Title: Multi-Objective Algorithm Configuration with Applications to Sparse Neural Networks

Abstract: Modern AI systems involve many interacting design choices that together define large, heterogeneous configuration spaces. Furthermore, they must balance conflicting objectives such as performance and resource usage. Yet, most automated algorithm configuration methods still focus on optimising for a single objective. In this talk, I present a multi-objective extension of the SMAC framework that directly targets Pareto-optimal configurations using predicted hypervolume improvement and a novel intensification strategy. I demonstrate the effectiveness of our approach across multiple AI domains and illustrate its practical value through a case study on sparse neural network training. The results show that significant efficiency gains are possible with only minor accuracy trade-offs, while also uncovering complex interactions between sparsity and other hyperparameters. This work highlights multi-objective configuration as an important tool for building and understanding efficient and trustworthy AI systems.

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Title: Cayley Based-Methods for Quantum Optimal Control Systems

Abstract: This talk present a new family of structure-preserving integrators based on the Cayley commutator-free (CF–Cayley) formulation. The central idea is to replace expensive matrix
exponentials by Cayley transforms, simple linear solves that automatically evolve on the unitary manifold, while avoiding nested commutators. This yields algorithms that are fast, symmetric,
and exactly norm-preserving, making them ideally suited for repeated forward and backward propagations in optimal control algorithms such as Krotov’s method.
Beyond quantum control, this approach provides a general framework for the geometric integration of differential equations on Lie groups, bridging ideas from applied mathematics, physics, and
numerical analysis. It demonstrates how respecting the underlying geometry of a system, rather than merely discretizing its equations, can lead to both deeper insight and practical computational
gains.

Title: High-order Cayley based methods for quantum optimal control problems

Abstract: Differential equations posed on quadratic matrix Lie groups arise in the context of classical mechanics and quantum dynamical systems. Lie group numerical integrators preserve the constants of motion defining the Lie group. Thus, they respect important physical laws of the dynamical system, such as unitarity and energy conservation in the context of quantum dynamical systems, for instance.

This thesis concerns the construction, implementation, and numerical analysis of a novel Cayley-based, commutator-free multistep scheme for general differential equations evolving on quadratic Lie groups, with an emphasis on applications in quantum optimal control. The method builds on Cayley commutator-free schemes recently introduced, but, in contrast, is not restricted to linear equations; it can, for example, handle dynamics governed by nonlinear Schrödinger equations.

We conduct numerical experiments to compare accuracy and computational performance with established Lie group integrators. We also discuss applications to quantum optimal control problems whose dynamics are governed by the nonlinear Schrödinger (Gross–Pitaevskii) equation, highlighting a design advantage afforded by the new method in this setting.

Title: Variation integrators for stochatic Hamiltonian systems

Abstract: We develop variational integrators for stochastic Hamiltonian systems. Stochastic Hamilton’s equations are the stochastic extension of Hamilton’s equations, which can be derived from a stochastic version of the phase space principle with an additional stochastic term expressed as a Stratonovich integral of stochastic Hamiltonians. Variational integrators can be constructed from the discretization of the stochastic variational principle. We construct such integrators for systems whose configuration space is a Lie group, and also for systems with advected parameters. We focus on the midpoint method, which provides the most basic strongly convergent integrator, and explore the geometry-preserving properties of the resulting integrator. Strong convergence is proven for the special case of stochastic rigid body equations. Various applications to both finite-dimensional and infinite-dimensional systems are also presented.