Boosting Causal Additive Models

Subjects: “…Artificial Intelligence & Machine Learning…”
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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

Subjects: “…Artificial Intelligence & Machine Learning…”
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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

Subjects: “…Artificial Intelligence & Machine 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

Subjects: “…Artificial Intelligence & Machine Learning…”
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WEFE: A Python Library for Measuring and Mitigating Bias in Word Embeddings

Subjects: “…Artificial Intelligence & Machine Learning…”
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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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Classification in the high dimensional Anisotropic mixture framework: A new take on Robust Interpolation

Subjects: “…Artificial Intelligence & Machine Learning…”
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Universal Online Convex Optimization Meets Second-order Bounds

Subjects: “…Artificial Intelligence & Machine Learning…”
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Sample Complexity of the Linear Quadratic Regulator: A Reinforcement Learning Lens

Subjects: “…Artificial Intelligence & Machine Learning…”
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Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests

Subjects: “…Artificial Intelligence & Machine Learning…”
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