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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Distribution Estimation under the Infinity Norm

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Extending Temperature Scaling with Homogenizing Maps

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Density Estimation Using the Perceptron

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Simplex Constrained Sparse Optimization via Tail Screening

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Score-Based Diffusion Models in Function Space

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Regularized Rényi Divergence Minimization through Bregman Proximal Gradient Algorithms

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WEFE: A Python Library for Measuring and Mitigating Bias in Word Embeddings

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Frontiers to the learning of nonparametric hidden Markov models

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On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes

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