Journal Papers
- A simple network of nodes moving on the circle, Dimitris Cheliotis, Ioannis Kontoyiannis, Michail Loulakis, Stavros Toumpis, Random Struct Alg. 2020; 57:317—338,
https://doi.org/10.1002/rsa.20932 - Sharp Second-Order Pointwise Asymptotics for Lossless Compression with Side Information, Lampros Gavalakis, Ioannis Kontoyiannis, Entropy, 2020, 22(6), 705,
https://doi.org/10.3390/e22060705 - Fundamental Limits of Lossless Data Compression with Side Information, Lampros Gavalakis, Ioannis Kontoyiannis, IEEE Transactions on Information Theory 2021; 67(5): 2680—2692, DOI: 1109/TIT.2021.3062614
- Differential Temporal Difference Learning, A.M.Devraj, I. Kontoyiannis, S.P. Meyn, IEEE Transactions on Automatic Control 2021; 66(10): 4652 – 4667,
DOI: 1109/TAC.2020.3033417 - Nonasymptotic Gaussian Approximation for Inference with Stable Noise. Marina Riabiz, Tohid Ardeshiri, Ioannis Kontoyiannis, Simon Godsill, IEEE Transactions on Information Theory 2020; 66(8): 4966 – 4991,
DOI:1109/TIT.2020.2996135 - An Information-Theoretic Proof of a Finite de Finetti Theorem. Lampros Gavalakis, Ioannis Kontoyannis, Commun. Probab. 2021; 26:1-5,
https://doi.org/10.1214/21-ECP428 - Functional Limit Theorems for the Pólya urn. Dimitris Cheliotis, Dimitra Kouloumpou, J Theoretical Probab. 2022; 35:2038—2051,
https://doi.org/10.1007/s10959-021-01123-3 - Limit behavior of the q-Pólya urn. Dimitris Cheliotis, Dimitra Kouloumpou, Ramanujan J. 2023 (60): 69—93,
https://doi.org/10.1007/s11139-021-00542-4 - Interspecies spin-noise correlations in hot atomic vapors, Mouloudakis, F. Vouzinas, A. Margaritakis, A. Koutsimpela, et al. Physical Review A 2023; 108: 052822,
https://doi.org/10.1103/PhysRevA.108.052822 - Discrete gradient flow approximations of high dimensional evolution partial differential equations via deep neural networks, Emmanuil H. Georgoulis, Michail Loulakis, Asterios Tsiourvas, Nonlinear Science and Num. Simul. 2023; 117: 106893,
https://doi.org/10.1016/j.cnsns.2022.106893 - Context-Tree Weighting and Bayesian Context Trees: Asymptotic and Non-Asymptotic Justifications, Ioannis Kontoyiannis, IEEE Transactions on Information Theory 2024; 70(2):1204 – 1219,
DOI: 1109/TIT.2023.3313114 - Entropy and the discrete central limit theorem, Lampros Gavalakis, Ioannis Kontoyiannis, Stochastic Processes and their Applications 2024; 170 (4): 104294,
https://doi.org/10.1016/j.spa.2023.104294 - On the optimally controlled stochastic shallow lake. Angelina Koutsimpela, Michail Loulakis, International J. Control 2024; 97(11): 2539—2551,
https://doi.org/10.1080/00207179.2023.2284861 - Posterior Representations for Bayesian Context Trees: Sampling, Estimation and Convergence. Ioannis Papageorgiou, Ioannis Kontoyiannis, Bayesian Anal. 2024;
19(2): 501–529 https://doi.org/10.1214/23-BA1362 - Universality of the least singular value and singular vector delocalisation for Lévy non-symmetric random matrices, Michail Louvaris, Annales de l’ Institut Henri Poincaré, accepted for publication,
https://www.e-publications.org/ims/submission/AIHP/user/submissionFile/55582?confirm=42dccd97
Conference Proceedings
- Lossless Data Compression with Side Information: Nonasymptotics and Dispersion, Lampros Gavalakis and Ioannis Kontoyiannis, 2020 IEEE International Symposium on Information Theory (ISIT),
DOI: 10.1109/ISIT44484.2020.9174326 - The Entropic Central Limit Theorem for Discrete Random Variables. Lampros Gavalakis and Ioannis Kontoyiannis, 2020 IEEE International Symposium on Information Theory (ISIT),
DOI: 10.1109/ISIT50566.2022.9834855 - The Posterior Distribution of Bayesian Context-Tree Models: Theory and Applications. Ioannis Papageorgiou, Ioannis Kontoyiannis, 2022 IEEE International Symposium on Information Theory (ISIT),
DOI: 10.1109/ISIT50566.2022.9834791 - Bayesian Change-Point Detection via Context-Tree Weighting. Valentinian Lungu, Ioannis Papageorgiou, Ioannis Kontoyiannis, 2022 IEEE Information Theory Workshop (ITW),
DOI: 10.1109/ITW54588.2022.9965823 - Truly Bayesian Entropy Estimation. Ioannis Papageorgiou, Ioannis Kontoyiannis, 2023 IEEE Information Theory Workshop (ITW),
DOI: 10.1109/ITW55543.2023.10161645
Preprints
- The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning. Vivek Borkar, Shuhang Chen, Adithya Devraj, Ioannis Kontoyiannis, Sean Meyn, arXiv:2110.14427 [math.ST]
- The limit of the operator norm for random matrices with a variance profile. Dimitris Cheliotis, Michail Louvaris, arXiv:2404.13795 [math.PR]
- A new approach to generalisation error of machine learning algorithms: Estimates and convergence, Michail Loulakis, Charalambos G. Makridakis, arXiv:2306.13784 [stat.ML]