On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inference

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An Augmentation Overlap Theory of Contrastive Learning

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Algorithms for ridge estimation with convergence guarantees

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Talent: A Tabular Analytics and Learning Toolbox

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Inferring Change Points in High-Dimensional Regression via Approximate Message Passing

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Universality of Kernel Random Matrices and Kernel Regression in the Quadratic Regime

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Lexicographic Lipschitz Bandits: New Algorithms and a Lower Bound

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On the Natural Gradient of the Evidence Lower Bound

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Geometry and Stability of Supervised Learning Problems

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Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination

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Optimal Rates of Kernel Ridge Regression under Source Condition in Large Dimensions

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A Hybrid Weighted Nearest Neighbour Classifier for Semi-Supervised Learning

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Scalable and Adaptive Variational Bayes Methods for Hawkes Processes

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Biological Sequence Kernels with Guaranteed Flexibility

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Unified Discrete Diffusion for Categorical Data

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Reinforcement Learning for Infinite-Dimensional Systems

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Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation

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Generation of Geodesics with Actor-Critic Reinforcement Learning to Predict Midpoints

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Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation

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Sparse Semiparametric Discriminant Analysis for High-dimensional Zero-inflated Data

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