Machine learning in an expectation-maximisation framework for nowcasting

• URL: http://arxiv.org/abs/2512.07335v1
• Date: Mon, 08 Dec 2025 09:22:22 GMT
• Title: Machine learning in an expectation-maximisation framework for nowcasting
• Authors: Paul Wilsens, Katrien Antonio, Gerda Claeskens,
• Abstract summary: Leveraging observable information to learn the complete information is called nowcasting.<n>In practice, incomplete information is often a consequence of reporting or observation delays.<n>We propose an expectation-maximisation framework that uses machine learning techniques to model both the occurrence as well as the reporting process of events.
• Score: 0.9558392439655012
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Decision making often occurs in the presence of incomplete information, leading to the under- or overestimation of risk. Leveraging the observable information to learn the complete information is called nowcasting. In practice, incomplete information is often a consequence of reporting or observation delays. In this paper, we propose an expectation-maximisation (EM) framework for nowcasting that uses machine learning techniques to model both the occurrence as well as the reporting process of events. We allow for the inclusion of covariate information specific to the occurrence and reporting periods as well as characteristics related to the entity for which events occurred. We demonstrate how the maximisation step and the information flow between EM iterations can be tailored to leverage the predictive power of neural networks and (extreme) gradient boosting machines (XGBoost). With simulation experiments, we show that we can effectively model both the occurrence and reporting of events when dealing with high-dimensional covariate information. In the presence of non-linear effects, we show that our methodology outperforms existing EM-based nowcasting frameworks that use generalised linear models in the maximisation step. Finally, we apply the framework to the reporting of Argentinian Covid-19 cases, where the XGBoost-based approach again is most performant.

Related papers

• CIKT: A Collaborative and Iterative Knowledge Tracing Framework with Large Language Models [14.273311275013057]
  Knowledge Tracing aims to model a student's learning state over time and predict their future performance.<n>Traditional KT methods often face challenges in explainability, scalability, and effective modeling of complex knowledge dependencies.<n>We propose Collaborative Iterative Knowledge Tracing (CIKT), a framework that harnesses Large Language Models to enhance both prediction accuracy and explainability.
  arXiv  Detail & Related papers  (2025-05-23T10:16:16Z)
• Continuous Visual Autoregressive Generation via Score Maximization [69.67438563485887]
  We introduce a Continuous VAR framework that enables direct visual autoregressive generation without vector quantization.<n>Within this framework, all we need is to select a strictly proper score and set it as the training objective to optimize.
  arXiv  Detail & Related papers  (2025-05-12T17:58:14Z)
• AdvKT: An Adversarial Multi-Step Training Framework for Knowledge Tracing [64.79967583649407]
  Knowledge Tracing (KT) monitors students' knowledge states and simulates their responses to question sequences.<n>Existing KT models typically follow a single-step training paradigm, which leads to significant error accumulation.<n>We propose a novel Adversarial Multi-Step Training Framework for Knowledge Tracing (AdvKT) which focuses on the multi-step KT task.
  arXiv  Detail & Related papers  (2025-04-07T03:31:57Z)
• In-Context Linear Regression Demystified: Training Dynamics and Mechanistic Interpretability of Multi-Head Softmax Attention [52.159541540613915]
  We study how multi-head softmax attention models are trained to perform in-context learning on linear data.<n>Our results reveal that in-context learning ability emerges from the trained transformer as an aggregated effect of its architecture and the underlying data distribution.
  arXiv  Detail & Related papers  (2025-03-17T02:00:49Z)
• Optimal Classification under Performative Distribution Shift [13.508249764979075]
  We propose a novel view in which performative effects are modelled as push-forward measures. We prove the convexity of the performative risk under a new set of assumptions. We also establish a connection with adversarially robust classification by reformulating the minimization of the performative risk as a min-max variational problem.
  arXiv  Detail & Related papers  (2024-11-04T12:20:13Z)
• Low-rank finetuning for LLMs: A fairness perspective [54.13240282850982]
  Low-rank approximation techniques have become the de facto standard for fine-tuning Large Language Models. This paper investigates the effectiveness of these methods in capturing the shift of fine-tuning datasets from the initial pre-trained data distribution. We show that low-rank fine-tuning inadvertently preserves undesirable biases and toxic behaviors.
  arXiv  Detail & Related papers  (2024-05-28T20:43:53Z)
• Enhancing Dynamical System Modeling through Interpretable Machine Learning Augmentations: A Case Study in Cathodic Electrophoretic Deposition [0.8796261172196743]
  We introduce a comprehensive data-driven framework aimed at enhancing the modeling of physical systems. As a demonstrative application, we pursue the modeling of cathodic electrophoretic deposition (EPD), commonly known as e-coating.
  arXiv  Detail & Related papers  (2024-01-16T14:58:21Z)
• Finding emergence in data by maximizing effective information [2.1714094454496013]
  It's crucial to develop a framework to identify emergent phenomena and capture emergent dynamics at the macro-level using available data. Inspired by the theory of causal emergence (CE), this paper introduces a machine learning framework to learn macro-dynamics in an emergent latent space. Experimental results on simulated and real data demonstrate the effectiveness of the proposed framework.
  arXiv  Detail & Related papers  (2023-08-19T09:12:47Z)
• Meta-learning using privileged information for dynamics [66.32254395574994]
  We extend the Neural ODE Process model to use additional information within the Learning Using Privileged Information setting. We validate our extension with experiments showing improved accuracy and calibration on simulated dynamics tasks.
  arXiv  Detail & Related papers  (2021-04-29T12:18:02Z)
• Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation [51.29486247405601]
  We design a framework to generate counterfactuals for raw data instances with the proposed Attribute-Informed Perturbation (AIP) By utilizing generative models conditioned with different attributes, counterfactuals with desired labels can be obtained effectively and efficiently. Experimental results on real-world texts and images demonstrate the effectiveness, sample quality as well as efficiency of our designed framework.
  arXiv  Detail & Related papers  (2021-01-18T08:37:13Z)
• Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations [13.680949377743392]
  We address the problem of learning of a spreading model such that the predictions generated from this model are accurate. We introduce a computationally efficient algorithm, based on a scalable dynamic message-passing approach. We show that tractable inference from the learned model generates a better prediction of marginal probabilities compared to the original model.
  arXiv  Detail & Related papers  (2020-07-13T17:58:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.