JAIR 2020

• Ronald Ortner: Regret Bounds for Reinforcement Learning via Markov Chain Concentration

AISTATS 2020

• Xuedong Shang, Rianne de Heide, Emilie Kaufmann, Pierre Ménard, Michal Valko: Fixed-Confidence Guarantees for Bayesian Best-Arm Identification
• Julien Seznec, Pierre Ménard, Alessandro Lazaric, Michal Valko: A single algorithm for both restless and rested rotting bandits

ICMA 2020

• Aurélien Garivier, Pierre Ménard, Laurent Rossi: Thresholding Bandit for Dose-ranging: The Impact of Monotonicity

AAAI 2020

• Daniel Furelos-Blanco, Mark Law, Alessandra Russo, Krysia Broda, Anders Jonsson: Induction of Subgoal Automata for Reinforcement Learning
• Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson: Generalized Planning with Positive and Negative Examples

AIJ 2019

• Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson: Computing programs for generalized planning using a classical planner

NeurIPS 2019

• Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric Maillard: Regret Bounds for Learning State Representations in Reinforcement Learning
• Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Ménard, Rémi Munos, Michal Valko: Planning in entropy-regularized Markov decision processes and games
• Rémy Degenne, Wooter Koolen, Pierre Ménard: Non-Asymptotic Pure Exploration by Solving Games

UAI 2019

• Ronald Ortner, Pratik Gajane, Peter Auer: Variational Regret Bounds for Reinforcement Learning

COLT 2019

• Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco: Gaussian process optimization with adaptive sketching: Scalable and no regret
• Peter Auer, Pratik Gajane, Ronald Ortner: Adaptively Tracking the Best Bandit Arm with an Unknown Number of
  Distribution Changes
• Peter Auer, Yifang Chen, Pratik Gajane, Chung-Wei Lee, Haipeng Luo, Ronald Ortner, Chen-Yu Wei: Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information

ICML 2019

• Pierre Perrault, Vianney Perchet, Michal Valko: Exploiting structure of uncertainty for efficient combinatorial semi-bandits
• Peter Bartlett, Victor Gabillon, Jennifer Healey, Michal Valko: Scale-free adaptive planning for deterministic dynamics with discounted rewards

AutoML@ICML 2019

• Xuedong Shang, Emilie Kaufmann, Michal Valko: A simple dynamic bandit-based algorithm for hyper-parameter tuning

ALT 2019

• Xuedong Shang, Emilie Kaufmann, Michal Valko: General parallel optimization without metric
• Peter L. Bartlett, Victor Gabillon, Michal Valko: A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption

AISTATS 2019

• Julien Seznec, Andrea Locatelli, Alexandra Carpentier, Alessandro Lazaric, Michal Valko: Rotting bandits are no harder than stochastic ones
• Pierre Perrault, Vianney Perchet, Michal Valko: Finding the bandit in a graph: Sequential search-and-stop
• Andrea Locatelli, Alexandra Carpentier, Michal Valko: Active multiple matrix completion with adaptive confidence sets

ICAPS 2019

• Miquel Junyent, Anders Jonsson, Vicenç Gómez: Deep Policies for Width-Based Planning in Pixel Domains

AAAI 2019

• Daniel Furelos-Blanco, Anders Jonsson: Solving Multiagent Planning Problems with Concurrent Conditional Effects

IWSDS 2019

• Eneko Agirre, Anders Jonsson, Anthony Larcher: Framing Lifelong Learning as Autonomous Deployment: Tune Once Live Forever

GRETSI 2019

• Lilian Besson, Emilie Kaufmann: Non-asymptotic analysis of a sequential change-point detection test and applications to non-stationary bandits

JAIR 2018

• Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson: Computing Hierarchical Finite State Controllers with Classical Planning

PAL 2018

• Miquel Junyent, Anders Jonsson, Vicenç Gómez: Improving width-based planning with compact policies

EWRL 2018

• Peter Auer, Pratik Gajane, and Ronald Ortner Adaptively Tracking the Best Arm with an Unknown Number of Distribution Changes
• Xuedong Shang, Emilie Kaufmann, Michal Valko: Adaptive black-box optimization got easier: HCT needs only local smoothness