{"id":607,"date":"2021-08-31T20:20:57","date_gmt":"2021-08-31T20:20:57","guid":{"rendered":"https:\/\/elo-x.eu\/?p=607"},"modified":"2024-04-30T18:27:35","modified_gmt":"2024-04-30T18:27:35","slug":"ramin-abbasi","status":"publish","type":"post","link":"https:\/\/elo-x.eu\/?p=607","title":{"rendered":"Amon Lahr"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\n\t\t\t\tAmon Lahr \n\t\t\t<\/h2 > \n\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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PhD Candidate in Mechanical and Process Engineering<\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Institute for Dynamic Systems and Control<\/b><\/span><\/p>

ETH Z\u00fcrich<\/b><\/p><\/div>

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Amon Lahr was born in Berlin, Germany, in 1996. He completed his Bachelor’s studies in Engineering<\/p>

Science in 2018, with two semester-long stays at New York University and the German Aerospace<\/p>

Center in Stuttgart, Germany, respectively. Redirecting his study focus towards numerical mathematics, control theory and model reduction, Amon later received a Master’s degree in Scientific Computing from TU Berlin in 2021, with his thesis on “\u210b-\u221e Control for Large-Scale Linear Systems”. During his studies, Amon has worked as a Linux and web developer for IoT devices in the automotive industry, shaping his interests in embedded systems and data-driven control methods.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Project description<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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While the performance and potential of learning-based control has been recently demonstrated, the associated computational challenges remain a key limiting factor for moving these techniques into industrial applications. On embedded hardware in particular, the feasible model complexity and sampling times are restricted by the limited storage and computational power. The aim of this PhD project is to develop new controllers and computational methods for embedded control systems. Possible research directions include the development of tailored real-time optimization routines, controller approximation using learning-based function approximation schemes, as well as efficient data selection and reduction.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tRead more about this project<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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1.<\/div>

Lahr, Amon; K\u00f6hler, Johannes; Scampicchio, Anna; Zeilinger, Melanie N.<\/p>

Optimal Kernel Regression Bounds under Energy-Bounded Noise<\/a> Working paper<\/span> <\/p>

2025<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@workingpaper{lahr_optimal_2025,
\r\ntitle = {Optimal Kernel Regression Bounds under Energy-Bounded Noise},
\r\nauthor = {Amon Lahr and Johannes K\u00f6hler and Anna Scampicchio and Melanie N. Zeilinger},
\r\ndoi = {10.48550\/arXiv.2505.22235},
\r\nyear  = {2025},
\r\ndate = {2025-05-28},
\r\nabstract = {Non-conservative uncertainty bounds are key for both assessing an estimation algorithm's accuracy and in view of downstream tasks, such as its deployment in safety-critical contexts. In this paper, we derive a tight, non-asymptotic uncertainty bound for kernel-based estimation, which can also handle correlated noise sequences. Its computation relies on a mild norm-boundedness assumption on the unknown function and the noise, returning the worst-case function realization within the hypothesis class at an arbitrary query input location. The value of this function is shown to be given in terms of the posterior mean and covariance of a Gaussian process for an optimal choice of the measurement noise covariance. By rigorously analyzing the proposed approach and comparing it with other results in the literature, we show its effectiveness in returning tight and easy-to-compute bounds for kernel-based estimates.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {workingpaper}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Non-conservative uncertainty bounds are key for both assessing an estimation algorithm's accuracy and in view of downstream tasks, such as its deployment in safety-critical contexts. In this paper, we derive a tight, non-asymptotic uncertainty bound for kernel-based estimation, which can also handle correlated noise sequences. Its computation relies on a mild norm-boundedness assumption on the unknown function and the noise, returning the worst-case function realization within the hypothesis class at an arbitrary query input location. The value of this function is shown to be given in terms of the posterior mean and covariance of a Gaussian process for an optimal choice of the measurement noise covariance. By rigorously analyzing the proposed approach and comparing it with other results in the literature, we show its effectiveness in returning tight and easy-to-compute bounds for kernel-based estimates.<\/div>
Close<\/a><\/p><\/div>