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Tag: drift

  • Drift Estimation for Stochastic Differential Equations with Denoising Diffusion Models

    Drift Estimation for Stochastic Differential Equations with Denoising Diffusion Models arXiv:2602.17830v1 Announce Type: new Abstract: We study the estimation of time-homogeneous drift functions in multivariate stochastic differential equations with known diffusion coefficient, from multiple trajectories observed at high frequency over a fixed time horizon. We formulate drift estimation as a denoising problem conditional on previous…

  • Plug-In Classification of Drift Functions in Diffusion Processes Using Neural Networks

    Plug-In Classification of Drift Functions in Diffusion Processes Using Neural Networks arXiv:2602.02791v1 Announce Type: new Abstract: We study a supervised multiclass classification problem for diffusion processes, where each class is characterized by a distinct drift function and trajectories are observed at discrete times. Extending the one-dimensional multiclass framework of Denis et al. (2024) to multidimensional…

  • Drift Estimation for Diffusion Processes Using Neural Networks Based on Discretely Observed Independent Paths

    Drift Estimation for Diffusion Processes Using Neural Networks Based on Discretely Observed Independent Paths arXiv:2511.11161v1 Announce Type: new Abstract: This paper addresses the nonparametric estimation of the drift function over a compact domain for a time-homogeneous diffusion process, based on high-frequency discrete observations from $N$ independent trajectories. We propose a neural network-based estimator and derive…

  • An Adaptive Sampling Framework for Detecting Localized Concept Drift under Label Scarcity

    An Adaptive Sampling Framework for Detecting Localized Concept Drift under Label Scarcity arXiv:2511.02452v1 Announce Type: new Abstract: Concept drift and label scarcity are two critical challenges limiting the robustness of predictive models in dynamic industrial environments. Existing drift detection methods often assume global shifts and rely on dense supervision, making them ill-suited for regression tasks…

  • Implementing DRIFT Search with Neo4j and LlamaIndex

    Implementing DRIFT Search with Neo4j and LlamaIndex Combining global and local search to get the most accurate response The post Implementing DRIFT Search with Neo4j and LlamaIndex appeared first on Towards Data Science. Tomaz Bratanic Go to original source

  • Data Drift Is Not the Actual Problem: Your Monitoring Strategy Is

    Data Drift Is Not the Actual Problem: Your Monitoring Strategy Is Monitoring is easy; what to monitor is not. In the field of machine learning, data drift is just noise until you know what it means. The post Data Drift Is Not the Actual Problem: Your Monitoring Strategy Is appeared first on Towards Data Science.…

  • How to Spot and Prevent Model Drift Before it Impacts Your Business

    How to Spot and Prevent Model Drift Before it Impacts Your Business Despite the AI hype, many tech companies still rely heavily on machine learning to power critical applications, from personalized recommendations to fraud detection.  I’ve seen firsthand how undetected drifts can result in significant costs — missed fraud detection, lost revenue, and suboptimal business…

  • datadriftR: An R Package for Concept Drift Detection in Predictive Models

    datadriftR: An R Package for Concept Drift Detection in Predictive Models arXiv:2412.11308v1 Announce Type: new Abstract: Predictive models often face performance degradation due to evolving data distributions, a phenomenon known as data drift. Among its forms, concept drift, where the relationship between explanatory variables and the response variable changes, is particularly challenging to detect and…