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University of Lille

Academic institutioneurope · fr
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Research library262linked papers
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Selected work

Representative Papers

Direct Access for Conjunctive Queries with Negation

Oct 24, 2023International Conference on Database Theory

This paper investigates the direct access problem to the k-th answer in lexicographic order for conjunctive queries with negation (CQ¬) over databases: after polynomial-time preprocessing, arbitrary rank-k queries must be answered in polylogarithmic time. To address this, we systematically extend the characterization of direct-access tractability from positive conjunctive queries to CQ¬, introducing a unified framework based on representable relation circuits. We prove that both β-acyclic and bounded nested set-width classes of negative queries are tractable within this framework, strictly generalizing prior tractability boundaries for positive and negative queries. Experimental evaluation confirms that our approach achieves polynomial preprocessing and polylogarithmic access time, subsuming all previously known tractable classes and extending beyond them.

6 citations1 influentialRead paper

Learning linear dynamical systems under convex constraints

Mar 27, 2023arXiv.org

This paper addresses the finite-sample identification of the system matrix (A^*) for linear dynamical systems under convex set constraints, based on a single trajectory of length (T). To overcome the low sample efficiency of conventional unconstrained estimators, we propose a constrained least-squares estimation framework. We establish, for the first time, non-asymptotic error bounds for this estimator, explicitly quantifying how local geometric properties—such as the local Rademacher complexity—affect sample complexity. Our method integrates convex optimization with structured modeling to uniformly handle four canonical structural priors: sparsity, subspace constraints, convex regression, and Lipschitz row-wise constraints. Theoretically, we prove that, under such structural constraints, reliable estimation is achievable with significantly fewer samples than required in the unconstrained setting—thereby substantially improving identification efficiency in the small-sample regime.

2 citationsRead paper

Impact of diversity on bounded archives for multi-objective local search

Feb 04, 2026

This work addresses the challenges of rapidly growing nondominated solution sets and the tendency of search processes to become trapped in local regions of the Pareto front in multi-objective optimization. To overcome the limitations of existing approaches that focus solely on diversity in objective space, the authors propose a novel method emphasizing diversity in decision space. The approach incorporates a bounded archive mechanism based on Hamming distance and is rigorously evaluated against adaptive grid and hypervolume-based archiving strategies. Experimental results demonstrate that the proposed algorithm not only maintains a controllable archive size but also significantly enhances both the distribution and convergence efficiency of multi-objective local search, outperforming current state-of-the-art methods.

1 citations1 influentialRead paper

Monte Carlo with kernel-based Gibbs measures: Guarantees for probabilistic herding

Feb 18, 2024arXiv.org

This work addresses the lack of theoretical convergence advantages—specifically, rates faster than $O(n^{-1/2})$—for kernel herding in infinite-dimensional reproducing kernel Hilbert spaces (RKHS). We propose a deterministic sampling framework grounded in Gibbs measures, wherein node configurations are selected by minimizing the worst-case integration error under a suitably constructed joint distribution. This marks the first systematic incorporation of Gibbs measure theory into deterministic numerical integration analysis. Theoretically, our method yields tighter concentration inequalities for integration error in RKHS compared to i.i.d. Monte Carlo, establishing a strictly superior worst-case error bound. Empirically, preliminary experiments demonstrate super-root-$n$ convergence rates beyond the worst-case setting. The core innovation lies in the synergistic integration of Gibbs measures with worst-case error analysis, providing the first theoretically grounded acceleration mechanism for kernel herding.

1 citations1 influentialRead paper

Combinatorial Optimization Augmented Machine Learning

Jan 15, 2026

This work addresses the central challenge of integrating predictive models with combinatorial optimization to enable data-driven intelligent decision-making while preserving solution feasibility. It proposes a unified framework that embeds combinatorial optimization solvers directly into machine learning pipelines, systematically combining empirical risk minimization, imitation learning, and reinforcement learning. A key component of the framework is a feasibility-preserving mechanism designed to operate effectively in both static and dynamic settings. Beyond algorithmic integration, the study establishes a comprehensive problem taxonomy and algorithmic paradigm for this research direction, and provides a systematic review of theoretical foundations and applications in domains such as scheduling and routing, thereby offering a clear roadmap for future research.

1 citationsRead paper
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