Graph Bandits

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Beyond Binary: Continuous State Optimization with Graph-Structured Objectives
Aug 10, 2026Corinna Cortes, Yishay Mansour, Mehryar MohriFactor Graph Optimization Multi-Armed Bandits
Google Research · Tel Aviv University · Courant Institute
Learning from Local Walks on Dynamic Graphs with Bandit Feedback
Jul 12, 2026Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni +1Multi-Armed Bandits Graph Bandits
Department of Computer Science, University of Colorado, Boulder, CO 80309, USA · INRIA, Paris, France
Graph Dimensionality Reduction for Contextual Bandits: Structure-Specific Regret Bounds under Approximate Smoothness and Noisy Eigenspaces
Jun 26, 2026Joyanta Jyoti Mondal, Ibne Farabi Shihab, Anuj SharmaMulti-Armed Bandits Graph Bandits
Department of Computer and Information Sciences, University of Delaware, USA · Department of Computer Science, Iowa State University, USA · Department of Civil, Construction & Environmental Engineering, Iowa State University, USA
Graph Structured Combinatorial Semi-Bandit with Nonlinear Reward Associations through Separable Signals
Jun 12, 2026Christoph Bauschmann, Setareh MaghsudiReward Modeling Multi-Armed Bandits
Department of Electrical Engineering and Information Technology Ruhr University Bochum Bochum, Germany
Spectral bandits for smooth graph functions with applications in recommender systems
May 19, 2026Tomáš Kocák, Michal Valko, Rémi Munos +2Graph Signal Processing Multi-Armed Bandits
SequeL team Inria Lille France · SequeL team, Inria France · Microsoft Research New England +2
Bandits on graphs and structures
May 5, 2026Michal ValkoMulti-Armed Bandits Graph Bandits
L’ÉCOLE NORMALE SUPÉRIEURE DE CACHAN
Revealing graph bandits for maximizing local influence
May 1, 2026Alexandra Carpentier, Michal ValkoMulti-Armed Bandits Graph Bandits
Universität Potsdam · SequeL team, Inria Lille - Nord Europe
Spectral bandits
Apr 28, 2026Tomáš Kocák, Rémi Munos, Branislav Kveton +2Multi-Armed Bandits Graph Bandits
ENS de Lyon, 15 Parvis Ren´e Descartes, 69342 Lyon, France · Inria Lille – Nord Europe, SequeL team · DeepMind Paris, 14 Rue de Londres, 75009 Paris, France +2
Spectral bandits for smooth graph functions
Apr 20, 2026Michal Valko, Rémi Munos, Branislav Kveton +1Graph Signal Processing Multi-Armed Bandits
INRIA Lille - Nord Europe, SequeL team, 40 avenue Halley 59650, Villeneuve d’Ascq, France · INRIA Lille - Nord Europe, SequeL team, France · Microsoft Research New England, Cambridge, MA, USA +1
Bridging Rested and Restless Bandits with Graph-Triggering: Rising and Rotting
Sep 9, 2024Gianmarco Genalti, Marco Mussi, Nicola Gatti +3Multi-Armed Bandits Graph Bandits
Politecnico di Milano2026 Piazza Leonardo da Vinci 32, Milan, 20133, Italy

Aug 10, 2026, cs.LGJ/K move · Enter open · S save

Beyond Binary: Continuous State Optimization with Graph-Structured Objectives

Corinna Cortes, Yishay Mansour, Mehryar Mohri

Google Research · Tel Aviv University · Courant Institute

Factor Graph Optimization Multi-Armed Bandits Graph Bandits

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Large-scale learning systems often face the challenge of balancing multiple, potentially competing objectives, such as fairness, accuracy, and latency. While recent work has formalized this as an optimization problem over binary states, many real-world control parameters, such as fairness thresholds, diversity mixing rates, or resource budgets, are continuous. In this work, we extend the framework to \emph{continuous state spaces}. We model the problem as minimizing a sum of linear objectives subject to \emph{movement costs} that penalize system instability. We capture the local structure of the objectives using a \emph{dependency graph} (or factor graph), where each objective is determined by a subset of the state attributes. To address the tension between exploration and stability, we propose \emph{Lazy Graph-LinUCB}, an algorithm that performs lazy updates to minimize switching costs while maintaining near-optimal regret. Beyond stability, we introduce three advanced mechanisms to exploit the underlying graph structure: (1) an \emph{asynchronous} update schedule that eliminates synchronization overhead in sparse graphs; (2) an \emph{adaptive} algorithm that learns the graph structure from data; and (3) a \emph{joint estimator} that leverages data sharing among correlated objectives to significantly tighten regret bounds. Empirically, we demonstrate that these structural exploitations reduce movement costs by more than a factor of three in heterogeneous systems while maintaining similar cumulative losses.

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