In this paper we propose a new classification algorithm designed for application on complex networks motivated by algorithmic similarities between boosting learning and message passing. We consider a network classifier as a logistic regression where the variables define the nodes and the interaction effects define the edges. From this definition we represent the problem as a factor graph of local exponential loss functions. Using the factor graph representation it is possible to interpret the network classifier as an […]

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Mechanism design is pivotal to federated learning (FL) for maximizing social welfare by coordinating self-interested clients. Existing mechanisms, however, often overlook the network effects of client participation and the diverse model performance requirements (i.e., generalization error) across applications, leading to suboptimal incentives and social welfare, or even inapplicability in real deployments. To address this gap, we explore incentive mechanism design for FL with network effects and application-specific requirements of model performance. We develop a theoretical model to quantify […]

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This work presents a real-time default-detection model integrating streaming behavioral signals, including app usage dynamics, repayment timing, transaction irregularities, and short-term income proxies. The model is built on 2.7 million active loan accounts with second-level event streams. A streaming-enabled ensemble classifier (online gradient boosting + incremental random forest) is deployed with a sliding window of 7 days. The model predicts impending 30-day delinquency with an ROC-AUC of 0.89 and reduces detection delay by 9.2 days on average. Incorporating […]

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arXiv:2601.04274v1 Announce Type: new Abstract: This paper presents an Answer Set Programming (ASP)-based framework for medical appointment scheduling, aimed at improving efficiency, reducing administrative overhead, and enhancing patient-centered care. The framework personalizes scheduling for vulnerable populations by integrating Blueprint Personas. It ensures real-time availability updates, conflict-free assignments, and seamless interoperability with existing healthcare platforms by centralizing planning operations within an ASP logic model.

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arXiv:2506.22543v2 Announce Type: replace-cross Abstract: The Laser Interferometer Space Antenna (LISA) is expected to detect thousands of individually resolved gravitational wave sources, overlapping in time and frequency, on top of unresolved astrophysical and/or primordial backgrounds. Disentangling resolved sources from backgrounds and extracting their parameters in a computationally intensive “global fit” is normally regarded as a necessary step toward reconstructing the properties of the underlying astrophysical populations. Here, we show that it is in principle feasible to infer the […]

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Every technologist, at some point, is tempted by the idea of creating a digital version of themselves. Not a simple chatbot that answers questions, but a true digital persona — an AI that doesn’t just know what you know, but thinks how you think. This project, which I call Damian AI, was my attempt to build just that: a conversational agent grounded in my public work and architected to mirror my own systematic, logic-driven cognitive model. The journey was a […]

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arXiv:2601.07901v1 Announce Type: new Abstract: Decentralized online convex optimization (D-OCO), where multiple agents within a network collaboratively learn optimal decisions in real-time, arises naturally in applications such as federated learning, sensor networks, and multi-agent control. In this paper, we study D-OCO under unknown, time-and agent-varying feedback delays. While recent work has addressed this problem (Nguyen et al., 2024), existing algorithms assume prior knowledge of the total delay over agents and still suffer from suboptimal dependence on both the […]

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