195 citations · 242 across the 18 of their papers we have counts for
8 papers · 1 filter
ECPv2: Fast, Efficient, and Scalable Global Optimization of Lipschitz Functions
Fares Fourati, Mohamed-Slim Alouini, Vaneet Aggarwal
We propose ECPv2, a scalable and theoretically grounded algorithm for global optimization of Lipschitz-continuous functions with unknown Lipschitz constants. Building on the Every…
Every Call is Precious: Global Optimization of Black-Box Functions with Unknown Lipschitz Constants
Fares Fourati, Salma Kharrat, Vaneet Aggarwal +1
Optimizing expensive, non-convex, black-box Lipschitz continuous functions presents significant challenges, particularly when the Lipschitz constant of the underlying function is u…
Stochastic Q-learning for Large Discrete Action Spaces
Fares Fourati, Vaneet Aggarwal, Mohamed-Slim Alouini
In complex environments with large discrete action spaces, effective decision-making is critical in reinforcement learning (RL). Despite the widespread use of value-based RL approa…
Federated Combinatorial Multi-Agent Multi-Armed Bandits
Fares Fourati, Mohamed-Slim Alouini, Vaneet Aggarwal
This paper introduces a federated learning framework tailored for online combinatorial optimization with bandit feedback. In this setting, agents select subsets of arms, observe no…
Addressing Bias Through Ensemble Learning and Regularized Fine-Tuning
Ahmed Radwan, Layan Zaafarani, Jetana Abudawood +2
Addressing biases in AI models is crucial for ensuring fair and accurate predictions. However, obtaining large, unbiased datasets for training can be challenging. This paper propos…
Combinatorial Stochastic-Greedy Bandit
Fares Fourati, Christopher John Quinn, Mohamed-Slim Alouini +1
We propose a novel combinatorial stochastic-greedy bandit (SGB) algorithm for combinatorial multi-armed bandit problems when no extra information other than the joint reward of the…
Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback
Fares Fourati, Vaneet Aggarwal, Christopher John Quinn +1
We investigate the problem of unconstrained combinatorial multi-armed bandits with full-bandit feedback and stochastic rewards for submodular maximization. Previous works investiga…
FilFL: Client Filtering for Optimized Client Participation in Federated Learning
Fares Fourati, Salma Kharrat, Vaneet Aggarwal +2
Federated learning, an emerging machine learning paradigm, enables clients to collaboratively train a model without exchanging local data. Clients participating in the training pro…