Markus Walker

Sample-Efficient and Smooth Cross-Entropy Method Model Predictive Control Using Deterministic Samples

Markus Walker, Daniel R. Frisch, Uwe D. Hanebeck

Cross-entropy method model predictive control (CEM--MPC) is a powerful gradient-free technique for nonlinear optimal control, but its performance is often limited by the reliance on random sampling. This conventional approach can lead to inefficient exploration of the solution space and non-smooth control …

Weaknesses of the ANEES and New Calibration Measures for Multivariate Predictions

Markus Walker, Marcel Reith‐Braun, Uwe D. Hanebeck

Reliable quantification of uncertainty is crucial for trustworthy predictions in estimation theory and machine learning. However, existing credibility and calibration measures, such as the widely used averaged normalized estimation error squared (ANEES), often exhibit limitations when applied to biased model predictions. In …

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Local Calibration Testing in Supervised Machine Learning Models Using Input Space Kernels

Markus Walker, Marcel Reith‐Braun, Uwe D. Hanebeck

Bayesian machine learning models-especially Bayesian neural networks (BNNs)-offer powerful black-box approaches for prediction and uncertainty quantification. However, these models frequently exhibit inconsistent prediction quality across input regions, and conventional global metrics (e.g., the mean squared error (MSE)) are inadequate for capturing such …

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Bridging Bayesian Inference and Neural Network Training: Equivalence of KBNN and Statistical Linearization

Hayk Amirkhanian, Markus Walker, Uwe D. Hanebeck, Marco F. Huber

Accurate uncertainty quantification is critical for robust and trustworthy predictions in many real-world applications. Bayesian Neural Networks (BNNs) provide a principled approach for modeling uncertainty but are often limited by the computational complexity of Bayesian inference. In this paper, we introduce a …

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Bridging Bayesian Inference and Neural Network Training: Equivalence of KBNN and Statistical Linearization

Hayk Amirkhanian, Markus Walker, Uwe D. Hanebeck, Marco Huber

Accurate uncertainty quantification is critical for robust and trustworthy predictions in many real-world applications.Bayesian Neural Networks (BNNs) provide a principled approach for modeling uncertainty but are often limited by the computational complexity of Bayesian inference.In this paper, we introduce a statistical linearization …

Stochastic Optimal Control of an Optical Sorter With Material Recirculation

Markus Walker, Marcel Reith‐Braun, Albert Bauer, Florian Pfaff et autres

The optical bulk material sorting is a key technology on our way toward a circular economy and efficient recycling. However, controlling the sorting accuracy has so far been severely limited, as the achievable accuracy of conventional sorters is strongly determined by the …

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Trustworthy Bayesian Perceptrons

Markus Walker, Hayk Amirkhanian, Marco F. Huber, Uwe D. Hanebeck

Bayesian Neural Networks (BNNs) offer a sophisticated framework for extending classical neural network point estimates to encompass predictive distributions. Despite the high potential of BNNs, established BNN training methods such as Variational Inference (VI) and Markov Chain Monte Carlo (MCMC) grapple with …

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Identifying Trust Regions of Bayesian Neural Networks

Markus Walker, Marcel Reith‐Braun, Peter Schichtel, Mirko Knaak et autres

Bayesian neural networks (BNNs) offer an elegant and promising approach to deciding whether the predictions of a neural network are trustworthy by allowing the estimation of predictive distributions. However, training and prediction can only be performed approximately, and state-of-the-art approximation methods are …

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