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Gilles Stoltz
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2020 – today
- 2024
- [j16]Hédi Hadiji, Sébastien Gerchinovitz, Jean-Michel Loubes, Gilles Stoltz:
Diversity-Preserving K-Armed Bandits, Revisited. Trans. Mach. Learn. Res. 2024 (2024) - [i23]Daniil Tiapkin, Evgenii Chzhen, Gilles Stoltz:
Narrowing the Gap between Adversarial and Stochastic MDPs via Policy Optimization. CoRR abs/2407.05704 (2024) - 2023
- [j15]Hédi Hadiji, Gilles Stoltz:
Adaptation to the Range in K-Armed Bandits. J. Mach. Learn. Res. 24: 13:1-13:33 (2023) - [c21]Antoine Barrier, Aurélien Garivier, Gilles Stoltz:
On Best-Arm Identification with a Fixed Budget in Non-Parametric Multi-Armed Bandits. ALT 2023: 136-181 - [c20]Evgenii Chzhen, Christophe Giraud, Zhen Li, Gilles Stoltz:
Small Total-Cost Constraints in Contextual Bandits with Knapsacks, with Application to Fairness. NeurIPS 2023 - [i22]Evgenii Chzhen, Christophe Giraud, Zhen Li, Gilles Stoltz:
Small Total-Cost Constraints in Contextual Bandits with Knapsacks, with Application to Fairness. CoRR abs/2305.15807 (2023) - [i21]Matthieu Jonckheere, Chiara Mignacco, Gilles Stoltz:
Symphony of experts: orchestration with adversarial insights in reinforcement learning. CoRR abs/2310.16473 (2023) - 2022
- [j14]Aurélien Garivier, Hédi Hadiji, Pierre Ménard, Gilles Stoltz:
KL-UCB-Switch: Optimal Regret Bounds for Stochastic Bandits from Both a Distribution-Dependent and a Distribution-Free Viewpoints. J. Mach. Learn. Res. 23: 179:1-179:66 (2022) - [c19]Zhen Li, Gilles Stoltz:
Contextual Bandits with Knapsacks for a Conversion Model. NeurIPS 2022 - [i20]Zhen Li, Gilles Stoltz:
Contextual Bandits with Knapsacks for a Conversion Model. CoRR abs/2206.00314 (2022) - [i19]Antoine Barrier, Aurélien Garivier, Gilles Stoltz:
On Best-Arm Identification with a Fixed Budget in Non-Parametric Multi-Armed Bandits. CoRR abs/2210.00895 (2022) - 2021
- [c18]Evgenii Chzhen, Christophe Giraud, Gilles Stoltz:
A Unified Approach to Fair Online Learning via Blackwell Approachability. NeurIPS 2021: 18280-18292 - [i18]Evgenii Chzhen
, Christophe Giraud, Gilles Stoltz:
A Unified Approach to Fair Online Learning via Blackwell Approachability. CoRR abs/2106.12242 (2021) - 2020
- [i17]Hédi Hadiji, Sébastien Gerchinovitz, Jean-Michel Loubes, Gilles Stoltz:
Diversity-Preserving K-Armed Bandits, Revisited. CoRR abs/2010.01874 (2020)
2010 – 2019
- 2019
- [j13]Aurélien Garivier, Pierre Ménard, Gilles Stoltz
Explore First, Exploit Next: The True Shape of Regret in Bandit Problems. Math. Oper. Res. 44(2): 377-399 (2019) - [c17]Pierre Gaillard, Sébastien Gerchinovitz, Malo Huard, Gilles Stoltz:
Uniform regret bounds over Rd for the sequential linear regression problem with the square loss. ALT 2019: 404-432 - [c16]Margaux Brégère, Pierre Gaillard, Yannig Goude, Gilles Stoltz:
Target Tracking for Contextual Bandits: Application to Demand Side Management. ICML 2019: 754-763 - [i16]Margaux Brégère, Pierre Gaillard, Yannig Goude, Gilles Stoltz:
