arXiv:2404.15209v3 Announce Type: replace-cross Abstract: In data-driven decision-making in marketing, healthcare, and education, it is desirable to utilize a large amount of data from existing ventures to navigate high-dimensional feature spaces and address data scarcity in new ventures. We explore knowledge transfer in dynamic decision-making by concentrating on batch stationary environments and formally defining task discrepancies through the lens of Markov decision processes (MDPs). We propose a framework of Transferred Fitted $Q$-Iteration algorithm with general function approximation, enabling […]

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With the growing reliance on peer-to-peer (P2P) networks for digital transactions, traditional electronic payment systems require enhancements to ensure security, efficiency, and trust. This study introduces an innovative digital payment framework enabling currency-based exchanges between consumers and vendors within a peer-to-peer environment. The outlined approach is inspired by Millicent’s scrip methodology and leverages digital envelope encryption to bolster protection. Unlike conventional payment methods that heavily rely on financial institutions, the protocol minimizes their involvement, restricting their role to […]

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We present improved learning-augmented algorithms for finding an approximate minimum spanning tree (MST) for points in an arbitrary metric space. Our work follows a recent framework called metric forest completion (MFC), where the learned input is a forest that must be given additional edges to form a full spanning tree. Veldt et al. (2025) showed that optimally completing the forest takes $Ω(n^2)$ time, but designed a 2.62-approximation for MFC with subquadratic complexity. The same method is a $(2γ+ […]

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arXiv:2506.13652v2 Announce Type: replace-cross Abstract: Accurate weather forecasts are essential for supporting a wide range of activities and decision-making processes, as well as mitigating the impacts of adverse weather events. While traditional numerical weather prediction (NWP) remains the cornerstone of operational forecasting, machine learning is emerging as a powerful alternative for fast, flexible, and scalable predictions. We introduce PeakWeather, a high-quality dataset of surface weather observations collected every 10 minutes over more than 8 years from the ground […]

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Preference elicitation (PE) is an important component of interactive decision support systems that aim to make optimal recommendations to users by actively querying their preferences. In this paper, we outline five principles important for PE in real-world problems: (1) real-time, (2) multiattribute, (3) low cognitive load, (4) robust to noise, and (5) scalable. In light of these requirements, we introduce an approximate PE framework based on TrueSkill for performing efficient closed-form Bayesian updates and query selection for a […]

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arXiv:2601.05330v1 Announce Type: new Abstract: Predicting enzyme-substrate interactions has long been a fundamental problem in biochemistry and metabolic engineering. While existing methods could leverage databases of expert-curated enzyme-substrate pairs for models to learn from known pair interactions, the databases are often sparse, i.e., there are only limited and incomplete examples of such pairs, and also labor-intensive to maintain. This lack of sufficient training data significantly hinders the ability of traditional enzyme prediction models to generalize to unseen interactions. […]

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Neurochaos Learning (NL) has shown promise in recent times over traditional deep learning due to its two key features: ability to learn from small sized training samples, and low compute requirements. In prior work, NL has been implemented and extensively tested on separable and time series data, and demonstrated its superior performance on both classification and regression tasks. In this paper, we investigate the next step in NL, viz., applying NL to linked data, in particular, data that […]

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arXiv:2602.05970v1 Announce Type: cross Abstract: Neural scaling laws relate loss to model size in large language models (LLMs), yet depth and width may contribute to performance differently, requiring more detailed studies. Here, we quantify how depth affects loss via analysis of LLMs and toy residual networks. We find loss scales inversely proportional to depth in LLMs, probably due to functionally similar layers reducing error through ensemble averaging rather than compositional learning or discretizing smooth dynamics. This regime is […]

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arXiv:2602.15071v1 Announce Type: new Abstract: “Intangible Victory” is an audiovisual installation in the form of the intangible being of the Victory of Samothrace that uses interactive digital media. Specifically, through this installation, we redefine the visual symbolism of the ancient sculpture, paying attention to time as a wear factor (entropy) and the special importance of the void as an absence of the sculptural form. Emptiness completes the intangible essence of the sculpture in the field of symbolism as […]

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