arXiv:2603.26066v1 Announce Type: new
Abstract: We study a class of adversarial bandit optimization problems in which the loss functions may be non-convex and non-smooth. In each round, the learner observes a loss that consists of an underlying linear…
arXiv:2504.05654v5 Announce Type: replace-cross
Abstract: By analogy to the terminology of curved exponential families in statistics, we define curved Bregman divergences as Bregman divergences restricted to non-affine parameter subspaces and sub-dime…
arXiv:2603.25538v2 Announce Type: replace
Abstract: Automated incident management is critical for microservice reliability. While recent unified frameworks leverage multimodal data for joint optimization, they unrealistically assume perfect data compl…
arXiv:2603.26030v1 Announce Type: new
Abstract: In practical structural design and solid mechanics simulations, material properties inherently exhibit random variations within bounded intervals. However, evaluating mechanical responses under continuou…
arXiv:2603.26024v1 Announce Type: new
Abstract: Identification of causal directionality in bivariate numerical data is a fundamental research problem with important practical implications. This paper presents two alternative methods to identify direct…
arXiv:2602.20396v3 Announce Type: replace
Abstract: Explainable artificial intelligence promises to yield insights into relevant features, thereby enabling humans to examine and scrutinize machine learning models or even facilitating scientific discov…
arXiv:2603.26023v1 Announce Type: new
Abstract: Digital twins of complex physical systems are expected to infer unobserved states from sparse measurements and predict their evolution in time, yet these two functions are typically treated as separate t…
arXiv:2603.26017v1 Announce Type: new
Abstract: Time series forecasting is critical across finance, healthcare, and cloud computing, yet progress is constrained by a fundamental bottleneck: the scarcity of large-scale, high-quality benchmarks. To addr…
arXiv:2511.10938v2 Announce Type: replace
Abstract: Motivated by the challenges of edge inference, we study a variant of the cascade bandit model in which each arm corresponds to an inference model with an associated accuracy and error probability. We…
arXiv:2603.25976v1 Announce Type: new
Abstract: Second-order methods promise improved stability and faster convergence, yet they remain underused due to implementation overhead, tuning brittleness, and the lack of composable APIs. We introduce Somax, …