Zobrazeno 1 - 10
of 108
pro vyhledávání: '"Mozeika, Alexander"'
Replica analysis of overfitting in regression models for time to event data: the impact of censoring
We use statistical mechanics techniques, viz. the replica method, to model the effect of censoring on overfitting in Cox's proportional hazards model, the dominant regression method for time-to-event data. In the overfitting regime, Maximum Likelihoo
Externí odkaz:
http://arxiv.org/abs/2312.02870
We present Carnot, a leader-based Byzantine Fault Tolerant (BFT) consensus protocol that is responsive and operates under the partially synchronous model. Responsive BFT consensus protocols exhibit wire-speed operation and deliver instantaneous final
Externí odkaz:
http://arxiv.org/abs/2308.16016
Blockchains facilitate decentralization, security, identity, and data management in cyber-physical systems. However, consensus protocols used in blockchains are prone to high message and computational complexity costs and are not suitable to be used
Externí odkaz:
http://arxiv.org/abs/2303.14276
We analyse the equilibrium behaviour and non-equilibrium dynamics of sparse Boolean networks with self-interactions that evolve according to synchronous Glauber dynamics. Equilibrium analysis is achieved via a novel application of the cavity method t
Externí odkaz:
http://arxiv.org/abs/2206.05228
We study the impact of vaccination on the risk of epidemics spreading through structured networks using the cavity method of statistical physics. We relax the assumption that vaccination prevents all transmission of a disease used in previous studies
Externí odkaz:
http://arxiv.org/abs/2110.06616
Autor:
Mozeika, Alexander, Sheikh, Mansoor, Aguirre-Lopez, Fabian, Antenucci, Fabrizio, Coolen, Anthony CC
Publikováno v:
Phys. Rev. E 103, 042142 (2021)
It is clear that conventional statistical inference protocols need to be revised to deal correctly with the high-dimensional data that are now common. Most recent studies aimed at achieving this revision rely on powerful approximation techniques, tha
Externí odkaz:
http://arxiv.org/abs/2009.13229
Publikováno v:
Phys. Rev. Lett. 125, 168301 (2020)
We study the space of functions computed by random-layered machines, including deep neural networks and Boolean circuits. Investigating the distribution of Boolean functions computed on the recurrent and layer-dependent architectures, we find that it
Externí odkaz:
http://arxiv.org/abs/2004.08930
The adaptive immune system relies on diversity of its repertoire of receptors to protect the organism from a great variety of pathogens. Since the initial repertoire is the result of random gene rearrangement, binding of receptors is not limited to p
Externí odkaz:
http://arxiv.org/abs/1910.13357
Autor:
Mozeika, Alexander, Coolen, Anthony CC
We use statistical mechanics to study model-based Bayesian data clustering. In this approach, each partition of the data into clusters is regarded as a microscopic system state, the negative data log-likelihood gives the energy of each state, and the
Externí odkaz:
http://arxiv.org/abs/1810.02627
Autor:
Mozeika, Alexander, Coolen, Anthony CC
Publikováno v:
Phys. Rev. E 98, 042133 (2018)
We show that model-based Bayesian clustering, the probabilistically most systematic approach to the partitioning of data, can be mapped into a statistical physics problem for a gas of particles, and as a result becomes amenable to a detailed quantita
Externí odkaz:
http://arxiv.org/abs/1709.01632