arXiv:2510.11839v1 Announce Type: new Abstract: Time series are ubiquitous in many applications that involve forecasting, classification and causal inference tasks, such as healthcare, finance, audio signal processing and climate sciences. Still, large, high-quality time series datasets remain scarce. Synthetic generation…
arXiv:2510.11832v1 Announce Type: new Abstract: A critical aspect of analyzing and improving modern machine learning systems lies in understanding how individual training examples influence a model’s predictive behavior. Estimating this influence enables critical applications, including data selection and model debugging;…
arXiv:2510.11829v1 Announce Type: new Abstract: Generative AI can be framed as the problem of learning a model that maps simple reference measures into complex data distributions, and it has recently found a strong connection to the classical theory of the…
arXiv:2510.11827v1 Announce Type: new Abstract: Node-level anomaly detection (NAD) is challenging due to diverse structural patterns and feature distributions. As such, NAD is a critical task with several applications which range from fraud detection, cybersecurity, to recommendation systems. We introduce…
arXiv:2505.15696v2 Announce Type: replace-cross Abstract: The [CLS] token in BERT is commonly used as a fixed-length representation for classification tasks, yet prior work has shown that both other tokens and intermediate layers encode valuable contextual information. In this work, we…
arXiv:2510.11856v1 Announce Type: new Abstract: Predictive Process Monitoring (PPM) is a key task in Process Mining that aims to predict future behavior, outcomes, or performance indicators. Accurate prediction of the latter is critical for proactive decision-making. Given that processes are…
arXiv:2510.11868v1 Announce Type: new Abstract: Knowledge graphs represent information as structured triples and serve as the backbone for a wide range of applications, including question answering, link prediction, and recommendation systems. A prominent line of research for exploring knowledge graphs…
arXiv:2510.12687v1 Announce Type: cross Abstract: Open-Set Domain Generalization (OSDG) aims to enable deep learning models to recognize unseen categories in new domains, which is crucial for real-world applications. Label noise hinders open-set domain generalization by corrupting source-domain knowledge, making it…
arXiv:2510.11877v1 Announce Type: new Abstract: The Transformer, a highly expressive architecture for sequence modeling, has recently been adapted to solve sequential decision-making, most notably through the Decision Transformer (DT), which learns policies by conditioning on desired returns. Yet, the adversarial…
arXiv:2406.02613v3 Announce Type: replace Abstract: Training LLMs relies on distributed implementations using multiple GPUs to compute gradients in parallel with sharded optimizers. However, synchronizing gradients in data parallel setups introduces communication overhead that grows with the number of workers, limiting…
