Sameer Bansal, Sharon Goldwater. Analyzing ASR pretraining for low-resource speech-to-text translation. In Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020. https://doi.org/10.48550/arXiv.1910.10762
