no code implementations • 28 May 2024 • Onno Eberhard, Claire Vernade, Michael Muehlebach
Reinforcement learning has traditionally focused on learning state-dependent policies to solve optimal control problems in a closed-loop fashion.
no code implementations • 17 May 2024 • Guner Dilsad Er, Sebastian Trimpe, Michael Muehlebach
We also characterize the effect of communication drops and demonstrate that our algorithm is robust to communication failures.
no code implementations • 8 Apr 2024 • Hao Ma, Melanie Zeilinger, Michael Muehlebach
We propose a novel gradient-based online optimization framework for solving stochastic programming problems that frequently arise in the context of cyber-physical and robotic systems.
no code implementations • 19 Mar 2024 • Liang Zhang, Niao He, Michael Muehlebach
In this work, we propose a simple primal method, termed Constrained Gradient Method (CGM), for addressing functional constrained variational inequality problems, without necessitating any information on the optimal Lagrange multipliers.
no code implementations • 8 Feb 2024 • Jasan Zughaibi, Bradley J. Nelson, Michael Muehlebach
This greatly expands the range of potential medical applications and includes even dynamic environments as encountered in cardiovascular interventions.
no code implementations • 25 Jan 2024 • Florian Dörfler, Zhiyu He, Giuseppe Belgioioso, Saverio Bolognani, John Lygeros, Michael Muehlebach
Traditionally, numerical algorithms are seen as isolated pieces of code confined to an {\em in silico} existence.
no code implementations • 11 Oct 2023 • Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach
Counterfactuals answer questions of what would have been observed under altered circumstances and can therefore offer valuable insights.
1 code implementation • 9 Jun 2023 • Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach
We study causal effect estimation from a mixture of observational and interventional data in a confounded linear regression model with multivariate treatments.
no code implementations • 24 May 2023 • Jan Achterhold, Philip Tobuschat, Hao Ma, Dieter Buechler, Michael Muehlebach, Joerg Stueckler
Our gray-box approach builds on a physical model.
no code implementations • 6 Apr 2023 • Michael Muehlebach
We consider adaptive decision-making problems where an agent optimizes a cumulative performance objective by repeatedly choosing among a finite set of options.
no code implementations • 16 Mar 2023 • Sholom Schechtman, Daniil Tiapkin, Michael Muehlebach, Eric Moulines
We consider the problem of minimizing a non-convex function over a smooth manifold $\mathcal{M}$.
no code implementations • 1 Feb 2023 • Michael Muehlebach, Michael I. Jordan
We exploit analogies between first-order algorithms for constrained optimization and non-smooth dynamical systems to design a new class of accelerated first-order algorithms for constrained optimization.
1 code implementation • 12 Dec 2022 • Daniel Frank, Decky Aspandi Latif, Michael Muehlebach, Benjamin Unger, Steffen Staab
In this work, we represent a recurrent neural network as a linear time-invariant system with nonlinear disturbances.
no code implementations • 7 Jun 2022 • Aniket Das, Bernhard Schölkopf, Michael Muehlebach
We obtain tight convergence rates for RR and SO and demonstrate that these strategies lead to faster convergence than uniform sampling.
no code implementations • 17 Jul 2021 • Michael Muehlebach, Michael I. Jordan
We introduce a class of first-order methods for smooth constrained optimization that are based on an analogy to non-smooth dynamical systems.
no code implementations • 28 Feb 2020 • Michael Muehlebach, Michael. I. Jordan
We analyze the convergence rate of various momentum-based optimization algorithms from a dynamical systems point of view.
no code implementations • ICML 2020 • Michael Muehlebach, Michael. I. Jordan
This article derives lower bounds on the convergence rate of continuous-time gradient-based optimization algorithms.
Optimization and Control Systems and Control Systems and Control
no code implementations • 26 May 2019 • N. Benjamin Erichson, Michael Muehlebach, Michael W. Mahoney
In addition to providing high-profile successes in computer vision and natural language processing, neural networks also provide an emerging set of techniques for scientific problems.
no code implementations • 17 May 2019 • Michael Muehlebach, Michael. I. Jordan
We present a dynamical system framework for understanding Nesterov's accelerated gradient method.