Efficient Online Prediction for High-Dimensional Time Series via Joint Tensor Tucker Decomposition

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Fast Computation of Superquantile-Constrained Optimization Through Implicit Scenario Reduction

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Collaborative likelihood-ratio estimation over graphs

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On the Utility of Equal Batch Sizes for Inference in Stochastic Gradient Descent

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Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies

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Convergence and Sample Complexity of Natural Policy Gradient Primal-Dual Methods for Constrained MDPs

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Differentially Private Multivariate Medians

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VFOSA: Variance-Reduced Fast Operator Splitting Algorithms for Generalized Equations

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Scaling Capability in Token Space: An Analysis of Large Vision Language Model

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Minimax Optimal Two-Sample Testing under Local Differential Privacy

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Jackpot: Approximating Uncertainty Domains with Adversarial Manifolds

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An Asymptotically Optimal Coordinate Descent Algorithm for Learning Bayesian Networks from Gaussian Models

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Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation

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A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning

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Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection

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Stable learning using spiking neural networks equipped with affine encoders and decoders

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Efficient Knowledge Deletion from Trained Models Through Layer-wise Partial Machine Unlearning

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General Loss Functions Lead to (Approximate) Interpolation in High Dimensions

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Piecewise deterministic sampling with splitting schemes

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Hierarchical and Stochastic Crystallization Learning: Geometrically Leveraged Nonparametric Regression with Delaunay Triangulation

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