Target Tracking for Contextual Bandits: Application to Demand Side Management. CoRR abs/1901.09532 (2019) - 2018
- [i15]Aurélien Garivier, Hédi Hadiji, Pierre Ménard, Gilles Stoltz:
KL-UCB-switch: optimal regret bounds for stochastic bandits from both a distribution-dependent and a distribution-free viewpoints. CoRR abs/1805.05071 (2018) - [i14]Pierre Gaillard, Sébastien Gerchinovitz, Malo Huard, Gilles Stoltz:
Uniform regret bounds over Rd for the sequential linear regression problem with the square loss. CoRR abs/1805.11386 (2018) - [i13]Raphaël Deswarte, Véronique Gervais, Gilles Stoltz, Sébastien Da Veiga:
Sequential model aggregation for production forecasting. CoRR abs/1812.10389 (2018) - 2017
- [i12]Sébastien Gerchinovitz, Pierre Ménard, Gilles Stoltz:
Fano's inequality for random variables. CoRR abs/1702.05985 (2017) - 2016
- [i11]Aurélien Garivier, Pierre Ménard, Gilles Stoltz:
Explore First, Exploit Next: The True Shape of Regret in Bandit Problems. CoRR abs/1602.07182 (2016) - 2014
- [j12]Shie Mannor, Vianney Perchet, Gilles Stoltz:
Set-valued approachability and online learning with partial monitoring. J. Mach. Learn. Res. 15(1): 3247-3295 (2014) - [j11]Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann:
Guest Editors' foreword. Theor. Comput. Sci. 558: 1-4 (2014) - [c15]Pierre Gaillard, Gilles Stoltz, Tim van Erven:
A second-order bound with excess losses. COLT 2014: 176-196 - [c14]Shie Mannor, Vianney Perchet, Gilles Stoltz:
Approachability in unknown games: Online learning meets multi-objective optimization. COLT 2014: 339-355 - [i10]Shie Mannor
Approachability in unknown games: Online learning meets multi-objective optimization. CoRR abs/1402.2043 (2014) - [i9]Pierre Gaillard, Gilles Stoltz, Tim van Erven:
A Second-order Bound with Excess Losses. CoRR abs/1402.2044 (2014) - 2013
- [j10]Marie Devaine, Pierre Gaillard, Yannig Goude, Gilles Stoltz:
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. Mach. Learn. 90(2): 231-260 (2013) - [i8]Shie Mannor
A Primal Condition for Approachability with Partial Monitoring. CoRR abs/1305.5399 (2013) - 2012
- [c13]Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann:
Editors' Introduction. ALT 2012: 1-11 - [c12]Nicolò Cesa-Bianchi, Pierre Gaillard, Gábor Lugosi, Gilles Stoltz:
Mirror Descent Meets Fixed Share (and feels no regret). NIPS 2012: 989-997 - [e1]Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann:
Algorithmic Learning Theory - 23rd International Conference, ALT 2012, Lyon, France, October 29-31, 2012. Proceedings. Lecture Notes in Computer Science 7568, Springer 2012, ISBN 978-3-642-34105-2 [contents] - [i7]Nicolò Cesa-Bianchi, Pierre Gaillard, Gábor Lugosi, Gilles Stoltz:
A new look at shifting regret. CoRR abs/1202.3323 (2012) - [i6]Marie Devaine, Pierre Gaillard, Yannig Goude, Gilles Stoltz:
Forecasting electricity consumption by aggregating specialized experts. CoRR abs/1207.1965 (2012) - 2011
- [j9]Sébastien Bubeck, Rémi Munos, Gilles Stoltz, Csaba Szepesvári:
X-Armed Bandits. J. Mach. Learn. Res. 12: 1655-1695 (2011) - [j8]Sébastien Bubeck, Rémi Munos, Gilles Stoltz:
