Internal Regret in On-Line Portfolio Selection. - Gilles Stoltz; Gábor Lugosi (Mach. Learn., 2005)

Minimizing regret with label efficient prediction. - Nicolò Cesa-Bianchi; Gábor Lugosi; Gilles Stoltz (IEEE Trans. Inf. Theory, 2005)

Regret Minimization Under Partial Monitoring. - Nicolò Cesa-Bianchi; Gábor Lugosi; Gilles Stoltz (Math. Oper. Res., 2006)

Learning correlated equilibria in games with compact sets of strategies. - Gilles Stoltz; Gábor Lugosi (Games Econ. Behav., 2007)

Improved second-order bounds for prediction with expert advice. - Nicolò Cesa-Bianchi; Yishay Mansour; Gilles Stoltz (Mach. Learn., 2007)

Online Optimization in X-Armed Bandits. - Sébastien Bubeck; Rémi Munos; Gilles Stoltz; Csaba Szepesvári (NIPS, 2008)

Pure Exploration for Multi-Armed Bandit Problems - Sébastien Bubeck; Rémi Munos; Gilles Stoltz (CoRR, 2008)

Strategies for Prediction Under Imperfect Monitoring. - Gábor Lugosi; Shie Mannor; Gilles Stoltz (Math. Oper. Res., 2008)

Pure Exploration in Multi-armed Bandits Problems. - Sébastien Bubeck; Rémi Munos; Gilles Stoltz (ALT, 2009)

Online Multi-task Learning with Hard Constraints. - Gábor Lugosi; Omiros Papaspiliopoulos; Gilles Stoltz (COLT, 2009)

X-Armed Bandits - Sébastien Bubeck; Rémi Munos; Gilles Stoltz; Csaba Szepesvári (CoRR, 2010)

A Geometric Proof of Calibration. - Shie Mannor; Gilles Stoltz (Math. Oper. Res., 2010)

Lipschitz Bandits without the Lipschitz Constant. - Sébastien Bubeck; Gilles Stoltz; Jia Yuan Yu (ALT, 2011)

X-Armed Bandits. - Sébastien Bubeck; Rémi Munos; Gilles Stoltz; Csaba Szepesvári (J. Mach. Learn. Res., 2011)

A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences. - Odalric-Ambrym Maillard; Rémi Munos; Gilles Stoltz (COLT, 2011)

Robust approachability and regret minimization in games with partial monitoring. - Shie Mannor; Vianney Perchet; Gilles Stoltz (COLT, 2011)

Pure exploration in finitely-armed and continuous-armed bandits. - Sébastien Bubeck; Rémi Munos; Gilles Stoltz (Theor. Comput. Sci., 2011)

Editors' Introduction. - Nader H. Bshouty; Gilles Stoltz; Nicolas Vayatis; Thomas Zeugmann (ALT, 2012)

Mirror Descent Meets Fixed Share (and feels no regret). - Nicolò Cesa-Bianchi; Pierre Gaillard; Gábor Lugosi; Gilles Stoltz (NIPS, 2012)

A new look at shifting regret - Nicolò Cesa-Bianchi; Pierre Gaillard; Gábor Lugosi; Gilles Stoltz (CoRR, 2012)

Forecasting electricity consumption by aggregating specialized experts - Marie Devaine; Pierre Gaillard; Yannig Goude; Gilles Stoltz (CoRR, 2012)

A Primal Condition for Approachability with Partial Monitoring - Shie Mannor; Vianney Perchet; Gilles Stoltz (CoRR, 2013)

Forecasting electricity consumption by aggregating specialized experts - A review of the sequential aggregation of specialized experts, with an application to Slovakian and French country-wide one-day-ahead (half-)hourly predictions. - Marie Devaine; Pierre Gaillard; Yannig Goude; Gilles Stoltz (Mach. Learn., 2013)

A second-order bound with excess losses. - Pierre Gaillard; Gilles Stoltz; Tim van Erven (COLT, 2014)

Approachability in unknown games: Online learning meets multi-objective optimization. - Shie Mannor; Vianney Perchet; Gilles Stoltz (COLT, 2014)

Set-valued approachability and online learning with partial monitoring. - Shie Mannor; Vianney Perchet; Gilles Stoltz (J. Mach. Learn. Res., 2014)

Guest Editors' foreword. - Nader H. Bshouty; Gilles Stoltz; Nicolas Vayatis; Thomas Zeugmann (Theor. Comput. Sci., 2014)

Fano's inequality for random variables. - Sébastien Gerchinovitz; Pierre Ménard; Gilles Stoltz (CoRR, 2017)

Sequential model aggregation for production forecasting. - Raphaël Deswarte; Véronique Gervais; Gilles Stoltz; Sébastien Da Veiga (CoRR, 2018)

Uniform regret bounds over Rd for the sequential linear regression problem with the square loss. - Pierre Gaillard; Sébastien Gerchinovitz; Malo Huard; Gilles Stoltz (ALT, 2019)

Target Tracking for Contextual Bandits: Application to Demand Side Management. - Margaux Brégère; Pierre Gaillard; Yannig Goude; Gilles Stoltz (ICML, 2019)

Explore First, Exploit Next: The True Shape of Regret in Bandit Problems. - Aurélien Garivier; Pierre Ménard; Gilles Stoltz (Math. Oper. Res., 2019)

A Unified Approach to Fair Online Learning via Blackwell Approachability. - Evgenii Chzhen; Christophe Giraud 002; Gilles Stoltz (NeurIPS, 2021)

Contextual Bandits with Knapsacks for a Conversion Model. - Zhen Li; Gilles Stoltz (NeurIPS, 2022)

KL-UCB-Switch: Optimal Regret Bounds for Stochastic Bandits from Both a Distribution-Dependent and a Distribution-Free Viewpoints. - Aurélien Garivier; Hédi Hadiji; Pierre Ménard; Gilles Stoltz (J. Mach. Learn. Res., 2022)

On Best-Arm Identification with a Fixed Budget in Non-Parametric Multi-Armed Bandits. - Antoine Barrier; Aurélien Garivier; Gilles Stoltz (ALT, 2023)

Small Total-Cost Constraints in Contextual Bandits with Knapsacks, with Application to Fairness. - Evgenii Chzhen; Christophe Giraud 002; Zhen Li; Gilles Stoltz (NeurIPS, 2023)

Symphony of experts: orchestration with adversarial insights in reinforcement learning. - Matthieu Jonckheere; Chiara Mignacco; Gilles Stoltz (CoRR, 2023)

Adaptation to the Range in K-Armed Bandits. - Hédi Hadiji; Gilles Stoltz (J. Mach. Learn. Res., 2023)

Conformal Prediction for Hierarchical Data. - Guillaume Principato; Yvenn Amara-Ouali; Yannig Goude; Bachir Hamrouche; Jean-Michel Poggi; Gilles Stoltz (CoRR, 2024)

Diversity-Preserving K-Armed Bandits, Revisited. - Hédi Hadiji; Sébastien Gerchinovitz; Jean-Michel Loubes; Gilles Stoltz (Trans. Mach. Learn. Res., 2024)

Narrowing the Gap between Adversarial and Stochastic MDPs via Policy Optimization. - Daniil Tiapkin; Evgenii Chzhen; Gilles Stoltz (AISTATS, 2025)

Policy Optimization via Adv2: Adversarial Learning on Advantage Functions. - Matthieu Jonckheere; Chiara Mignacco; Gilles Stoltz (Trans. Mach. Learn. Res., 2025)