arXiv:2509.15810v2 Announce Type: replace Abstract: To relieve intensive human-expertise required to design optimization algorithms, recent Meta-Black-Box Optimization (MetaBBO) researches leverage generalization strength of meta-learning to train neural network-based algorithm design policies over a predefined training problem set, which automates the…
arXiv:2511.04807v2 Announce Type: replace Abstract: Given a “data manifold” $Msubset mathbb{R}^n$ and “latent space” $mathbb{R}^ell$, an autoencoder is a pair of continuous maps consisting of an “encoder” $Ecolon mathbb{R}^nto mathbb{R}^ell$ and “decoder” $Dcolon mathbb{R}^ellto mathbb{R}^n$ such that the “round trip”…
arXiv:2410.12982v2 Announce Type: replace Abstract: While transformers have been at the core of most recent advancements in sequence generative models, their computational cost remains quadratic in sequence length. Several subquadratic architectures have been proposed to address this computational issue. Some…
arXiv:2505.22422v2 Announce Type: replace Abstract: The construction of confidence intervals for the mean of a bounded random variable is a classical problem in statistics with numerous applications in machine learning and virtually all scientific fields. In particular, obtaining the tightest…
arXiv:2511.07470v1 Announce Type: new Abstract: This work demonstrates “slimmable Neural Amp Models”, whose size and computational cost can be changed without additional training and with negligible computational overhead, enabling musicians to easily trade off between the accuracy and compute of…
arXiv:2511.08500v1 Announce Type: cross Abstract: Large language models (LLMs) adapted to financial domains often suffer from catastrophic forgetting of general reasoning capabilities essential for customer interactions and complex financial analysis. We introduce Selective Parameter Evaluation and Restoration via Model Merging…
arXiv:2511.07484v1 Announce Type: new Abstract: This study presents a novel framework for counterfactual user behavior forecasting that combines structural causal models with transformer-based generative artificial intelligence. To model fictitious situations, the method creates causal graphs that map the connections between…
arXiv:2511.07485v1 Announce Type: new Abstract: Machine learning systems exhibit diverse failure modes: unfairness toward protected groups, brittleness to spurious correlations, poor performance on minority sub-populations, which are typically studied in isolation by distinct research communities. We propose a unifying theoretical…
arXiv:2501.05530v2 Announce Type: replace-cross Abstract: This paper introduces two novel, outlyingness scores (OSs) based on Cluster Catch Digraphs (CCDs): Outbound Outlyingness Score (OOS) and Inbound Outlyingness Score (IOS). These scores enhance the interpretability of outlier detection results. Both OSs employ…
arXiv:2509.15810v2 Announce Type: replace Abstract: To relieve intensive human-expertise required to design optimization algorithms, recent Meta-Black-Box Optimization (MetaBBO) researches leverage generalization strength of meta-learning to train neural network-based algorithm design policies over a predefined training problem set, which automates the…
