arXiv subjects

Thomas Wedenig

Publications and source records attributed to Thomas Wedenig.

4 recordsLinked to original sources

arXiv2026

RaMMOBO: Random Max-Margin Multi-Objective Bayesian Optimization

Multi-objective Bayesian optimization (MOBO) maximizes $M$ expensive black-box objectives with the goal of approximating the Pareto set, whose objective vectors cannot be improved in one component without sacrificing another. A common quality measure is hypervolume, the volume in objective space dominated by these vectors relative to a reference point. Expected hypervolume improvement (EHVI) directly targets this metric and is one-step Bayes-optimal, but its standard computation scales exponentially with $M$, making it prohibitively expensive for many-objective problems. We introduce random max-margin multi-objective Bayesian optimization (RaMMOBO), whose acquisition function lower-bounds EHVI while requiring only linear cost in $M$ to evaluate. RaMMOBO measures the volume of a max-margin box contained in a candidate's hypervolume-improvement region and averages over random reweightings of the objective space, yielding a utility that is strictly Pareto-monotone on hypervolume-improving outcomes. It naturally handles noisy observations and different probabilistic surrogates and supports efficient gradient-based optimization. Across synthetic and real-world benchmarks, RaMMOBO matches or outperforms state-of-the-art methods, with largest gains at higher $M$, with acquisition optimization up to two orders of magnitude faster than qLogNEHVI. Our implementation is available at https://anonymous.4open.science/r/rammobo.

cs.LG↗

arXiv2026

Acquisition Function-Guided Generation for Bayesian Optimization over Discrete Spaces

Bayesian optimization (BO) over discrete spaces is challenging due to missing gradients and the sparsity of valid solutions. Latent-space BO sidesteps both issues by fitting a surrogate in the latent space of a generative model, which however substantially complicates the direct incorporation of domain knowledge that is naturally expressed in the original discrete space. In this work, we instead combine a pretrained generative model with a surrogate defined directly in data space, where inductive biases such as domain-specific kernels can be readily incorporated. Rather than maximizing the acquisition function over the discrete design space, our method samples candidates from the generative model under a distribution tilted by the acquisition function. To recover useful gradients for sampling, we embed this tilted distribution into a diffusion bridge that yields a smooth, posterior-averaged acquisition function. This leads to \emph{acquisition function guided generation} (AFGG), which we instantiate with a categorical diffusion model and an autoregressive normalizing flow. Across a broad suite of molecular benchmarks, the diffusion model variant performs on par with leading BO methods, while the flow model variant outperforms state-of-the-art methods in most cases and remains highly competitive otherwise. Our implementation is available at https://anonymous.4open.science/r/afgg.

cs.LG↗

arXiv2025

Exact Soft Analytical Side-Channel Attacks using Tractable Circuits

Detecting weaknesses in cryptographic algorithms is of utmost importance for designing secure information systems. The state-of-the-art soft analytical side-channel attack (SASCA) uses physical leakage information to make probabilistic predictions about intermediate computations and combines these "guesses" with the known algorithmic logic to compute the posterior distribution over the key. This attack is commonly performed via loopy belief propagation, which, however, lacks guarantees in terms of convergence and inference quality. In this paper, we develop a fast and exact inference method for SASCA, denoted as ExSASCA, by leveraging knowledge compilation and tractable probabilistic circuits. When attacking the Advanced Encryption Standard (AES), the most widely used encryption algorithm to date, ExSASCA outperforms SASCA by more than 31% top-1 success rate absolute. By leveraging sparse belief messages, this performance is achieved with little more computational cost than SASCA, and about 3 orders of magnitude less than exact inference via exhaustive enumeration. Even with dense belief messages, ExSASCA still uses 6 times less computations than exhaustive inference.

cs.LG↗

arXiv2023

Forecasting Electric Vehicle Charging Station Occupancy: Smarter Mobility Data Challenge

The transport sector is a major contributor to greenhouse gas emissions in Europe. Shifting to electric vehicles (EVs) powered by a low-carbon energy mix would reduce carbon emissions. However, to support the development of electric mobility, a better understanding of EV charging behaviours and more accurate forecasting models are needed. To fill that gap, the Smarter Mobility Data Challenge has focused on the development of forecasting models to predict EV charging station occupancy. This challenge involved analysing a dataset of 91 charging stations across four geographical areas over seven months in 2020-2021. The forecasts were evaluated at three levels of aggregation (individual stations, areas and global) to capture the inherent hierarchical structure of the data. The results highlight the potential of hierarchical forecasting approaches to accurately predict EV charging station occupancy, providing valuable insights for energy providers and EV users alike. This open dataset addresses many real-world challenges associated with time series, such as missing values, non-stationarity and spatio-temporal correlations. Access to the dataset, code and benchmarks are available at https://gitlab.com/smarter-mobility-data-challenge/tutorials to foster future research.

cs.DB↗