29 citations · 31 across the 6 of their papers we have counts for
6 papers
Quantum-Inspired Reinforcement Learning in the Presence of Epistemic Ambivalence
Alireza Habibi, Saeed Ghoorchian, Setareh Maghsudi
The complexity of online decision-making under uncertainty stems from the requirement of finding a balance between exploiting known strategies and exploring new possibilities. Natu…
Online Learning with Costly Features in Non-stationary Environments
Saeed Ghoorchian, Evgenii Kortukov, Setareh Maghsudi
Maximizing long-term rewards is the primary goal in sequential decision-making problems. The majority of existing methods assume that side information is freely available, enabling…
Non-stationary Delayed Combinatorial Semi-Bandit with Causally Related Rewards
Saeed Ghoorchian, Setareh Maghsudi
Sequential decision-making under uncertainty is often associated with long feedback delays. Such delays degrade the performance of the learning agent in identifying a subset of arm…
Linear Combinatorial Semi-Bandit with Causally Related Rewards
Behzad Nourani-Koliji, Saeed Ghoorchian, Setareh Maghsudi
In a sequential decision-making problem, having a structural dependency amongst the reward distributions associated with the arms makes it challenging to identify a subset of alter…
Bayesian Non-stationary Linear Bandits for Large-Scale Recommender Systems
Saeed Ghoorchian, Evgenii Kortukov, Setareh Maghsudi
Taking advantage of contextual information can potentially boost the performance of recommender systems. In the era of big data, such side information often has several dimensions.…
Multi-Armed Bandit for Energy-Efficient and Delay-Sensitive Edge Computing in Dynamic Networks with Uncertainty
Saeed Ghoorchian, Setareh Maghsudi
In the edge computing paradigm, mobile devices offload the computational tasks to an edge server by routing the required data over the wireless network. The full potential of edge…