Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions

Subjects: “…Artificial Intelligence & Machine Learning…”
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Regularizing Hard Examples Improves Adversarial Robustness

Subjects: “…Artificial Intelligence & Machine Learning…”
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Random ReLU Neural Networks as Non-Gaussian Processes

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Riemannian Bilevel Optimization

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Supervised Learning with Evolving Tasks and Performance Guarantees

Subjects: “…Artificial Intelligence & Machine Learning…”
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Error estimation and adaptive tuning for unregularized robust M-estimator

Subjects: “…Artificial Intelligence & Machine Learning…”
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From Sparse to Dense Functional Data in High Dimensions: Revisiting Phase Transitions from a Non-Asymptotic Perspective

Subjects: “…Artificial Intelligence & Machine Learning…”
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Locally Private Causal Inference for Randomized Experiments

Subjects: “…Artificial Intelligence & Machine Learning…”
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Estimating Network-Mediated Causal Effects via Principal Components Network Regression

Subjects: “…Artificial Intelligence & Machine Learning…”
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Selective Inference with Distributed Data

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Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization

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An Axiomatic Definition of Hierarchical Clustering

Subjects: “…Artificial Intelligence & Machine Learning…”
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Test-Time Training on Video Streams

Subjects: “…Artificial Intelligence & Machine Learning…”
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Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback

Subjects: “…Artificial Intelligence & Machine Learning…”
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A Random Matrix Approach to Low-Multilinear-Rank Tensor Approximation

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Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents

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Enhancing Graph Representation Learning with Localized Topological Features

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Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization

Subjects: “…Artificial Intelligence & Machine Learning…”
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DisC2o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data

Subjects: “…Artificial Intelligence & Machine Learning…”
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Bayes Meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes

Subjects: “…Artificial Intelligence & Machine Learning…”
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