![The optimal transport plan reveals a concentration of probability mass along a diagonal-consistent with the martingale constraint [latex]\mathbb{E}[X\_{1}|X\_{0}]=X\_{0}[/latex]-and highlights a high-probability transition path clustered near the point [latex](5500, 6500)[/latex].](https://arxiv.org/html/2601.05290v1/x5.png)
This review introduces a novel neural network framework for efficiently solving multi-period martingale optimal transport problems, accelerating the pricing and risk management of complex financial derivatives.
A new approach leverages issue tracking data and advanced language models to automatically generate insightful explanations of software behavior, offering a powerful alternative to traditional documentation.
A new approach leverages the power of graph-based machine learning to identify malicious activity as it spreads within enterprise networks.
A new method improves the accuracy and efficiency of uncovering causal relationships by focusing on robustly learning the underlying structure of complex systems.
New research demonstrates how incorporating the meaning of categorical data improves the performance of foundation models in understanding financial transactions.
This research introduces a novel deep learning framework designed to enhance the resilience and performance of systematic macro trading strategies in dynamic economic environments.
New research offers a robust toolkit for overcoming measurement challenges and building more accurate demand estimations using unstructured data.
New research reveals that foreign investor order flow becomes a much stronger predictor of market movements during times of crisis, demanding more dynamic modeling approaches.
New research demonstrates that AI agents can be trained to not only detect credit card fraud, but also provide interpretable reasoning behind their decisions.
As generative AI rapidly evolves, a novel continual learning framework is needed to reliably identify synthetic images and combat the spread of misinformation.