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Ruhr-Universität Bochum

Academic institutioneurope · de
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Research library369linked papers
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Selected work

Representative Papers

Realized Range-Based Estimation of Integrated Variance

Jun 04, 2006

This study addresses the downward bias and inefficiency of conventional realized variance estimators under discrete observations by proposing a novel high-low range–based estimator for the quadratic variation of continuous semimartingales. The method replaces squared returns with normalized squared price ranges, yielding a consistent and asymptotically mixed normal estimator that effectively corrects bias induced by non-trading periods. Leveraging probabilistic limit theory, continuous semimartingale modeling, and high-low price statistics, the approach achieves an 80% reduction in theoretical variance compared to traditional estimators. Empirical analysis using TAQ data demonstrates that the proposed estimator substantially outperforms existing benchmarks in terms of estimation accuracy, efficiency, and robustness.

251 citations42 influentialRead paper

Task Adaptation from Skills: Information Geometry, Disentanglement, and New Objectives for Unsupervised Reinforcement Learning

Jun 12, 2025International Conference on Learning Representations

Unsupervised reinforcement learning (URL) aims to acquire transferable skills for unknown downstream tasks, yet existing mutual information-based skill learning (MISL) lacks theoretical characterization of skill transferability. We identify that skill diversity and separability are essential for efficient downstream policy initialization—properties not guaranteed by MISL. To address this, we propose two novel objectives—WSEP and PWSEP—grounded in Wasserstein geometry, along with a decoupling-aware metric, LSEPIN. Crucially, we establish the first theoretical link between Wasserstein distance and downstream adaptation cost, rigorously proving that our framework ensures complete discovery of optimal initial policies. Experiments demonstrate significant improvements in zero-shot transfer performance across multiple benchmarks, consistently outperforming MISL. Our method yields more disentangled skill representations and superior policy pretraining, enabling more effective downstream adaptation.

7 citationsRead paper

Governance of Generative Artificial Intelligence for Companies

Feb 05, 2024arXiv.org

While generative AI (GenAI) systems—such as ChatGPT—are being rapidly deployed in enterprises, existing AI governance frameworks fail to address GenAI’s unique technical characteristics (e.g., hallucination, non-determinism, data leakage risks) and their business implications (e.g., process integration, accountability allocation), resulting in governance gaps. Method: Grounded in Nickerson’s classical governance framework, this study integrates technical and business perspectives through conceptual analysis, cross-disciplinary literature synthesis, and iterative modeling to develop the first GenAI-specific governance framework for enterprise contexts. Contribution/Results: The framework adopts a three-dimensional structure—*scope*, *governance objectives*, and *implementation mechanisms*—defining organizational governance boundaries, hierarchical objectives (compliance, security, efficacy), and actionable mechanisms (policies, tools, workflows). It bridges a critical theoretical gap in organizational GenAI governance, delivers the first feasible and systematic implementation guide for enterprises, identifies key operational deficits, and proposes a phased adoption roadmap.

6 citationsRead paper

Comparison of Generative Learning Methods for Turbulence Modeling

Nov 25, 2024arXiv.org

High-fidelity turbulent flow simulations—such as direct numerical simulation (DNS) and large-eddy simulation (LES)—remain computationally prohibitive for routine engineering applications. This work systematically compares three generative probabilistic models—variational autoencoders (VAEs), deep convolutional generative adversarial networks (DCGANs), and denoising diffusion probabilistic models (DDPMs)—for modeling two-dimensional Karman vortex streets, trained exclusively on LES data. Evaluation is conducted across three dimensions: statistical fidelity, spatial structure preservation, and multiscale dynamical consistency. Results demonstrate that DCGAN achieves the best overall performance in generation fidelity, inference speed, and sample efficiency—accurately reconstructing turbulent fields from limited LES data. DDPM attains higher accuracy but suffers from prohibitively slow inference; VAE trains rapidly yet yields significant structural distortions. This study establishes generative modeling as a novel, high-fidelity, low-cost surrogate paradigm for turbulence, providing a scalable, data-driven methodology for turbulent flow simulation.

5 citationsRead paper

Fake It Until You Break It: On the Adversarial Robustness of AI-generated Image Detectors

Oct 02, 2024arXiv.org

This study addresses the insufficient adversarial robustness of AI-generated image detectors in real-world settings, where they are vulnerable to black-box attacks and common social media degradations (e.g., JPEG compression, resizing, color distortion), enabling malicious misuse for disinformation and undermining democratic trust. We conduct the first systematic evaluation of mainstream detectors under combined black-box adversarial perturbations and realistic degradations, revealing that state-of-the-art models suffer over 40% accuracy degradation without model access. To mitigate this, we propose a lightweight CLIP-enhanced defense grounded in zero-shot detection and black-box transfer attack modeling—requiring no retraining or fine-tuning. Our method preserves original detection performance while reducing adversarial success rates by 76%, substantially restoring practical utility and robustness on real platforms. This work delivers a deployable, trustworthy solution for AI-generated content authentication.

3 citations1 influentialRead paper
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