Existing support vector machines(SVM) models are sensitive to noise and lack sparsity, which limits their performance. To address these issues, we combine the elastic net loss with a robust loss framework to construct a sparse $varepsilon$-insensitive bounded asymmetric elastic net loss, and integrate it with SVM to build $varepsilon$ Insensitive Zone Bounded Asymmetric Elastic Net Loss-based SVM($varepsilon$-BAEN-SVM). $varepsilon$-BAEN-SVM is both sparse and robust. Sparsity is proven by showing that samples inside the $varepsilon$-insensitive band are not support vectors. […]

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Lung cancer is a condition where there is abnormal growth of malignant cells that spread in an uncontrollable fashion in the lungs. Some common treatment strategies are surgery, chemotherapy, and radiation which aren’t the best options due to the heterogeneous nature of cancer. In personalized medicine, treatments are tailored according to the individual’s genetic information along with lifestyle aspects. In addition, AI-based deep learning methods can analyze large sets of data to find early signs of cancer, types […]

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arXiv:2409.06890v3 Announce Type: replace Abstract: Many tools exist to detect dependence between random variables, a core question across a wide range of machine learning, statistical, and scientific endeavors. Although several statistical tests guarantee eventual detection of any dependence with enough samples, standard tests may require an exorbitant amount of samples for detecting subtle dependencies between high-dimensional random variables with complex distributions. In this work, we study two related ways to learn powerful independence tests. First, we show how […]

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arXiv:2604.01373v1 Announce Type: new Abstract: Estimating the dissipativity of nonlinear systems from empirical data is useful for the analysis and control of nonlinear systems, especially when an accurate model is unavailable. Based on a Koopman operator model of the nonlinear system on a reproducing kernel Hilbert space (RKHS), the storage function and supply rate functions are expressed as kernel quadratic forms, through which the dissipative inequality is expressed as a linear operator inequality. The RKHS is specified by […]

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arXiv:2601.19321v1 Announce Type: cross Abstract: This paper investigates whether structural econometric models can rival machine learning in forecasting energy–macro dynamics while retaining causal interpretability. Using monthly data from 1999 to 2025, we develop a unified framework that integrates Time-Varying Parameter Structural VARs (TVP-SVAR) with advanced dependence structures, including DCC-GARCH, t-copulas, and mixed Clayton–Frank–Gumbel copulas. These models are empirically evaluated against leading machine learning techniques Gaussian Process Regression (GPR), Artificial Neural Networks, Random Forests, and Support Vector Regression across […]

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Almost every company and non-profit working on COVID-19 that I offered to help asked for support with clinical trials—for companies focusing on developing novel drugs, vaccines, and diagnostics, rapidly spinning up trials is one of their biggest bottlenecks.  Science remains the only way out of the COVID-19 crisis. Dramatically improving clinical trials, which are usually time-consuming and cost tens to hundreds of millions of dollars, is one of the highest-leverage ways to get out of it faster.   […]

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We give a Gordon-Greenwald-Marks (GGM) style black-box reduction from online learning to online multicalibration. Concretely, we show that to achieve high-dimensional multicalibration with respect to a class of functions H, it suffices to combine any no-regret learner over H with an expected variational inequality (EVI) solver. We also prove a converse statement showing that efficient multicalibration implies efficient EVI solving, highlighting how EVIs in multicalibration mirror the role of fixed points in the GGM result for $Φ$-regret. This […]

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arXiv:2601.05304v1 Announce Type: cross Abstract: Neuro-symbolic reasoning systems face fundamental challenges in maintaining semantic coherence while satisfying physical and logical constraints. Building upon our previous work on Ontology Neural Networks, we present an enhanced framework that integrates topological conditioning with gradient stabilization mechanisms. The approach employs Forman-Ricci curvature to capture graph topology, Deep Delta Learning for stable rank-one perturbations during constraint projection, and Covariance Matrix Adaptation Evolution Strategy for parameter optimization. Experimental evaluation across multiple problem sizes demonstrates […]

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arXiv:2602.09314v1 Announce Type: cross Abstract: Optimizers leveraging the matrix structure in neural networks, such as Shampoo and Muon, are more data-efficient than element-wise algorithms like Adam and Signum. While in specific settings, Shampoo and Muon reduce to spectral descent analogous to how Adam and Signum reduce to sign descent, their general relationship and relative data efficiency under controlled settings remain unclear. Through extensive experiments on language models, we demonstrate that Shampoo achieves higher token efficiency than Muon, mirroring […]

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