Tag: method
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Low-Degree Method Fails to Predict Robust Subspace Recovery
Low-Degree Method Fails to Predict Robust Subspace Recovery arXiv:2603.02594v1 Announce Type: new Abstract: The low-degree polynomial framework has been highly successful in predicting computational versus statistical gaps for high-dimensional problems in average-case analysis and machine learning. This success has led to the low-degree conjecture, which posits that this method captures the power and limitations of…
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An efficient, accurate, and interpretable machine learning method for computing probability of failure
An efficient, accurate, and interpretable machine learning method for computing probability of failure arXiv:2601.21089v1 Announce Type: new Abstract: We introduce a novel machine learning method called the Penalized Profile Support Vector Machine based on the Gabriel edited set for the computation of the probability of failure for a complex system as determined by a threshold…
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A Geometric Method to Spot Hallucinations Without an LLM Judge
A Geometric Method to Spot Hallucinations Without an LLM Judge Imagine a flock of birds in flight. There’s no leader. No central command. Each bird aligns with its neighbors—matching direction, adjusting speed, maintaining coherence through purely local coordination. The result is global order emerging from local consistency. Now imagine one bird flying with the same…
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Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming
Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming arXiv:2512.08948v1 Announce Type: new Abstract: We study online statistical inference for the solutions of stochastic optimization problems with equality and inequality constraints. Such problems are prevalent in statistics and machine learning, encompassing constrained $M$-estimation, physics-informed models, safe reinforcement learning, and algorithmic fairness. We develop…
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Enhanced Cyclic Coordinate Descent Methods for Elastic Net Penalized Linear Models
Enhanced Cyclic Coordinate Descent Methods for Elastic Net Penalized Linear Models arXiv:2510.19999v1 Announce Type: new Abstract: We present a novel enhanced cyclic coordinate descent (ECCD) framework for solving generalized linear models with elastic net constraints that reduces training time in comparison to existing state-of-the-art methods. We redesign the CD method by performing a Taylor expansion…
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A Smoothing Newton Method for Rank-one Matrix Recovery
A Smoothing Newton Method for Rank-one Matrix Recovery arXiv:2507.23017v1 Announce Type: new Abstract: We consider the phase retrieval problem, which involves recovering a rank-one positive semidefinite matrix from rank-one measurements. A recently proposed algorithm based on Bures-Wasserstein gradient descent (BWGD) exhibits superlinear convergence, but it is unstable, and existing theory can only prove local linear…
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The Stochastic Occupation Kernel (SOCK) Method for Learning Stochastic Differential Equations
The Stochastic Occupation Kernel (SOCK) Method for Learning Stochastic Differential Equations arXiv:2505.11622v1 Announce Type: new Abstract: We present a novel kernel-based method for learning multivariate stochastic differential equations (SDEs). The method follows a two-step procedure: we first estimate the drift term function, then the (matrix-valued) diffusion function given the drift. Occupation kernels are integral functionals…
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Extended Fiducial Inference for Individual Treatment Effects via Deep Neural Networks
Extended Fiducial Inference for Individual Treatment Effects via Deep Neural Networks arXiv:2505.01995v1 Announce Type: new Abstract: Individual treatment effect estimation has gained significant attention in recent data science literature. This work introduces the Double Neural Network (Double-NN) method to address this problem within the framework of extended fiducial inference (EFI). In the proposed method, deep…
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Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees
Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees arXiv:2504.16356v1 Announce Type: new Abstract: Graphical models are widely used in diverse application domains to model the conditional dependencies amongst a collection of random variables. In this paper, we consider settings where the graph structure is covariate-dependent, and investigate a deep neural network-based approach to…
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Coupled Hierarchical Structure Learning using Tree-Wasserstein Distance
Coupled Hierarchical Structure Learning using Tree-Wasserstein Distance arXiv:2501.03627v1 Announce Type: cross Abstract: In many applications, both data samples and features have underlying hierarchical structures. However, existing methods for learning these latent structures typically focus on either samples or features, ignoring possible coupling between them. In this paper, we introduce a coupled hierarchical structure learning method…
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Graph Max Shift: A Hill-Climbing Method for Graph Clustering
Graph Max Shift: A Hill-Climbing Method for Graph Clustering arXiv:2411.18794v1 Announce Type: new Abstract: We present a method for graph clustering that is analogous with gradient ascent methods previously proposed for clustering points in space. We show that, when applied to a random geometric graph with data iid from some density with Morse regularity, the…
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Isometry pursuit
Isometry pursuit arXiv:2411.18502v1 Announce Type: new Abstract: Isometry pursuit is a convex algorithm for identifying orthonormal column-submatrices of wide matrices. It consists of a novel normalization method followed by multitask basis pursuit. Applied to Jacobians of putative coordinate functions, it helps identity isometric embeddings from within interpretable dictionaries. We provide theoretical and experimental results justifying…