mailitics

Tag: sets

  • Flow-Based Conformal Predictive Distributions

    Flow-Based Conformal Predictive Distributions arXiv:2602.07633v1 Announce Type: new Abstract: Conformal prediction provides a distribution-free framework for uncertainty quantification via prediction sets with exact finite-sample coverage. In low dimensions these sets are easy to interpret, but in high-dimensional or structured output spaces they are difficult to represent and use, which can limit their ability to integrate…

  • Testing Most Influential Sets

    Testing Most Influential Sets arXiv:2510.20372v1 Announce Type: new Abstract: Small subsets of data with disproportionate influence on model outcomes can have dramatic impacts on conclusions, with a few data points sometimes overturning key findings. While recent work has developed methods to identify these emph{most influential sets}, no formal theory exists to determine when their influence…

  • Valid Selection among Conformal Sets

    Valid Selection among Conformal Sets arXiv:2506.20173v1 Announce Type: new Abstract: Conformal prediction offers a distribution-free framework for constructing prediction sets with coverage guarantees. In practice, multiple valid conformal prediction sets may be available, arising from different models or methodologies. However, selecting the most desirable set, such as the smallest, can invalidate the coverage guarantees. To…

  • Minimum Volume Conformal Sets for Multivariate Regression

    Minimum Volume Conformal Sets for Multivariate Regression arXiv:2503.19068v1 Announce Type: new Abstract: Conformal prediction provides a principled framework for constructing predictive sets with finite-sample validity. While much of the focus has been on univariate response variables, existing multivariate methods either impose rigid geometric assumptions or rely on flexible but computationally expensive approaches that do not…

  • Positivity sets of hinge functions

    Positivity sets of hinge functions arXiv:2503.13512v1 Announce Type: new Abstract: In this paper we investigate which subsets of the real plane are realisable as the set of points on which a one-layer ReLU neural network takes a positive value. In the case of cones we give a full characterisation of such sets. Furthermore, we give…

  • Rule-based Evolving Fuzzy System for Time Series Forecasting: New Perspectives Based on Type-2 Fuzzy Sets Measures Approach

    Rule-based Evolving Fuzzy System for Time Series Forecasting: New Perspectives Based on Type-2 Fuzzy Sets Measures Approach arXiv:2502.03650v1 Announce Type: new Abstract: Real-world data contain uncertainty and variations that can be correlated to external variables, known as randomness. An alternative cause of randomness is chaos, which can be an important component of chaotic time series.…

  • Measuring Cross-Product Adoption Using dbt_set_similarity

    Measuring Cross-Product Adoption Using dbt_set_similarity Enhancing cross-product insights within dbt workflows Introduction For multi-product companies, one critical metric is often what is called “cross-product adoption”. (i.e. understanding how users engage with multiple offerings in a given product portfolio) One measure suggested to calculate cross-product or cross-feature usage in the popular book Hacking Growth [1] is…

  • Why Sets Are So Useful in Programming

    Why Sets Are So Useful in Programming And how you can use them to boost your code performance A set is a simple structure defined as a collection of distinct elements. Sets are most commonly seen in fields like mathematics or logic, but they’re also useful in programming for writing efficient code. In this article,…