arXiv:2601.12178v1 Announce Type: cross Abstract: We propose a federated learning framework for the calibration of parametric insurance indices under heterogeneous renewable energy production losses. Producers locally model their losses using Tweedie generalized linear models and private data, while a common index is learned through federated optimization without sharing raw observations. The approach accommodates heterogeneity in variance and link functions and directly minimizes a global deviance objective in a distributed setting. We implement and compare FedAvg, FedProx and FedOpt, […]

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AGI is here! When exactly it arrived, we’ll never know; whether it was one company’s Pro or another company’s Pro Max (Eddie Bauer Edition) that tip-toed first across the line … you may debate. But generality has been achieved, & now we can proceed to new questions. […] The key word in Artificial General Intelligence is General. That’s the word that makes this AI unlike every other AI: because every other AI was trained for a particular purpose. Consider landmark models across the decades: […]

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arXiv:2601.18948v1 Announce Type: new Abstract: Split-Federated (SplitFed) learning is an extension of federated learning that places minimal requirements on the clients computing infrastructure, since only a small portion of the overall model is deployed on the clients hardware. In SplitFed learning, feature values, gradient updates, and model updates are transferred across communication channels. In this paper, we study the effects of noise in the communication channels on the learning process and the quality of the final model. We […]

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In one of his earliest writings on the topic, Teilhard de Chardin explores humanity’s unique place in evolution. He argues that humans represent an entirely new phase of life on Earth—the , or sphere of conscious thought. While physically similar to other primates, humans are revolutionary in their ability to use tools, form global connections, and reflect on their own existence. This self-awareness comes with both great power and great risk, as humans can choose to either advance […]

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arXiv:2309.12450v3 Announce Type: replace Abstract: We study policy evaluation of offline contextual bandits subject to unobserved confounders. Sensitivity analysis methods are commonly used to estimate the policy value under the worst-case confounding over a given uncertainty set. However, existing work often resorts to some coarse relaxation of the uncertainty set for the sake of tractability, leading to overly conservative estimation of the policy value. In this paper, we propose a general estimator that provides a sharp lower bound […]

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In this quiz, you’ll test your understanding of The Zen of Python. By working through this quiz, you’ll revisit core aphorisms and learn how they guide readable, maintainable, and Pythonic code. The questions explore practical tradeoffs like breaking dense expressions into smaller parts, favoring clarity over cleverness, and making code behavior explicit. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> […]

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Automated machine learning (AutoML) frameworks can lower technical barriers for predictive and prognostic model development in radiomics by enabling researchers without programming expertise to build models. However, their effectiveness in addressing radiomics-specific challenges remains unclear. This study evaluates the performance, efficiency, and accessibility of general-purpose and radiomics-specific AutoML frameworks on diverse radiomics classification tasks, thereby highlighting development needs for radiomics. Ten public/private radiomics datasets with varied imaging modalities (CT/MRI), sizes, anatomies and endpoints were used. Six general-purpose and […]

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arXiv:2601.09039v1 Announce Type: new Abstract: Large language model (LLM) tokenizers act as structured compressors: by mapping text to discrete token sequences, they determine token count (and thus compute and context usage) and the statistical structure seen by downstream models. Despite their central role in LLM pipelines, the link between tokenization, compression efficiency and induced structure is not well understood. We empirically demonstrate that tokenizer training scale redistributes entropy: as training data grows, the token stream becomes more diverse […]

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arXiv:2601.08896v1 Announce Type: new Abstract: This study develops a robust machine learning framework for one-step-ahead forecasting of daily log-returns in the Nepal Stock Exchange (NEPSE) Index using the XGBoost regressor. A comprehensive feature set is engineered, including lagged log-returns (up to 30 days) and established technical indicators such as short- and medium-term rolling volatility measures and the 14-period Relative Strength Index. Hyperparameter optimization is performed using Optuna with time-series cross-validation on the initial training segment. Out-of-sample performance is […]

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Systematic literature reviews (SLRs) are fundamental to evidence-based research, but manual screening is an increasing bottleneck as scientific output grows. Screening features low prevalence of relevant studies and scarce, costly expert decisions. Traditional active learning (AL) systems help, yet typically rely on fixed query strategies for selecting the next unlabeled documents. These static strategies do not adapt over time and ignore the relational structure of scientific literature networks. This thesis introduces AutoDiscover, a framework that reframes AL as […]

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