Towards Understanding Gradient Flow Dynamics of Homogeneous Neural Networks Beyond the Origin

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Optimal Complexity in Byzantine-Robust Distributed Stochastic Optimization with Data Heterogeneity

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Towards Unified Native Spaces in Kernel Methods

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TorchCP: A Python Library for Conformal Prediction

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Hopfield-Fenchel-Young Networks: A Unified Framework for Associative Memory Retrieval

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Identifiability of Causal Graphs under Non-Additive Conditionally Parametric Causal Models

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Fundamental Limits of Membership Inference Attacks on Machine Learning Models

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On the Robustness of Kernel Goodness-of-Fit Tests

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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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