19 citations · 36 across the 6 of their papers we have counts for
9 papers
Bayesian Topic Regression for Causal Inference
Maximilian Ahrens, Julian Ashwin, Jan-Peter Calliess +1
Causal inference using observational text data is becoming increasingly popular in many research areas. This paper presents the Bayesian Topic Regression (BTR) model that uses both…
Safety Guarantees for Planning Based on Iterative Gaussian Processes
Kyriakos Polymenakos, Luca Laurenti, Andrea Patane +5
Gaussian Processes (GPs) are widely employed in control and learning because of their principled treatment of uncertainty. However, tracking uncertainty for iterative, multi-step p…
Online Optimisation for Online Learning and Control -- From No-Regret to Generalised Error Convergence
Jan-P. Calliess
This paper presents early work aiming at the development of a new framework for the design and analysis of algorithms for online learning based prediction and control. Firstly, we…
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation
Ahsan S. Alvi, Binxin Ru, Jan Calliess +2
Batch Bayesian optimisation (BO) has been successfully applied to hyperparameter tuning using parallel computing, but it is wasteful of resources: workers that complete jobs ahead…
Lipschitz Optimisation for Lipschitz Interpolation
Jan-Peter Calliess
Techniques known as Nonlinear Set Membership prediction, Kinky Inference or Lipschitz Interpolation are fast and numerically robust approaches to nonparametric machine learning tha…
Lazily Adapted Constant Kinky Inference for Nonparametric Regression and Model-Reference Adaptive Control
Jan-Peter Calliess
Techniques known as Nonlinear Set Membership prediction, Lipschitz Interpolation or Kinky Inference are approaches to machine learning that utilise presupposed Lipschitz properties…
Conservative collision prediction and avoidance for stochastic trajectories in continuous time and space
Jan-Peter Calliess, Michael Osborne, Stephen Roberts
Existing work in multi-agent collision prediction and avoidance typically assumes discrete-time trajectories with Gaussian uncertainty or that are completely deterministic. We prop…
Stochastic processes and feedback-linearisation for online identification and Bayesian adaptive control of fully-actuated mechanical systems
Jan-Peter Calliess, Antonis Papachristodoulou, Stephen J. Roberts
This work proposes a new method for simultaneous probabilistic identification and control of an observable, fully-actuated mechanical system. Identification is achieved by conditio…
Chance-constrained Model Predictive Control for Multi-Agent Systems
Daniel Lyons, Jan-P. Calliess, Uwe D. Hanebeck
We consider stochastic model predictive control of a multi-agent systems with constraints on the probabilities of inter-agent collisions. We first study a sample-based approximatio…