Open Access
| Issue |
EPJ Web Conf.
Volume 360, 2026
1st International Conference on “Quantum Innovations for Computing and Knowledge Systems” (QUICK’26)
|
|
|---|---|---|
| Article Number | 01001 | |
| Number of page(s) | 15 | |
| DOI | https://doi.org/10.1051/epjconf/202636001001 | |
| Published online | 23 March 2026 | |
- C. K. Chow, On optimum recognition error and reject tradeoff. IEEE Trans. Inf. Theory 16, 41–46 (1970). doi: 10.1109/TIT.1970.1054406 [Google Scholar]
- R. Herbei, M. H. Wegkamp, Classification with reject option. Can. J. Stat. 34(4), 709–721 (2006). doi: 10.1002/cjs.5550340410 [Google Scholar]
- Y. Grandvalet, A. Rakotomamonjy, J. Keshet, S. Canu, Support vector machines with a reject option. In Advances in Neural Information Processing Systems (NeurIPS) (2008). https://publications.idiap.ch/downloads/papers/2009/Grandvalet_NIPS_2008.pdf [Google Scholar]
- C. Cortes, G. DeSalvo, M. Mohri, Learning with rejection (2016). https://research.google/pubs/learning-with-rejection/ [Google Scholar]
- Y. Geifman, R. El-Yaniv, Selective classification for deep neural networks. In Advances in Neural Information Processing Systems (NeurIPS) (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/4a8423d5e91fda00bb7e46540e2b0cf1-Paper.pdf [Google Scholar]
- Y. Geifman, R. El-Yaniv, SelectiveNet: A deep neural network with an integrated reject option. arXiv arXiv:1901.09192 (2019). https://arxiv.org/abs/1901.09192 [Google Scholar]
- A. Fisch, T. Jaakkola, R. Barzilay, Calibrated selective classification. arXiv arXiv:2208.12084 (2022). https://arxiv.org/abs/2208.12084 [Google Scholar]
- A. Gangrade, A. Kag, V. Saligrama, Selective classification via one-sided prediction. arXiv arXiv:2010.07853 (2020). https://arxiv.org/abs/2010.07853 [Google Scholar]
- V. Franc, D. Prűša, V. Voráček, Optimal strategies for reject option classifiers. arXiv arXiv:2101.12523 (2021). https://arxiv.org/abs/2101.12523 [Google Scholar]
- M. Hasan et al., Survey on leveraging uncertainty estimation towards trustworthy deep neural networks: The case of reject option and post-training processing. arXiv arXiv:2304.04906 (2023). https://arxiv.org/abs/2304.04906 [Google Scholar]
- Y. Gal, Z. Ghahramani, Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. arXiv arXiv:1506.02142 (2016). https://arxiv.org/abs/1506.02142 [Google Scholar]
- J. Pawlick, E. Colbert, Q. Zhu, A game-theoretic taxonomy and survey of defensive deception for cybersecurity and privacy. ACM Comput. Surv. 52(4), 1–28 (2019). doi: 10.1145/3337772 [Google Scholar]
- A. Schlenker et al., Deceiving cyber adversaries: A game theoretic approach. In Proc. Int. Conf. Autonomous Agents and Multiagent Systems (AAMAS) (2018). https://par.nsf.gov/biblio/10050303-deceiving-cyber-adversaries-game-theoretic-approach [Google Scholar]
- S. Sengupta, A. Chowdhary, A. Sabur, A. Alshamrani, D. Huang, S. Kambhampati, A survey of moving target defenses for network security. arXiv arXiv:1905.00964 (2020). https://arxiv.org/abs/1905.00964 [Google Scholar]
- J.-H. Cho et al., Toward proactive, adaptive defense: A survey on moving target defense. arXiv arXiv:1909.08092 (2019). https://arxiv.org/abs/1909.08092 [Google Scholar]
- S. Tariq, M. B. Chhetri, S. Nepal, C. Paris, Alert fatigue in security operations centres: Research challenges and opportunities. ACM Comput. Surv. 57(9) (2025). doi: 10.1145/3723158 [Google Scholar]
- G. Apruzzese, M. Andreolini, L. Ferretti, M. Marchetti, M. Colajanni, Modeling realistic adversarial attacks against network intrusion detection systems. Digit. Threat. Res. Pract. (2021). doi: 10.1145/3469659 [Google Scholar]
- A. Łukasik, Quantum models of cognition and decision. Int. J. Parallel Emerg. Distrib. Syst. 33(3), 336–345 (2018). doi: 10.1080/17445760.2017.1410547 [Google Scholar]
- J. M. Yearsley, J. R. Busemeyer, Quantum cognition and decision theories: A tutorial. J. Math. Psychol. 74, 99–116 (2016). doi: 10.1016/j.jmp.2015.11.005 [Google Scholar]
- J. M. Arrazola, A. Delgado, B. R. Bardhan, S. Lloyd, Quantum-inspired algorithms in practice. Quantum 4, 307 (2020). doi: 10.22331/q-2020-08-13-307 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

