Jose, ST & Simeone, O 2022, 'An information-theoretic analysis of the cost of decentralization for learning and inference under privacy constraints', Entropy, vol. 24, no. 4, 485. https://doi.org/10.3390/e24040485
Jose, ST, Simeone, O & Durisi, G 2022, 'Transfer meta-learning: information- theoretic bounds and information meta-risk minimization', IEEE Transactions on Information Theory, vol. 68, no. 1, pp. 474-501. https://doi.org/10.1109/TIT.2021.3119605
Jose, ST & Simeone, O 2021, 'Free energy minimization: a unified framework for modeling, inference, learning, and optimization [lecture notes]', IEEE Signal Processing Magazine, vol. 38, no. 2, pp. 120-125. https://doi.org/10.1109/MSP.2020.3041414
Jose, ST & Simeone, O 2021, 'Information-theoretic generalization bounds for meta-learning and applications', Entropy, vol. 23, no. 1, 126. https://doi.org/10.3390/e23010126
Jose, ST & Kulkarni, AA 2020, 'Shannon Meets von Neumann: A Minimax Theorem for Channel Coding in the Presence of a Jammer', IEEE Transactions on Information Theory. https://doi.org/10.1109/TIT.2020.2971682
Jose, ST & Kulkarni, AA 2019, 'Improved Finite Blocklength Converses for Slepian–Wolf Coding via Linear Programming', IEEE Transactions on Information Theory. https://doi.org/10.1109/TIT.2018.2873623
Jose, S, Park, S & Simeone, O 2022, Information-theoretic analysis of epistemic uncertainty in Bayesian meta-learning. in G Camps-Valls, FJR Ruiz & I Valera (eds), International Conference on Artificial Intelligence and Statistics, 28-30 March 2022, A Virtual Conference. Proceedings of Machine Learning Research, vol. 151, Proceedings of Machine Learning Research, pp. 9758-9775, The 25th International Conference on Artificial Intelligence and Statistics, 28/03/22. <https://proceedings.mlr.press/v151/theresa-jose22a.html>
Jose, S & Simeone, O 2021, A unified PAC-Bayesian framework for machine unlearning via information risk minimization. in IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
Jose, S & Simeone, O 2021, An information-theoretic analysis of the impact of task similarity on meta-learning. in IEEE International Symposium on Information Theory (ISIT). https://doi.org/10.1109/ISIT45174.2021.9517767
Jose, S, Durisi, G & Rezazadeh, A 2021, Conditional mutual information-based generalization bound for meta learning. in IEEE International Symposium on Information Theory (ISIT). https://doi.org/10.1109/ISIT45174.2021.9518020
Jose, S & Simeone, O 2021, Information-theoretic bounds on transfer generalization gap based on Jensen-Shannon divergence. in European Signal Processing Conference (EUSIPCO). https://doi.org/10.23919/EUSIPCO54536.2021.9616270
Jose, S, Simeone, O & Zhang, Y 2021, Transfer Bayesian Meta-Learning Via Weighted Free Energy Minimization. in IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
Jose, S & Simeone, O 2020, Address-event variable-length compression for time-encoded data. in IEEE International Symposium on Information Theory and Applications. <https://ieeexplore.ieee.org/document/9366190>
Jose, S & Kulkarni, AA 2018, Linear Programming Based Finite Blocklength Converses in Information Theory. in IEEE Information Theory and Applications (ITA).
Jose, S & Kulkarni, AA 2018, New finite blocklength converses for asymmetric multiple access channels via linear programming. in IEEE International Conference on Signal Processing and Communications (SPCOM).
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