239 citations · 996 across the 29 of their papers we have counts for
29 papers
Approximate Analytical Protein Distributions for the Three-stage Model of Stochastic Gene Expression
Kenny Wong, Thomas Mourier, Sho Inaba +2
Gene expression is an intrinsically stochastic process that generates phenotypic heterogeneity within genetically identical cell populations. While the exact statistical moments of…
Analyzing Post-transcriptional Regulation in Stochastic Gene Expression Models Using Partitioned Poisson Arrivals
Kenny Wong, Argenis Arriojas, Sho Inaba +3
Gene expression is a stochastic process that allows for fluctuations in protein levels that can give rise to phenotypic heterogeneity within a population of genetically identical c…
Bounding the Optimal Value Function in Compositional Reinforcement Learning
Jacob Adamczyk, Volodymyr Makarenko, Argenis Arriojas +2
In the field of reinforcement learning (RL), agents are often tasked with solving a variety of problems differing only in their reward functions. In order to quickly obtain solutio…
Leveraging Prior Knowledge in Reinforcement Learning via Double-Sided Bounds on the Value Function
Jacob Adamczyk, Stas Tiomkin, Rahul Kulkarni
An agent's ability to leverage past experience is critical for efficiently solving new tasks. Approximate solutions for new tasks can be obtained from previously derived value func…
Utilizing Prior Solutions for Reward Shaping and Composition in Entropy-Regularized Reinforcement Learning
Jacob Adamczyk, Argenis Arriojas, Stas Tiomkin +1
In reinforcement learning (RL), the ability to utilize prior knowledge from previously solved tasks can allow agents to quickly solve new problems. In some cases, these new problem…
Entropy Regularized Reinforcement Learning Using Large Deviation Theory
Argenis Arriojas, Jacob Adamczyk, Stas Tiomkin +1
Reinforcement learning (RL) is an important field of research in machine learning that is increasingly being applied to complex optimization problems in physics. In parallel, conce…