335 citations · 1.1k across the 63 of their papers we have counts for
11 papers · 1 filter
Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning Environments
Manuel Eberhardinger, Johannes Maucher, Setareh Maghsudi
Understanding the interactions of agents trained with deep reinforcement learning is crucial for deploying agents in games or the real world. In the former, unreasonable actions co…
Federated Learning in UAV-Enhanced Networks: Joint Coverage and Convergence Time Optimization
Mariam Yahya, Setareh Maghsudi, Slawomir Stanczak
Federated learning (FL) involves several devices that collaboratively train a shared model without transferring their local data. FL reduces the communication overhead, making it a…
Piecewise-Stationary Combinatorial Semi-Bandit with Causally Related Rewards
Behzad Nourani-Koliji, Steven Bilaj, Amir Rezaei Balef +1
We study the piecewise stationary combinatorial semi-bandit problem with causally related rewards. In our nonstationary environment, variations in the base arms' distributions, cau…
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…
Distributed Consensus Algorithm for Decision-Making in Multi-agent Multi-armed Bandit
Xiaotong Cheng, Setareh Maghsudi
We study a structured multi-agent multi-armed bandit (MAMAB) problem in a dynamic environment. A graph reflects the information-sharing structure among agents, and the arms' reward…
Eigensubspace of Temporal-Difference Dynamics and How It Improves Value Approximation in Reinforcement Learning
Qiang He, Tianyi Zhou, Meng Fang +1
We propose a novel value approximation method, namely Eigensubspace Regularized Critic (ERC) for deep reinforcement learning (RL). ERC is motivated by an analysis of the dynamics o…
Cooperative Thresholded Lasso for Sparse Linear Bandit
Haniyeh Barghi, Xiaotong Cheng, Setareh Maghsudi
We present a novel approach to address the multi-agent sparse contextual linear bandit problem, in which the feature vectors have a high dimension whereas the reward function d…
Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks
Yuwen Cao, Tomoaki Ohtsuki, Setareh Maghsudi +1
In this paper, we develop a deep learning (DL)-guided hybrid beam and power allocation approach for multiuser millimeter-wave (mmWave) networks, which facilitates swift beamforming…
Piecewise-Stationary Multi-Objective Multi-Armed Bandit with Application to Joint Communications and Sensing
Amir Rezaei Balef, Setareh Maghsudi
We study a multi-objective multi-armed bandit problem in a dynamic environment. The problem portrays a decision-maker that sequentially selects an arm from a given set. If selected…
Robustness Implies Fairness in Causal Algorithmic Recourse
Ahmad-Reza Ehyaei, Amir-Hossein Karimi, Bernhard Schölkopf +1
Algorithmic recourse aims to disclose the inner workings of the black-box decision process in situations where decisions have significant consequences, by providing recommendations…