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Tag: sensitivity

  • Gradient-based Active Learning with Gaussian Processes for Global Sensitivity Analysis

    Gradient-based Active Learning with Gaussian Processes for Global Sensitivity Analysis arXiv:2601.11790v1 Announce Type: new Abstract: Global sensitivity analysis of complex numerical simulators is often limited by the small number of model evaluations that can be afforded. In such settings, surrogate models built from a limited set of simulations can substantially reduce the computational burden, provided…

  • A Sensitivity Approach to Causal Inference Under Limited Overlap

    A Sensitivity Approach to Causal Inference Under Limited Overlap arXiv:2511.22003v1 Announce Type: new Abstract: Limited overlap between treated and control groups is a key challenge in observational analysis. Standard approaches like trimming importance weights can reduce variance but introduce a fundamental bias. We propose a sensitivity framework for contextualizing findings under limited overlap, where we…

  • Deterministic Coreset Construction via Adaptive Sensitivity Trimming

    Deterministic Coreset Construction via Adaptive Sensitivity Trimming arXiv:2508.18340v1 Announce Type: new Abstract: We develop a rigorous framework for deterministic coreset construction in empirical risk minimization (ERM). Our central contribution is the Adaptive Deterministic Uniform-Weight Trimming (ADUWT) algorithm, which constructs a coreset by excising points with the lowest sensitivity bounds and applying a data-dependent uniform weight…

  • Distributional Sensitivity Analysis: Enabling Differentiability in Sample-Based Inference

    Distributional Sensitivity Analysis: Enabling Differentiability in Sample-Based Inference arXiv:2508.09347v1 Announce Type: new Abstract: We present two analytical formulae for estimating the sensitivity — namely, the gradient or Jacobian — at given realizations of an arbitrary-dimensional random vector with respect to its distributional parameters. The first formula interprets this sensitivity as partial derivatives of the inverse…

  • Simulation-Based Sensitivity Analysis in Optimal Treatment Regimes and Causal Decomposition with Individualized Interventions

    Simulation-Based Sensitivity Analysis in Optimal Treatment Regimes and Causal Decomposition with Individualized Interventions arXiv:2506.19010v1 Announce Type: new Abstract: Causal decomposition analysis aims to assess the effect of modifying risk factors on reducing social disparities in outcomes. Recently, this analysis has incorporated individual characteristics when modifying risk factors by utilizing optimal treatment regimes (OTRs). Since the…

  • Exploring specialization and sensitivity of convolutional neural networks in the context of simultaneous image augmentations

    Exploring specialization and sensitivity of convolutional neural networks in the context of simultaneous image augmentations arXiv:2503.03283v1 Announce Type: new Abstract: Drawing parallels with the way biological networks are studied, we adapt the treatment–control paradigm to explainable artificial intelligence research and enrich it through multi-parametric input alterations. In this study, we propose a framework for investigating…