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20162026
most citedConservative and Risk-Aware Offline Multi-Agent Reinforcement Learning

8 citations · 20 across the 10 of their papers we have counts for

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cs.LG2026

Asymptotic Behavior of Multi--Task Learning: Implicit Regularization and Double Descent Effects

Ayed M. Alrashdi, Oussama Dhifallah, Houssem Sifaou

Multi--task learning seeks to improve the generalization error by leveraging the common information shared by multiple related tasks. One challenge in multi--task learning is ident…

cs.LG2025

Multi-Fidelity Hybrid Reinforcement Learning via Information Gain Maximization

Houssem Sifaou, Osvaldo Simeone

Optimizing a reinforcement learning (RL) policy typically requires extensive interactions with a high-fidelity simulator of the environment, which are often costly or impractical.…

cs.LG2025

Reliable Wireless Indoor Localization via Cross-Validated Prediction-Powered Calibration

Seonghoon Yoo, Houssem Sifaou, Sangwoo Park +2

Wireless indoor localization using predictive models with received signal strength information (RSSI) requires proper calibration for reliable position estimates. One remedy is to…

cs.LG2024★ 8 cited

Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning

Eslam Eldeeb, Houssem Sifaou, Osvaldo Simeone +2

Reinforcement learning (RL) has been widely adopted for controlling and optimizing complex engineering systems such as next-generation wireless networks. An important challenge in…

cs.LG2022★ 4 cited

Over-The-Air Federated Learning under Byzantine Attacks

Houssem Sifaou, Geoffrey Ye Li

Federated learning (FL) is a promising solution to enable many AI applications, where sensitive datasets from distributed clients are needed for collaboratively training a global m…

cs.LG2021★ 3 cited

A Precise Performance Analysis of Support Vector Regression

Houssem Sifaou, Abla kammoun, Mohamed-Slim Alouini

In this paper, we study the hard and soft support vector regression techniques applied to a set of linear measurements of the form wh…