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Fares Fourati

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9 papers
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9

AAAI Conference 2026 Conference Paper

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 Call is Precious (ECP) framework, which ensures that each accepted function evaluation is potentially informative, ECPv2 addresses key limitations of ECP, including high computational cost and overly conservative early behavior. ECPv2 introduces three innovations: (i) an adaptive lower bound that prevents vacuous acceptance regions, (ii) a memory mechanism that restricts comparisons to a fixed-size subset of past evaluations, and (iii) a fixed random projection that accelerates distance computations in high dimensions. We theoretically show that ECPv2 retains ECP’s regret guarantees and expands the acceptance region with high probability. Extensive experiments and ablation studies empirically validate these findings. Using principled hyperparameter settings, we evaluate ECPv2 across a wide range of nonconvex optimization problems and find that it consistently matches or outperforms leading optimizers while significantly reducing wall clock time.

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EWRL Workshop 2025 Workshop Paper

ACING: Actor-Critic for Instruction Learning in Black-Box LLMs

  • Salma Kharrat
  • Fares Fourati
  • Marco Canini

The effectiveness of Large Language Models (LLMs) in solving tasks depends significantly on the quality of their instructions, which often require substantial human effort to craft. This underscores the need for automated instruction optimization. However, optimizing instructions is particularly challenging when working with black-box LLMs, where model parameters and gradients are inaccessible. We introduce ACING, an actor-critic reinforcement learning framework that formulates instruction optimization as a stateless, continuous-action problem, enabling exploration of infinite instruction spaces using only black-box feedback. ACING automatically discovers prompts that outperform human-written prompts in 76\% of instruction-induction tasks, with gains of up to 33 points and a 10-point median improvement over the best automatic baseline in 33 tasks spanning instruction-induction, summarization, and chain-of-thought reasoning. Extensive ablations highlight its robustness and efficiency. An implementation of ACING is available at \url{https: //github. com/salmakh1/ACING}.

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EWRL Workshop 2025 Workshop Paper

Latent Inference for Effective Multi-Agent Reinforcement Learning under Partial Observability

  • Salma Kharrat
  • Fares Fourati
  • Marco Canini
  • Mohamed-Slim Alouini
  • Vaneet Aggarwal

Partial observability remains a core challenge in cooperative multi-agent reinforcement learning (MARL), often causing poor coordination and suboptimal policies. We show that state-of-the-art methods fail even in simple settings under partial observability. To address this, we propose LIMARL, a latent-inference framework that augments centralized training with decentralized execution (CTDE) via structured latent representations. LIMARL integrates (i) a state representation module that learns compact global state embeddings, and (ii) a recurrent inference module that enables agents to recover these embeddings from local histories. We provide theoretical analysis on sufficiency and robustness under partial observability. Empirically, LIMARL outperforms strong baselines in diagnostic tasks and challenging SMAC and SMACv2 scenarios, demonstrating better performance and faster convergence. Our results highlight latent inference as an effective and scalable solution for partially observable MARL. An implementation of LIMALR is available at https: //github. com/salmakh1/LIMARL.

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AAAI Conference 2024 Conference Paper

Combinatorial Stochastic-Greedy Bandit

  • Fares Fourati
  • Christopher John Quinn
  • Mohamed-Slim Alouini
  • Vaneet Aggarwal

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 selected set of n arms at each time step t in [T] is observed. SGB adopts an optimized stochastic-explore-then-commit approach and is specifically designed for scenarios with a large set of base arms. Unlike existing methods that explore the entire set of unselected base arms during each selection step, our SGB algorithm samples only an optimized proportion of unselected arms and selects actions from this subset. We prove that our algorithm achieves a (1-1/e)-regret bound of O(n^(1/3) k^(2/3) T^(2/3) log(T)^(2/3)) for monotone stochastic submodular rewards, which outperforms the state-of-the-art in terms of the cardinality constraint k. Furthermore, we empirically evaluate the performance of our algorithm in the context of online constrained social influence maximization. Our results demonstrate that our proposed approach consistently outperforms the other algorithms, increasing the performance gap as k grows.

