Patrimony

The Louis Bachelier Group's patrimony has been defined as all the publications produced by academic researchers thanks to Group funding (ILB, FdR, IEF, Labex) or via the use of EquipEx data (BEDOFIH, EUROFIDAI).


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Computational learning noise in human decision-making.

Apprentissage par renforcement, Bayesian interference, Bruit computationnel, Cognitive process, Computational noise, Decision Making, Inférence Bayesienne, Neurosciences, Prise de décision, Processus cognitif, Reinforcement learning

Optimal control, statistical learning and order book modelling.

Apprentissage par renforcement, Carnet d’ordres, Ergodic properties, Hawkes processes, Limit order book, Modèle de file d’attente, Optimal trading, Processus de Hawkes, Propriétés ergodiques, Queuing model, Reinforcement learning, Trading optimal

Deep Reinforcement Learning for Market Making in Corporate Bonds: Beating the Curse of Dimensionality.

Actor-critic algorithms, Market making, Reinforcement learning, Stochastic optimal control

Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality.

Actor-critic algorithms, Market making, Reinforcement learning, Stochastic optimal control

Accelerated share repurchase and other buyback programs: what neural networks can bring.

ASR contracts, Deep learning, Optimal stopping, Recurrent neural networks, Reinforcement learning, Stochastic optimal control

Accelerated Share Repurchase and other buyback programs: what neural networks can bring.

ASR contracts, Deep learning, Optimal stopping, Recurrent neural networks, Reinforcement learning, Stochastic optimal control

Episodic reinforcement learning in finite MDPs: Minimax lower bounds revisited.

Episodic, Lower bounds, Reinforcement learning

Multi-Player Bandits Revisited.

Cognitive Radio, Decentralized algorithms, Multi-Armed Bandits, Opportunistic Spectrum Access, Reinforcement learning

Multi-Armed Bandit Learning in IoT Networks: Learning Helps Even in Non-stationary Settings.

Cognitive Radio, Internet of Things, Multi-Armed Bandits, Non-Stationary Bandits, Reinforcement Learning

Aggregation of multi-armed bandits learning algorithms for opportunistic spectrum access.

Aggregation algorithm, Aggregation of estimators, Cognitive radio, Learning theory, Multi-Armed Bandits, Reinforcement Learning

Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications.

Algorithms, Deep learning, Monte- Carlo, Performance iteration, Quantization, Reinforcement learning, Value iterations