Zobrazeno 1 - 10
of 472
pro vyhledávání: '"Yassaee, A"'
Autor:
Aminian, Gholamali, Bagheri, Amirhossien, JafariNodeh, Mahyar, Karimian, Radmehr, Yassaee, Mohammad-Hossein
This paper investigates a range of empirical risk functions and regularization methods suitable for self-training methods in semi-supervised learning. These approaches draw inspiration from various divergence measures, such as $f$-divergences and $\a
Externí odkaz:
http://arxiv.org/abs/2405.00454
Autor:
Kavian, Masoud, Mojahedian, Mohammad Mahdi, Yassaee, Mohammad Hossein, Mirmohseni, Mahtab, Aref, Mohammad Reza
Random binning is a widely utilized tool in information theory, finding applications in various domains. In this paper, we focus on the output statistics of random binning (OSRB) using the Tsallis divergence $T_\alpha$. Our investigation encompasses
Externí odkaz:
http://arxiv.org/abs/2304.12606
In this paper, we provide three applications for $f$-divergences: (i) we introduce Sanov's upper bound on the tail probability of the sum of independent random variables based on super-modular $f$-divergence and show that our generalized Sanov's boun
Externí odkaz:
http://arxiv.org/abs/2206.11042
The problem of Sequential Estimation under Multiple Resources (SEMR) is defined in a federated setting. SEMR could be considered as the intersection of statistical estimation and bandit theory. In this problem, an agent is confronting with k resource
Externí odkaz:
http://arxiv.org/abs/2109.14703
Autor:
Dabbah, Mohammad A., Reed, Angus B., Booth, Adam T. C., Yassaee, Arrash, Despotovic, Alex, Klasmer, Benjamin, Binning, Emily, Aral, Mert, Plans, David, Labrique, Alain B., Mohan, Diwakar
The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality
Externí odkaz:
http://arxiv.org/abs/2104.09226
We study learning algorithms when there is a mismatch between the distributions of the training and test datasets of a learning algorithm. The effect of this mismatch on the generalization error and model misspecification are quantified. Moreover, we
Externí odkaz:
http://arxiv.org/abs/2102.05695
We consider fundamental limits for communicating over a compound channel when the state of the channel needs to be masked. Our model is closely related to an area of study known as covert communication that is a setting in which the transmitter wishe
Externí odkaz:
http://arxiv.org/abs/2012.01706
In this paper, we consider fundamental communication limits over a compound channel. Covert communication in the information-theoretic context has been primarily concerned with fundamental limits when the transmitter wishes to communicate to legitima
Externí odkaz:
http://arxiv.org/abs/1906.06675
Autor:
Yassaee, Mohammad Hossein
This paper investigates the soft covering lemma under both the relative entropy and the total variation distance as the measures of deviation. The exact order of the expected deviation of the random i.i.d. code for the soft covering problem problem,
Externí odkaz:
http://arxiv.org/abs/1902.07956
A fundamental tool in network information theory is the covering lemma, which lower bounds the probability that there exists a pair of random variables, among a give number of independently generated candidates, falling within a given set. We use a w
Externí odkaz:
http://arxiv.org/abs/1901.00179