| Coding for deletion channels with multiple traces M Abroshan, R Venkataramanan, L Dolecek, AG i Fabregas 2019 IEEE International Symposium on Information Theory (ISIT), 1372-1376, 2019 | 34 | 2019 |
| Improving fairness and privacy in selection problems MM Khalili, X Zhang, M Abroshan, S Sojoudi Proceedings of the AAAI Conference on Artificial Intelligence 35 (9), 8092-8100, 2021 | 24 | 2021 |
| An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift G Aminian*, M Abroshan*, MM Khalili, L Toni, M Rodrigues International Conference on Artificial Intelligence and Statistics, 7433-7449, 2022 | 20 | 2022 |
| Efficient systematic encoding of non-binary VT codes M Abroshan, R Venkataramanan, AGI Fabregas 2018 IEEE International Symposium on Information Theory (ISIT), 91-95, 2018 | 18 | 2018 |
| Coding for segmented edit channels M Abroshan, R Venkataramanan, AG i Fàbregas IEEE Transactions on Information Theory 64 (4), 3086-3098, 2017 | 17 | 2017 |
| Fair sequential selection using supervised learning models MM Khalili, X Zhang, M Abroshan Advances in Neural Information Processing Systems 34, 28144-28155, 2021 | 13 | 2021 |
| Opportunities for machine learning to transform care for people with cystic fibrosis M Abroshan, AM Alaa, O Rayner, M van der Schaar Journal of Cystic Fibrosis 19 (1), 6-8, 2020 | 7 | 2020 |
| Counterfactual Fairness in Synthetic Data Generation M Abroshan, MM Khalili, A Elliott NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research, 2022 | 6 | 2022 |
| Zero error coordination M Abroshan, A Gohari, S Jaggi 2015 IEEE Information Theory Workshop-Fall (ITW), 202-206, 2015 | 6 | 2015 |
| Conservative policy construction using variational autoencoders for logged data with missing values M Abroshan, KH Yip, C Tekin, M van der Schaar IEEE Transactions on Neural Networks and Learning Systems, 2022 | 5 | 2022 |
| Multilayer codes for synchronization from deletions M Abroshan, R Venkataramanan, AG i Fabregas 2017 IEEE Information Theory Workshop (ITW), 449-453, 2017 | 4 | 2017 |
| Loss Balancing for Fair Supervised Learning MM Khalili, X Zhang, M Abroshan ICML 2023, 2023 | 3 | 2023 |
| Symbolic Metamodels for Interpreting Black-boxes Using Primitive Functions M Abroshan, S Mishra, MM Khalili The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23), 2023 | 3 | 2023 |
| Codes for channels with segmented edits M Abroshan, R Venkataramanan, AG i Fabregas 2017 IEEE International Symposium on Information Theory (ISIT), 1768-1772, 2017 | 3 | 2017 |
| Multilayer codes for synchronization from deletions and insertions M Abroshan, R Venkataramanan, AG i Fàbregas IEEE Transactions on Information Theory 67 (6), 3342-3359, 2020 | 2 | 2020 |
| Codes for synchronization in channels and sources with edits M Abroshan | 2 | 2019 |
| Revisiting DeepFool: generalization and improvement A Abdollahpourrostam, M Abroshan, SM Moosavi-Dezfooli arXiv preprint arXiv:2303.12481, 2023 | 1 | 2023 |
| Imposing Fairness Constraints in Synthetic Data Generation M Abroshan, A Elliott, MM Khalili International Conference on Artificial Intelligence and Statistics, 2269-2277, 2024 | | 2024 |
| Safe AI for health and beyond--Monitoring to transform a health service M Abroshan, M Burkhart, O Giles, S Greenbury, Z Kourtzi, J Roberts, ... arXiv preprint arXiv:2303.01513, 2023 | | 2023 |
| Learning machines for health and beyond M Abroshan, O Giles, S Greenbury, J Roberts, M van der Schaar, ... arXiv e-prints, arXiv: 2303.01513, 2023 | | 2023 |
Albert Guillen i FabregasUniversity of Cambridge, Universitat Pompeu FabraVerified email at ieee.org
