Hierarchical Decision Making Based on Structural Information Principles

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Generative Adversarial Networks: Dynamics

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"What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts

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Assumption-lean and data-adaptive post-prediction inference

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Bagged Regularized k-Distances for Anomaly Detection

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Four Axiomatic Characterizations of the Integrated Gradients Attribution Method

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Fast Algorithm for Constrained Linear Inverse Problems

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High-Rank Irreducible Cartesian Tensor Decomposition and Bases of Equivariant Spaces

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Best Linear Unbiased Estimate from Privatized Contingency Tables

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Interpretable Global Minima of Deep ReLU Neural Networks on Sequentially Separable Data

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Enhanced Feature Learning via Regularisation: Integrating Neural Networks and Kernel Methods

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Data-Driven Performance Guarantees for Classical and Learned Optimizers

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Contextual Bandits with Stage-wise Constraints

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Boosting Causal Additive Models

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Frequentist Guarantees of Distributed (Non)-Bayesian Inference

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Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection

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EMaP: Explainable AI with Manifold-based Perturbations

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Autoencoders in Function Space

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Nonparametric Regression on Random Geometric Graphs Sampled from Submanifolds

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System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning

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