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ICML Conference 2024 Conference Paper

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 noisy rewards for these subsets without accessing individual arm information, and can cooperate and share information at specific intervals. Our framework transforms any offline resilient single-agent $(\alpha-\epsilon)$-approximation algorithm—having a complexity of $\tilde{\mathcal{O}}\left(\frac{\psi}{\epsilon^\beta}\right)$, where the logarithm is omitted, for some function $\psi$ and constant $\beta$—into an online multi-agent algorithm with $m$ communicating agents and an $\alpha$-regret of no more than $\tilde{\mathcal{O}}\left(m^{-\frac{1}{3+\beta}} \psi^\frac{1}{3+\beta} T^\frac{2+\beta}{3+\beta}\right)$. Our approach not only eliminates the $\epsilon$ approximation error but also ensures sublinear growth with respect to the time horizon $T$ and demonstrates a linear speedup with an increasing number of communicating agents. Additionally, the algorithm is notably communication-efficient, requiring only a sublinear number of communication rounds, quantified as $\tilde{\mathcal{O}}\left(\psi T^\frac{\beta}{\beta+1}\right)$. Furthermore, the framework has been successfully applied to online stochastic submodular maximization using various offline algorithms, yielding the first results for both single-agent and multi-agent settings and recovering specialized single-agent theoretical guarantees. We empirically validate our approach to a stochastic data summarization problem, illustrating the effectiveness of the proposed framework, even in single-agent scenarios.

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ECAI Conference 2024 Conference Paper

FilFL: Client Filtering for Optimized Client Participation in Federated Learning

  • Fares Fourati
  • Salma Kharrat
  • Vaneet Aggarwal
  • Mohamed-Slim Alouini
  • Marco Canini

Federated learning, an emerging machine learning paradigm, enables clients to collaboratively train a model without exchanging local data. Clients participating in the training process significantly impact the convergence rate, learning efficiency, and model generalization. We propose a novel approach, client filtering, to improve model generalization and optimize client participation and training. The proposed method periodically filters available clients to identify a subset that maximizes a combinatorial objective function with an efficient greedy filtering algorithm. Thus, the clients are assessed as a combination rather than individually. We theoretically analyze the convergence of federated learning with client filtering in heterogeneous settings and evaluate its performance across diverse vision and language tasks, including realistic scenarios with time-varying client availability. Our empirical results demonstrate several benefits of our approach, including improved learning efficiency, faster convergence, and up to 10% higher test accuracy than training without client filtering.

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EWRL Workshop 2024 Workshop Paper

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 approaches like Q-learning, they come with a computational burden, necessitating the maximization of a value function over all actions in each iteration. This burden becomes particularly challenging when addressing large-scale problems and using deep neural networks as function approximators. In this paper, we present stochastic value-based RL approaches which, in each iteration, as opposed to optimizing over the entire set of $n$ actions, only consider a variable stochastic set of a sublinear number of actions, possibly as small as $\mathcal{O}(\log(n))$. The presented stochastic value-based RL methods include, among others, Stochastic Q-learning, StochDQN, and StochDDQN, all of which integrate this stochastic approach for both value-function updates and action selection. The theoretical convergence of Stochastic Q-learning is established, while an analysis of stochastic maximization is provided. Moreover, through empirical validation, we illustrate that the various proposed approaches outperform the baseline methods across diverse environments, including different control problems, achieving near-optimal average returns in significantly reduced time.

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ICML Conference 2024 Conference Paper

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 approaches like Q-learning, they come with a computational burden, necessitating the maximization of a value function over all actions in each iteration. This burden becomes particularly challenging when addressing large-scale problems and using deep neural networks as function approximators. In this paper, we present stochastic value-based RL approaches which, in each iteration, as opposed to optimizing over the entire set of $n$ actions, only consider a variable stochastic set of a sublinear number of actions, possibly as small as $\mathcal{O}(\log(n))$. The presented stochastic value-based RL methods include, among others, Stochastic Q-learning, StochDQN, and StochDDQN, all of which integrate this stochastic approach for both value-function updates and action selection. The theoretical convergence of Stochastic Q-learning is established, while an analysis of stochastic maximization is provided. Moreover, through empirical validation, we illustrate that the various proposed approaches outperform the baseline methods across diverse environments, including different control problems, achieving near-optimal average returns in significantly reduced time.

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EWRL Workshop 2023 Workshop Paper

Combinatorial Stochastic-Greedy Bandit

  • Fares Fourati
  • Christopher John Quinn
  • Mohamed-Slim Alouini
  • Vaneet Aggarwal

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 selected set of $n$ arms at each time step $t\in [T]$ is observed. SGB adopts an optimized stochastic-explore-then-commit approach and is specifically designed for scenarios with a large set of base arms. Unlike existing methods that explore the entire set of unselected base arms during each selection step, our SGB algorithm samples only an optimized proportion of unselected arms and selects actions from this subset. We prove that our algorithm achieves a $(1-1/e)$-regret bound of $\mathcal{O}(n^{\frac{1}{3}} k^{\frac{2}{3}} T^{\frac{2}{3}} \log(T)^{\frac{2}{3}})$ for monotone stochastic submodular rewards, which outperforms the state-of-the-art in terms of the cardinality constraint $k$. Furthermore, we empirically evaluate the performance of our algorithm in the context of online constrained social influence maximization. Our results demonstrate that our proposed approach consistently outperforms the other algorithms, increasing the performance gap as $k$ grows.

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