A Symplectic Analysis of Alternating Mirror Descent

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Two-way Node Popularity Model for Directed and Bipartite Networks

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Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization

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Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood

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A causal fused lasso for interpretable heterogeneous treatment effects estimation

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Unsupervised Feature Selection via Nonnegative Orthogonal Constrained Regularized Minimization

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Reparameterized Complex-valued Neurons Can Efficiently Learn More than Real-valued Neurons via Gradient Descent

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Hierarchical Causal Models

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Optimizing Attention with Mirror Descent: Generalized Max-Margin Token Selection

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Adaptive Forward Stepwise: A Method for High Sparsity Regression

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Optimization and Generalization of Gradient Descent for Shallow ReLU Networks with Minimal Width

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Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection

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CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration

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Persistence Diagrams Estimation of Multivariate Piecewise H{"o}lder-continuous Signals

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Exploring Novel Uncertainty Quantification through Forward Intensity Function Modeling

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Generative Bayesian Inference with GANs

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Communication-efficient Distributed Statistical Inference for Massive Data with Heterogeneous Auxiliary Information

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Decorrelated Local Linear Estimator: Inference for Non-linear Effects in High-dimensional Additive Models

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Refined Risk Bounds for Unbounded Losses via Transductive Priors

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A Common Interface for Automatic Differentiation

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