Pure exploration in finitely-armed and continuous-armed bandits. Theor. Comput. Sci. 412(19): 1832-1852 (2011) - [c11]Sébastien Bubeck, Gilles Stoltz, Jia Yuan Yu:
Lipschitz Bandits without the Lipschitz Constant. ALT 2011: 144-158 - [c10]Odalric-Ambrym Maillard, Rémi Munos, Gilles Stoltz:
A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences. COLT 2011: 497-514 - [c9]Shie Mannor, Vianney Perchet, Gilles Stoltz:
Robust approachability and regret minimization in games with partial monitoring. COLT 2011: 515-536 - [i5]Shie Mannor
Robust approachability and regret minimization in games with partial monitoring. CoRR abs/1105.4995 (2011) - 2010
- [j7]Shie Mannor
A Geometric Proof of Calibration. Math. Oper. Res. 35(4): 721-727 (2010) - [i4]Sébastien Bubeck, Rémi Munos, Gilles Stoltz, Csaba Szepesvári:
X-Armed Bandits. CoRR abs/1001.4475 (2010)
2000 – 2009
- 2009
- [c8]Sébastien Bubeck, Rémi Munos, Gilles Stoltz:
Pure Exploration in Multi-armed Bandits Problems. ALT 2009: 23-37 - [c7]Gábor Lugosi, Omiros Papaspiliopoulos, Gilles Stoltz:
Online Multi-task Learning with Hard Constraints. COLT 2009 - [i3]Gábor Lugosi, Omiros Papaspiliopoulos, Gilles Stoltz:
Online Multi-task Learning with Hard Constraints. CoRR abs/0902.3526 (2009) - 2008
- [j6]Gábor Lugosi
Strategies for Prediction Under Imperfect Monitoring. Math. Oper. Res. 33(3): 513-528 (2008) - [c6]Sébastien Bubeck, Rémi Munos, Gilles Stoltz, Csaba Szepesvári:
Online Optimization in X-Armed Bandits. NIPS 2008: 201-208 - [i2]Sébastien Bubeck, Rémi Munos, Gilles Stoltz:
Pure Exploration for Multi-Armed Bandit Problems. CoRR abs/0802.2655 (2008) - 2007
- [j5]Gilles Stoltz, Gábor Lugosi
Learning correlated equilibria in games with compact sets of strategies. Games Econ. Behav. 59(1): 187-208 (2007) - [j4]Nicolò Cesa-Bianchi, Yishay Mansour, Gilles Stoltz:
Improved second-order bounds for prediction with expert advice. Mach. Learn. 66(2-3): 321-352 (2007) - [c5]Gábor Lugosi, Shie Mannor
Strategies for Prediction Under Imperfect Monitoring. COLT 2007: 248-262 - [i1]Gábor Lugosi, Shie Mannor
Strategies for prediction under imperfect monitoring. CoRR abs/math/0701419 (2007) - 2006
- [j3]Nicolò Cesa-Bianchi, Gábor Lugosi
Regret Minimization Under Partial Monitoring. Math. Oper. Res. 31(3): 562-580 (2006) - [c4]Nicolò Cesa-Bianchi, Gábor Lugosi, Gilles Stoltz:
Regret Minimization Under Partial Monitoring. ITW 2006: 72-76 - 2005
- [j2]Gilles Stoltz, Gábor Lugosi
Internal Regret in On-Line Portfolio Selection. Mach. Learn. 59(1-2): 125-159 (2005) - [j1]Nicolò Cesa-Bianchi, Gábor Lugosi
Minimizing regret with label efficient prediction. IEEE Trans. Inf. Theory 51(6): 2152-2162 (2005) - [c3]Nicolò Cesa-Bianchi, Yishay Mansour, Gilles Stoltz:
Improved Second-Order Bounds for Prediction with Expert Advice. COLT 2005: 217-232 - 2004
- [c2]Nicolò Cesa-Bianchi, Gábor Lugosi, Gilles Stoltz:
Minimizing Regret with Label Efficient Prediction. COLT 2004: 77-92 - 2003
- [c1]Gilles Stoltz, Gábor Lugosi:
Internal Regret in On-Line Portfolio Selection. COLT 2003: 403-417
Coauthor Index
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