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Technische Universität Dortmund

Academic institutioneurope · de
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Research library328linked papers
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Selected work

Representative Papers

Work-Efficient Query Evaluation with PRAMs

Jan 19, 2023International Conference on Database Theory

This paper addresses two major bottlenecks—excessive work and scattered output memory—in parallel evaluation of relational queries on the CRCW PRAM model. We propose the first constant-time, weakly work-efficient (O(T^{1+ε})) parallel evaluation framework. Methodologically, we integrate the Goldberg–Zwick approximate prefix-sum algorithm with compactification techniques, leveraging practical assumptions such as sorting or numerical constraints to achieve operator-level parallelization for acyclic queries, semijoin algebra, and worst-case-optimal joins. Our contributions are threefold: (i) the first constant-time, weakly work-efficient parallel evaluation for these three fundamental query classes; (ii) work complexity asymptotically approaching that of the optimal sequential algorithms; and (iii) substantial improvements over naive parallel approaches, coupled with highly compact output memory layout.

3 citationsRead paper

Interpolation in Knowledge Representation

Dec 09, 2025

Craig and uniform interpolation lack theoretical guarantees and are computationally intractable in description logics and logic programming. Method: This paper systematically characterizes the existence boundaries of interpolation for prominent formalisms—including ALC, EL, and Answer Set Programming—by integrating model-theoretic and proof-theoretic criteria; it proposes a theoretically complete, polynomial-time interpolant construction framework. Contribution/Results: We establish the first interpolation property hierarchy across multiple sublogics, develop an extensible interpolant generator, and empirically validate its efficiency and practicality on standard ontologies and rule sets. The approach significantly advances key knowledge engineering tasks, including knowledge forgetting, modular reuse, and explainable reasoning.

2 citationsRead paper

Amortized Bayesian Workflow

Sep 06, 2024

Bayesian inference often faces a trade-off between computational efficiency and posterior accuracy, especially across multiple datasets. This paper proposes an adaptive hybrid inference workflow that—uniquely—integrates amortized variational inference (AVI) with Markov chain Monte Carlo (MCMC) in a dynamically coordinated manner. Leveraging principled posterior diagnostics, it constructs a Pareto frontier to enable automatic, optimal switching between AVI and MCMC. Computational reuse and scheduling optimization further boost inference throughput. The method unifies generative neural network modeling, MCMC refinement, and verifiable diagnostic mechanisms. Evaluated on tens of thousands of real and synthetic datasets, it achieves a 3.2× average speedup over standalone AVI or MCMC baselines, while preserving posterior fidelity—reducing KL divergence by 47% and increasing effective sample size (ESS) by 2.8×. This work delivers a scalable, efficient, and trustworthy solution for large-scale Bayesian inference.

2 citationsRead paper

From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection

Feb 03, 2026

This work addresses the challenge of deploying anomaly detection methods in scenarios where labeled anomalous samples are unavailable. We propose OUTFORMER, the first plug-and-play zero-shot framework for tabular anomaly detection that requires neither fine-tuning nor hyperparameter tuning. Its core innovations lie in a foundation model architecture enhanced by synthetic data pretraining, in-context learning, a synthetic prior mixing mechanism, and a self-evolving curriculum training strategy. Evaluated on AdBench and two newly constructed large-scale benchmarks encompassing over 1,500 datasets, OUTFORMER substantially outperforms existing methods while maintaining high inference efficiency.

1 citationsRead paper

Electricity Price Forecasting: Bridging Linear Models, Neural Networks and Online Learning

Jan 06, 2026arXiv.org

This study addresses the challenge of balancing prediction accuracy and computational efficiency in highly volatile electricity markets, where linear models fail to capture nonlinear dynamics and complex nonlinear models incur prohibitive computational costs. To overcome this limitation, the authors propose a novel multivariate architecture that deeply integrates linear and nonlinear feedforward neural networks, synergistically leveraging their respective strengths. The framework incorporates online learning and a forecast combination mechanism to effectively model the dynamic relationships between electricity prices and multiple exogenous factors—including wind and solar generation, load demand, fuel prices, and carbon prices. Extensive experiments on six years of data from six major European electricity markets demonstrate that the proposed method reduces RMSE by 12–13% and MAE by 15–18% compared to state-of-the-art models, while simultaneously achieving significantly lower computational overhead.

1 citationsRead paper
Recent publications

Latest Papers

Approximating Bayesian leave-one-group-out cross-validation

Sep 04, 2026

When data are grouped, hierarchical or multilevel models are commonly used to account for group-level variation with group-specific parameters. Leave-one-group-out cross-validation (LOGO-CV) is a suitable tool for evaluating predictive performance for new groups, providing an estimator of the expected log predictive density (elpd). Brute-force LOGO-CV requires one model refit per held-out group, often using computationally expensive inference algorithms such as MCMC. This is costly, particularly for large numbers of groups or complex model structures. Commonly used importance sampling approximations, intended to reduce this cost, tend to fail because the group-specific parameters of the held-out group must be integrated out. We identify two key challenges in LOGO-CV elpd estimation: approximating the LOGO posterior and computing the grouped marginal likelihood. We compare 11 strategies, including 5 newly proposed, to address them. Among others, we combine Pareto-smoothed importance sampling or adaptive importance sampling with integration techniques such as Laplace approximation, adaptive Gauss-Hermite quadrature, and bridge sampling. We evaluate these strategies in both simulation experiments and real-world case studies, which show that marginalising over the group-specific parameters substantially improves the reliability of the importance sampling approaches.

0 citationsRead paper