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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Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2

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Decentralized Bilevel Optimization: A Perspective from Transient Iteration Complexity

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Fair Text Classification via Transferable Representations

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Stochastic Interior-Point Methods for Smooth Conic Optimization with Applications

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Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration

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Generalized multi-view model: Adaptive density estimation under low-rank constraints

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(De)-regularized Maximum Mean Discrepancy Gradient Flow

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On Probabilistic Embeddings in Optimal Dimension Reduction

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Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficient-KAN and WAV-KAN

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