University of Bonn

Selected work

Representative Papers

Approximating Nash Social Welfare by Matching and Local Search

Nov 07, 2022Symposium on the Theory of Computing

This paper studies Nash social welfare (NSW) maximization under submodular utilities, addressing both symmetric and weighted (asymmetric) settings, while simultaneously pursuing approximation efficiency and fairness—specifically EFX. We propose the first deterministic algorithmic framework that integrates bipartite matching with local search. For the symmetric case, it achieves a $(4+varepsilon)$-approximation to optimal NSW, drastically improving upon the previous best ratio of 380; for the weighted case, it attains a $(omega+2+varepsilon)$-approximation, where $omega$ is the largest weight ratio. Crucially, it is the first polynomial-time algorithm to simultaneously guarantee $12$-EFX fairness and $(8+varepsilon)$-NSW approximation—breaking the prior barrier that precluded constant-factor NSW approximations under EFX. Our core innovation lies in unifying matching structures with submodular optimization, leveraging a weighted geometric mean objective to jointly approximate efficiency and fairness.

18 citations5 influentialRead paper

Projection Inference for set-identified SVARs

Apr 18, 2025

This paper addresses inference challenges for structural vector autoregressive (SVAR) models under set identification. We propose a projection-based inferential method that simultaneously delivers asymptotic frequentist coverage and robust Bayesian credibility: the Wald ellipsoid for reduced-form parameters is projected onto the structural parameter space to construct joint confidence regions. We establish, for the first time in general stationary SVARs, that this projection method achieves asymptotic 1−α frequentist coverage and robust Bayesian credibility. Moreover, we introduce a posterior-calibrated radius adjustment algorithm that ensures exact robust credibility of 1−α while guaranteeing precise 1−α coverage over the identification set. Theoretically, our work unifies dual guarantees—frequentist and robust Bayesian—within a coherent framework; computationally, it remains efficient and implementable. Empirically, we replicate the Baumeister–Hamilton (2015) labor supply–demand model, demonstrating the method’s tightness and robustness.

15 citations1 influentialRead paper

Flexible Covariate Adjustments in Regression Discontinuity Designs

Jul 16, 2021

To address low covariate efficiency and poor scalability to high-dimensional settings in regression discontinuity (RD) designs, this paper proposes a novel class of covariate-adjusted estimators. The method achieves efficient adjustment by subtracting from the outcome variable an optimal nonparametric prediction function of the covariates. Crucially, it preserves the intrinsic robustness of RD estimation while enabling flexible, data-driven estimation of the adjustment function via modern machine learning tools—including Lasso and random forests. Notably, it is the first RD adjustment framework that simultaneously attains asymptotic variance minimization and compatibility with machine learning estimators. Theoretical analysis confirms that the estimator’s first-order asymptotic properties remain unchanged. Empirical re-analyses demonstrate average standard error reductions of 15–30%. The approach is plug-in, computationally lightweight, and broadly applicable across diverse RD settings.

8 citations2 influentialRead paper

MEmilio -- A high performance Modular EpideMIcs simuLatIOn software for multi-scale and comparative simulations of infectious disease dynamics

Feb 11, 2026

This work proposes a unified, modular, high-performance simulation framework to address the fragmentation in current infectious disease modeling ecosystems, which hinders cross-model comparison and deployment across model types, spatial scales, and computational platforms. For the first time, the framework integrates compartmental models, meta-population models, and agent-based models within a single architecture, enabling multi-scale and comparable epidemic dynamics simulations. By standardizing representations of spatial, demographic, and mobility data, coupling a high-performance C++ core with a Python interface, and incorporating uncertainty quantification and parameter inference tools, the framework supports seamless deployment—from laptops to high-performance computing environments—significantly lowering barriers to reuse and accelerating the development of simulation-driven epidemic response capabilities.

2 citationsRead paper

The Impact of VR and 2D Interfaces on Human Feedback in Preference-Based Robot Learning

Mar 11, 2025arXiv.org

This study investigates how virtual reality (VR) versus traditional 2D interfaces affect the quality of human feedback and policy alignment in preference-based robot learning (PbRL). To this end, we construct the first cross-modal (VR/2D) human navigation preference dataset—comprising 2,325 pairwise comparisons—and integrate preference modeling, a custom VR experimental platform, and robot policy training to conduct systematic human-in-the-loop evaluation and statistical analysis. Results show that VR enhances spatial situational awareness but significantly reduces preference consistency; conversely, 2D interfaces yield more stable feedback yet impair semantic understanding of the environment. We are the first to empirically characterize the trade-offs among interface immersion, preference reliability, user consistency, and downstream policy performance. Furthermore, we publicly release the dual-modality dataset to serve as both an empirical foundation and benchmark resource for human–robot interface design in PbRL.

2 citationsRead paper

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