335 citations · 1.1k across the 63 of their papers we have counts for
25 papers · 1 filter
Graph Structured Combinatorial Semi-Bandit with Nonlinear Reward Associations through Separable Signals
Christoph Bauschmann, Setareh Maghsudi
The identification of optimal structures within vast arrays of interconnected data necessitates significant sampling- and computational effort. Learning and leveraging underlying s…
One Model for All: Multi-Objective Controllable Language Models
Qiang He, Yucheng Yang, Tianyi Zhou +3
Aligning large language models (LLMs) with human preferences is critical for enhancing LLMs' safety, helpfulness, humor, faithfulness, etc. Current reinforcement learning from huma…
Robust Optimization Approach and Learning Based Hide-and-Seek Game for Resilient Network Design
Mohammad Khosravi, Setareh Maghsudi
We study the design of resilient and reliable communication networks in which a signal can be transferred only up to a limited distance before its quality falls below an acceptable…
Meta-Learning Multi-armed Bandits for Beam Tracking in 5G and 6G Networks
Alexander Mattick, George Yammine, Georgios Kontes +2
Beamforming-capable antenna arrays with many elements enable higher data rates in next generation 5G and 6G networks. In current practice, analog beamforming uses a codebook of pre…
Pareto Multi-Objective Alignment for Language Models
Qiang He, Setareh Maghsudi
Large language models (LLMs) are increasingly deployed in real-world applications that require careful balancing of multiple, often conflicting, objectives, such as informativeness…
Stochastic Multi-Objective Multi-Armed Bandits: Regret Definition and Algorithm
Mansoor Davoodi, Setareh Maghsudi
Multi-armed bandit (MAB) problems are widely applied to online optimization tasks that require balancing exploration and exploitation. In practical scenarios, these tasks often inv…
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…
Budgeted Recommendation with Delayed Feedback
Kweiguu Liu, Setareh Maghsudi
In a conventional contextual multi-armed bandit problem, the feedback (or reward) is immediately observable after an action. Nevertheless, delayed feedback arises in numerous real-…
Adaptive Regularization of Representation Rank as an Implicit Constraint of Bellman Equation
Qiang He, Tianyi Zhou, Meng Fang +1
Representation rank is an important concept for understanding the role of Neural Networks (NNs) in Deep Reinforcement learning (DRL), which measures the expressive capacity of valu…
Meta Learning in Bandits within Shared Affine Subspaces
Steven Bilaj, Sofien Dhouib, Setareh Maghsudi
We study the problem of meta-learning several contextual stochastic bandits tasks by leveraging their concentration around a low-dimensional affine subspace, which we learn via onl…
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…
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…
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…
Hypothesis Transfer in Bandits by Weighted Models
Steven Bilaj, Sofien Dhouib, Setareh Maghsudi
We consider the problem of contextual multi-armed bandits in the setting of hypothesis transfer learning. That is, we assume having access to a previously learned model on an unobs…
Connecting sufficient conditions for domain adaptation: source-guided uncertainty, relaxed divergences and discrepancy localization
Sofien Dhouib, Setareh Maghsudi
Recent advances in domain adaptation establish that requiring a low risk on the source domain and equal feature marginals degrade the adaptation's performance. At the same time, em…
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…