In this paper, we present two novel training paradigms on how to train generative models on statistical data accessible in real-world experiments, to generate fine-grained particle information, which do not exist in measurement.

In this paper we introduce a model of hadronization based on invertible neural networks and a new training method for normalizing flows that improves the agreement between simulated and experimental data. We also demonstrate how to analyse statistical and modeling uncertainties in the generated distributions.

In this paper, we present, the first steps in developing a new class of hadronization models utilizing machine learning. We introduce the pipeline using conditional sliced Wasserstein autoencoder, and successfully reproduce a simplified version of the current state-of-the-art simulator

In this paper, we use normalizing flows to overcome previous limitations of ML-based hadronization models, which were restricted to the emission of pions only.

In this paper, we ‘hack’ generative models by injecting a small-scale trainable module between the intermediate layers of the model. This approach pushes their outputs away from the original training distribution towards a new objective.

The paper introduces a novel unsupervised text style transfer method using few-shot abstractive summarization. This approach aligns vector space embeddings of source and target texts, selects nearest neighbors based on semantic similarity, and reranks the summarization. This method significantly improves style transfer quality and achieves soa results in automatic evaluation metrics.

In this paper, a novel unbinned test statistic based on the Wasserstein distance is introduced to detect charge conjugation parity (CP) symmetry violation, demonstrated with CP asymmetric distributions in B0 and D0 decays. This statistic matches the sensitivity of traditional tests while providing more detailed insights into localized CP asymmetry.

The paper introduces a Monte Carlo-veto algorithm for efficient uncertainty estimation in collider-event simulations, applicable to the Lund string-fragmentation model. This approach enables analysis of different input parameters using a single simulated event set, streamlining uncertainty assessments in physics measurements.

In this paper, we calculate the leading and next-to-leading-logarithmic electroweak corrections for the charm-top-quark contribution to the CP violation parameter epsilon_K, which leads to the reduction of the theory prediction. A more accurate prediction in epsilon_K is crucial in particle physics as it measures CP violation in kaon decay, tests the Standard Model, and probes new physics.