default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDSafeAI@AAAI 2023: Washington, DC, USA
- Gabriel Pedroza, Xiaowei Huang, Xin Cynthia Chen, Andreas Theodorou, José Hernández-Orallo, Mauricio Castillo-Effen, Richard Mallah, John A. McDermid:
Proceedings of the Workshop on Artificial Intelligence Safety 2023 (SafeAI 2023) co-located with the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023), Washington DC, USA, February 13-14, 2023. CEUR Workshop Proceedings 3381, CEUR-WS.org 2023
Session 1: AI/ML Learning, Explainability, Accuracy and Policy Alignment
- Peter Barnett, Rachel Freedman, Justin Svegliato, Stuart Russell:
Active Reward Learning from Multiple Teachers. - Saaduddin Mahmud, Sandhya Saisubramanian, Shlomo Zilberstein:
REVEALE: Reward Verification and Learning Using Explanations. - Maxime Fuccellaro, Laurent Simon, Akka Zemmari:
A Robust Drift Detection Algorithm with High Accuracy and Low False Positives Rate.
Session 2: Short Presentations - Safety Assessment of AI-enabled systems
- Salah Ghamizi, Maxime Cordy, Mike Papadakis, Yves Le Traon:
On Evaluating Adversarial Robustness of Chest X-ray Classification. - Fateh Kaakai, Paul-Marie Raffi:
Towards Multi-timescale Online Monitoring of AI Models. - Chenyang Yang, Rachel A. Brower-Sinning, Grace A. Lewis, Christian Kästner, Tongshuang Wu:
Capabilities for Better ML Engineering.
Session 3: AI/ML for Safety Critical Applications: Assurance Cases and Datasets
- Chiara Picardi, Richard Hawkins, Colin Paterson, Ibrahim Habli:
Transfer Assurance for Machine Learning in Autonomous Systems. - Václav Divis, Tobias Schuster, Marek Hrúz:
Domain-centric ADAS Datasets. - Maryam Bagheri, Josephine Lamp, Xugui Zhou, Lu Feng, Homa Alemzadeh:
Towards Developing Safety Assurance Cases for Learning-Enabled Medical Cyber-Physical Systems.
Session 4 - Short Presentations: ML/DL Robustness: GAM and Attack Detection
- Weimin Zhao, Sanaa A. Alwidian, Qusay H. Mahmoud:
Evaluation of GAN Architectures for Adversarial Robustness of Convolution Classifier. - Khondoker Murad Hossain, Tim Oates:
Backdoor Attack Detection in Computer Vision by Applying Matrix Factorization on the Weights of Deep Networks.
Session 5 - AI Safety Assessment: Failure-Cause Analysis, Assurance, Verification
- Nikiforos Pittaras, Sean McGregor:
A Taxonomic System for Failure Cause Analysis of Open Source AI Incidents. - Felippe Schmoeller Roza, Simon Hadwiger, Ingo Thon, Karsten Roscher:
Towards Safety Assurance of Uncertainty-Aware Reinforcement Learning Agents. - Valency Oscar Colaco, Simin Nadjm-Tehrani:
Formal Verification of Tree Ensembles against Real-World Composite Geometric Perturbations.
Session 6: AI Robustness: Adversarial and Attacks Learning
- Matthias König, Annelot W. Bosman, Holger H. Hoos, Jan N. van Rijn:
Critically Assessing the State of the Art in CPU-based Local Robustness Verification. - Soumyadeep Pal, Ren Wang, Yuguang Yao, Sijia Liu:
Towards Understanding How Self-training Tolerates Data Backdoor Poisoning. - Yize Li, Pu Zhao, Xue Lin, Bhavya Kailkhura, Ryan A. Goldhahn:
Less is More: Data Pruning for Faster Adversarial Training. - Teddy Ferdinan, Jan Kocon:
Personalized Models Resistant to Malicious Attacks for Human-centered Trusted AI.
Session 7: AI Robustness: Deep Reinforcement Learning
- Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi, Vineet Gundecha, Sahand Ghorbanpour, Alexander Shmakov, Ricardo Luna Gutierrez, Antonio Guillen, Avisek Naug:
Robustness with Black-Box Adversarial Attack using Reinforcement Learning. - Stephen Casper, Dylan Hadfield-Menell, Gabriel Kreiman:
White-Box Adversarial Policies in Deep Reinforcement Learning. - Chen Chen, Haibo Hong, Mande Xie, Jun Shao, Tao Xiang:
Bab: A novel algorithm for training clean model based on poisoned data. - Sumanta Dey, Pallab Dasgupta, Soumyajit Dey:
Safe Reinforcement Learning through Phasic Safety-Oriented Policy Optimization.
Session 8 - Short Presentations: OoD Detection and Uncertainty for ML/DL Safety
- Fabio Arnez, Ansgar Radermacher, François Terrier:
Out-of-Distribution Detection Using Deep Neural Network Latent Space Uncertainty. - Tian Tan, Carlos Huertas, Qi Zhao:
Efficient and Effective Uncertainty Quantification in Gradient Boosting via Cyclical Gradient MCMC. - Dirk Eilers, Simon Burton, Felippe Schmoeller da Roza, Karsten Roscher:
Safety Assurance with Ensemble-based Uncertainty Estimation and overlapping alternative Predictions in Reinforcement Learning.
Session 9 - Short Presentations: Methods and Techniques for AI/ML Safety Assessment
- Juliette Mattioli, Henri Sohier, Agnès Delaborde, Gabriel Pedroza, Kahina Amokrane-Ferka, Afef Awadid, Zakaria Chihani, Souhaiel Khalfaoui:
Towards a holistic approach for AI trustworthiness assessment based upon aids for multi-criteria aggregation. - Alberto Huertas Celdrán, Jan Kreischer
, Melike Demirci, Joel Leupp, Pedro Miguel Sánchez Sánchez, Muriel Figueredo Franco, Gérôme Bovet, Gregorio Martínez Pérez, Burkhard Stiller:
A Framework Quantifying Trustworthiness of Supervised Machine and Deep Learning Models. - Axel Brando, Isabel Serra, Enrico Mezzetti, Francisco J. Cazorla, Jaume Abella:
Standardizing the Probabilistic Sources of Uncertainty for the sake of Safety Deep Learning.
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-11-15 19:43 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
