Scalable inverse reinforcement learning through multifidelity Bayesian optimization
M Imani, SF Ghoreishi - IEEE transactions on neural networks …, 2021 - ieeexplore.ieee.org
Data in many practical problems are acquired according to decisions or actions made by
users or experts to achieve specific goals. For instance, policies in the mind of biologists …
users or experts to achieve specific goals. For instance, policies in the mind of biologists …
Trust-based social networks with computing, caching and communications: A deep reinforcement learning approach
Social networks have continuously been expanding and trying to be innovative. The recent
advances of computing, caching, and communication (3C) can have significant impacts on …
advances of computing, caching, and communication (3C) can have significant impacts on …
Mayer-type optimal control of probabilistic Boolean control network with uncertain selection probabilities
M Toyoda, Y Wu - IEEE transactions on cybernetics, 2019 - ieeexplore.ieee.org
This article considers a Mayer-type optimal control problem of probabilistic Boolean control
networks (PBCNs) with uncertainty on selection probabilities which obey Beta probabilistic …
networks (PBCNs) with uncertainty on selection probabilities which obey Beta probabilistic …
Bayesian optimization objective-based experimental design
M Imani, SF Ghoreishi - 2020 American control conference …, 2020 - ieeexplore.ieee.org
Design has become a salient part of most of the scientific and engineering tasks, embracing
a wide range of domains including real experimental settings (eg, material discovery or drug …
a wide range of domains including real experimental settings (eg, material discovery or drug …
An optimal Bayesian intervention policy in response to unknown dynamic cell stimuli
SH Hosseini, M Imani - Information Sciences, 2024 - Elsevier
Interventions in gene regulatory networks (GRNs) aim to restore normal functions of cells
experiencing abnormal behavior, such as uncontrolled cell proliferation. The dynamic …
experiencing abnormal behavior, such as uncontrolled cell proliferation. The dynamic …
Boolean Kalman filter and smoother under model uncertainty
Partially-observed Boolean dynamical systems (POBDS) are a general class of nonlinear
state-space models that provide a rich framework for modeling many complex dynamical …
state-space models that provide a rich framework for modeling many complex dynamical …
Control of gene regulatory networks using Bayesian inverse reinforcement learning
M Imani, UM Braga-Neto - IEEE/ACM transactions on …, 2018 - ieeexplore.ieee.org
Control of gene regulatory networks (GRNs) to shift gene expression from undesirable states
to desirable ones has received much attention in recent years. Most of the existing methods …
to desirable ones has received much attention in recent years. Most of the existing methods …
Particle filters for partially-observed Boolean dynamical systems
M Imani, UM Braga-Neto - Automatica, 2018 - Elsevier
Partially-observed Boolean dynamical systems (POBDS) are a general class of nonlinear
models with application in estimation and control of Boolean processes based on noisy and …
models with application in estimation and control of Boolean processes based on noisy and …
Sequential experimental design for optimal structural intervention in gene regulatory networks based on the mean objective cost of uncertainty
Scientists are attempting to use models of ever-increasing complexity, especially in
medicine, where gene-based diseases such as cancer require better modeling of cell …
medicine, where gene-based diseases such as cancer require better modeling of cell …
Graph-based Bayesian optimization for large-scale objective-based experimental design
M Imani, SF Ghoreishi - IEEE transactions on neural networks …, 2021 - ieeexplore.ieee.org
Design is an inseparable part of most scientific and engineering tasks, including real and
simulation-based experimental design processes and parameter/hyperparameter …
simulation-based experimental design processes and parameter/hyperparameter …