7 citations · 15 across the 9 of their papers we have counts for
9 papers
A Unified Particle Filter LSTM for Data-Driven Process Simulation
Parvin Malekzadeh, Opher Baron, Dmitry Krass
Data-driven process simulation aims to generate realistic case trajectories from historical event logs without requiring an explicitly specified model of the underlying dynamics. D…
EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning
Parvin Malekzadeh, Zissis Poulos, Jacky Chen +2
Recent advancements in Distributional Reinforcement Learning (DRL) for modeling loss distributions have shown promise in developing hedging strategies in derivatives markets. A com…
A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement Learning
Parvin Malekzadeh, Konstantinos N. Plataniotis, Zissis Poulos +1
Distributional Reinforcement Learning (RL) estimates return distribution mainly by learning quantile values via minimizing the quantile Huber loss function, entailing a threshold p…
A unified uncertainty-aware exploration: Combining epistemic and aleatory uncertainty
Parvin Malekzadeh, Ming Hou, Konstantinos N. Plataniotis
Exploration is a significant challenge in practical reinforcement learning (RL), and uncertainty-aware exploration that incorporates the quantification of epistemic and aleatory un…
Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning
Parvin Malekzadeh, Ming Hou, Konstantinos N. Plataniotis
Sample efficiency is central to developing practical reinforcement learning (RL) for complex and large-scale decision-making problems. The ability to transfer and generalize knowle…
Active Inference and Reinforcement Learning: A unified inference on continuous state and action spaces under partial observability
Parvin Malekzadeh, Konstantinos N. Plataniotis
Reinforcement learning (RL) has garnered significant attention for developing decision-making agents that aim to maximize rewards, specified by an external supervisor, within fully…
AKF-SR: Adaptive Kalman Filtering-based Successor Representation
Parvin Malekzadeh, Mohammad Salimibeni, Ming Hou +2
Recent studies in neuroscience suggest that Successor Representation (SR)-based models provide adaptation to changes in the goal locations or reward function faster than model-free…
Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation
Mohammad Salimibeni, Arash Mohammadi, Parvin Malekzadeh +1
Distributed Multi-Agent Reinforcement Learning (MARL) algorithms has attracted a surge of interest lately mainly due to the recent advancements of Deep Neural Networks (DNNs). Conv…
MM-KTD: Multiple Model Kalman Temporal Differences for Reinforcement Learning
Parvin Malekzadeh, Mohammad Salimibeni, Arash Mohammadi +2
There has been an increasing surge of interest on development of advanced Reinforcement Learning (RL) systems as intelligent approaches to learn optimal control policies directly f…