arXiv:2409.03505v4 Announce Type: replace-cross Abstract: In the Newsvendor problem, the goal is to guess the number that will be drawn from some distribution, with asymmetric consequences for guessing too high vs. too low. In the data-driven version, the distribution is…
arXiv:2508.02581v2 Announce Type: replace-cross Abstract: Manipulating matter with a scanning tunneling microscope (STM) enables creation of atomically defined artificial structures that host designer quantum states. However, the time-consuming nature of the manipulation process, coupled with the sensitivity of the STM…
arXiv:2509.22462v2 Announce Type: replace Abstract: We propose a reduced-space formulation for optimizing over trained neural networks where the network’s outputs and derivatives are evaluated on a GPU. To do this, we treat the neural network as a “gray box” where…
arXiv:2512.00831v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) are Large Language Models (LLMs) explicitly trained to generate long-form Chain-of-Thoughts (CoTs), achieving impressive success on challenging tasks like math and programming. However, their underlying reasoning “algorithms” remain poorly understood. To…
arXiv:2011.13132v3 Announce Type: replace Abstract: We propose a multivariate generative model to capture the complex dependence structure often encountered in business and financial data. Our model features heterogeneous and asymmetric tail dependence between all pairs of individual dimensions while also…
arXiv:2503.09494v3 Announce Type: replace Abstract: In the era of big data, large-scale, multi-source, multi-modality datasets are increasingly ubiquitous, offering unprecedented opportunities for predictive modeling and scientific discovery. However, these datasets often exhibit complex heterogeneity, such as covariates shift, posterior drift,…
arXiv:2512.07855v1 Announce Type: new Abstract: Attention-based Transformers have revolutionized natural language processing (NLP) and shown strong performance in computer vision (CV) tasks. However, as the input sequence varies, the computational bottlenecks in Transformer models exhibit dynamic behavior across stages, which…
arXiv:2512.07856v1 Announce Type: new Abstract: Accurate disease detection is of paramount importance for effective medical treatment and patient care. However, the process of disease detection is often associated with extensive medical testing and considerable costs, making it impractical to perform…
arXiv:2512.07853v1 Announce Type: new Abstract: As deep learning models in agentic AI systems grow in scale and complexity, GPU memory requirements increase and often exceed the available GPU memory capacity, so that out-of-memory (OoM) errors occur. It is well known…
arXiv:2512.07854v1 Announce Type: new Abstract: Traffic forecasting task is significant to modern urban management. Recently, there is growing attention on large-scale forecasting, as it better reflects the complexity of real-world traffic networks. However, existing models often exhibit quadratic computational complexity,…
