arXiv:2411.02225v2 Announce Type: replace Abstract: This paper presents Sparse Gradient Descent as a solution for variable selection in convex piecewise linear regression, where the model is given as the maximum of $k$-affine functions $ x mapsto max_{j in [k]} langle…
arXiv:2509.20201v1 Announce Type: cross Abstract: It has been shown that perturbing the input during training implicitly regularises the gradient of the learnt function, leading to smoother models and enhancing generalisation. However, previous research mostly considered the addition of ambient noise…
arXiv:2509.20345v1 Announce Type: cross Abstract: The rapid proliferation of high-quality synthetic data — generated by advanced AI models or collected as auxiliary data from related tasks — presents both opportunities and challenges for statistical inference. This paper introduces a GEneral…
arXiv:2509.19900v1 Announce Type: cross Abstract: This paper introduces Generalized Nonnegative Structured Kruskal Tensor Regression (NS-KTR), a novel tensor regression framework that enhances interpretability and performance through mode-specific hybrid regularization and nonnegativity constraints. Our approach accommodates both linear and logistic regression…
arXiv:2509.19930v1 Announce Type: cross Abstract: We propose a randomized neural network approach called RaNNDy for learning transfer operators and their spectral decompositions from data. The weights of the hidden layers of the neural network are randomly selected and only the…
arXiv:2509.19988v1 Announce Type: new Abstract: Efficient design of genomic perturbation experiments is crucial for accelerating drug discovery and therapeutic target identification, yet exhaustive perturbation of the human genome remains infeasible due to the vast search space of potential genetic interactions…
arXiv:2509.20101v1 Announce Type: new Abstract: Extinction times in resampling processes are fundamental yet often intractable, as previous formulas scale as $2^M$ with the number of states $M$ present in the initial probability distribution. We solve this by treating multinomial updates…
arXiv:2509.19929v1 Announce Type: new Abstract: Uncertainty Quantification (UQ) is paramount for inference in engineering applications. A common inference task is to recover full-field information of physical systems from a small number of noisy observations, a usually highly ill-posed problem. Critically,…
arXiv:2509.19788v1 Announce Type: new Abstract: A common way to estimate an unknown convex regression function $f_0: Omega subset mathbb{R}^d rightarrow mathbb{R}$ from a set of $n$ noisy observations is to fit a convex function that minimizes the sum of squared…
arXiv:2509.19577v1 Announce Type: new Abstract: Time series analysis has emerged as an important tool for improving patient diagnosis and management in healthcare applications. However, these applications commonly face two critical challenges: time misalignment and data sparsity. Traditional approaches address these…
