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Found 9 papers, 1 papers with code
End-to-End Policy Learning of a Statistical Arbitrage Autoencoder Architecture
In Statistical Arbitrage (StatArb), classical mean reversion trading strategies typically hinge on asset-pricing or PCA based models to identify the mean of a synthetic asset.
Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets
We employ deep reinforcement learning (RL) to train an agent to successfully translate a high-frequency trading signal into a trading strategy that places individual limit orders.
Bayesian Topic Regression for Causal Inference
To this end, we combine a supervised Bayesian topic model with a Bayesian regression framework and perform supervised representation learning for the text features jointly with the regression parameter training, respecting the Frisch-Waugh-Lovell theorem.
Fast Agent-Based Simulation Framework with Applications to Reinforcement Learning and the Study of Trading Latency Effects
As a simple illustration, we employ our toolbox to investigate the role of the order processing delay in normal trading and for the scenario of a significant price change.
Safety Guarantees for Planning Based on Iterative Gaussian Processes
Gaussian Processes (GPs) are widely employed in control and learning because of their principled treatment of uncertainty.
Lipschitz Optimisation for Lipschitz Interpolation
Techniques known as Nonlinear Set Membership prediction, Kinky Inference or Lipschitz Interpolation are fast and numerically robust approaches to nonparametric machine learning that have been proposed to be utilised in the context of system identification and learning-based control.
Lazily Adapted Constant Kinky Inference for Nonparametric Regression and Model-Reference Adaptive Control
Techniques known as Nonlinear Set Membership prediction, Lipschitz Interpolation or Kinky Inference are approaches to machine learning that utilise presupposed Lipschitz properties to compute inferences over unobserved function values.
Conservative collision prediction and avoidance for stochastic trajectories in continuous time and space
Existing work in multi-agent collision prediction and avoidance typically assumes discrete-time trajectories with Gaussian uncertainty or that are completely deterministic.
Stochastic processes and feedback-linearisation for online identification and Bayesian adaptive control of fully-actuated mechanical systems
In contrast to previous work that has used stochastic processes for identification, we leverage the structural knowledge afforded by Lagrangian mechanics and learn the drift and control input matrix functions of the control-affine system separately.
