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Tag: non

  • The Partition Principle Revisited: Non-Equal Volume Designs Achieve Minimal Expected Star Discrepancy

    The Partition Principle Revisited: Non-Equal Volume Designs Achieve Minimal Expected Star Discrepancy arXiv:2603.00202v1 Announce Type: new Abstract: We study the expected star discrepancy under a newly designed class of non-equal volume partitions. The main contributions are twofold. First, we establish a strong partition principle for the star discrepancy, showing that our newly designed non-equal volume…

  • Non-Stationary Functional Bilevel Optimization

    Non-Stationary Functional Bilevel Optimization arXiv:2601.15363v1 Announce Type: new Abstract: Functional bilevel optimization (FBO) provides a powerful framework for hierarchical learning in function spaces, yet current methods are limited to static offline settings and perform suboptimally in online, non-stationary scenarios. We propose SmoothFBO, the first algorithm for non-stationary FBO with both theoretical guarantees and practical scalability.…

  • Mastering Non-Linear Data: A Guide to Scikit-Learn’s SplineTransformer

    Mastering Non-Linear Data: A Guide to Scikit-Learn’s SplineTransformer Forget stiff lines and wild polynomials. Discover why Splines are the “Goldilocks” of feature engineering, offering the perfect balance of flexibility and discipline for non-linear data using Scikit-Learn’s SplineTransformer. The post Mastering Non-Linear Data: A Guide to Scikit-Learn’s SplineTransformer appeared first on Towards Data Science. Gustavo Santos…

  • Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning

    Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning arXiv:2508.16027v1 Announce Type: new Abstract: Transformers have demonstrated exceptional performance across a wide range of domains. While their ability to perform reinforcement learning in-context has been established both theoretically and empirically, their behavior in non-stationary environments remains less understood. In this study, we address this gap…

  • Enjoying Non-linearity in Multinomial Logistic Bandits

    Enjoying Non-linearity in Multinomial Logistic Bandits arXiv:2507.05306v1 Announce Type: new Abstract: We consider the multinomial logistic bandit problem, a variant of generalized linear bandits where a learner interacts with an environment by selecting actions to maximize expected rewards based on probabilistic feedback from multiple possible outcomes. In the binary setting, recent work has focused on…

  • A Framework for Non-Linear Attention via Modern Hopfield Networks

    A Framework for Non-Linear Attention via Modern Hopfield Networks arXiv:2506.11043v1 Announce Type: new Abstract: In this work we propose an energy functional along the lines of Modern Hopfield Networks (MNH), the stationary points of which correspond to the attention due to Vaswani et al. [12], thus unifying both frameworks. The minima of this landscape form…

  • Learning Multi-Attribute Differential Graphs with Non-Convex Penalties

    Learning Multi-Attribute Differential Graphs with Non-Convex Penalties arXiv:2505.09748v1 Announce Type: new Abstract: We consider the problem of estimating differences in two multi-attribute Gaussian graphical models (GGMs) which are known to have similar structure, using a penalized D-trace loss function with non-convex penalties. The GGM structure is encoded in its precision (inverse covariance) matrix. Existing methods…

  • An Incremental Non-Linear Manifold Approximation Method

    An Incremental Non-Linear Manifold Approximation Method arXiv:2504.09068v1 Announce Type: new Abstract: Analyzing high-dimensional data presents challenges due to the “curse of dimensionality”, making computations intensive. Dimension reduction techniques, categorized as linear or non-linear, simplify such data. Non-linear methods are particularly essential for efficiently visualizing and processing complex data structures in interactive and graphical applications. This…

  • Feature learning from non-Gaussian inputs: the case of Independent Component Analysis in high dimensions

    Feature learning from non-Gaussian inputs: the case of Independent Component Analysis in high dimensions arXiv:2503.23896v1 Announce Type: new Abstract: Deep neural networks learn structured features from complex, non-Gaussian inputs, but the mechanisms behind this process remain poorly understood. Our work is motivated by the observation that the first-layer filters learnt by deep convolutional neural networks…

  • Asymptotics of Non-Convex Generalized Linear Models in High-Dimensions: A proof of the replica formula

    Asymptotics of Non-Convex Generalized Linear Models in High-Dimensions: A proof of the replica formula arXiv:2502.20003v1 Announce Type: new Abstract: The analytic characterization of the high-dimensional behavior of optimization for Generalized Linear Models (GLMs) with Gaussian data has been a central focus in statistics and probability in recent years. While convex cases, such as the LASSO,…

  • SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph

    SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph arXiv:2502.07857v1 Announce Type: new Abstract: Causal discovery can be computationally demanding for large numbers of variables. If we only wish to estimate the causal effects on a small subset of target variables, we might not need to learn the causal graph for…

  • Non-asymptotic analysis of the performance of the penalized least trimmed squares in sparse models

    Non-asymptotic analysis of the performance of the penalized least trimmed squares in sparse models arXiv:2501.04946v1 Announce Type: new Abstract: The least trimmed squares (LTS) estimator is a renowned robust alternative to the classic least squares estimator and is popular in location, regression, machine learning, and AI literature. Many studies exist on LTS, including its robustness,…

  • Non-Technical Principles All Data Scientists Should Have

    Non-Technical Principles All Data Scientists Should Have Making you a better data scientist, and enhancing your career. Continue reading on Towards Data Science » Marc Matterson Go to original source