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

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Test-Time Training on Video Streams

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

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DisC2o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data

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Efficiently Escaping Saddle Points in Bilevel Optimization

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Towards Understanding Gradient Flow Dynamics of Homogeneous Neural Networks Beyond the Origin

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Optimal Complexity in Byzantine-Robust Distributed Stochastic Optimization with Data Heterogeneity

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Towards Unified Native Spaces in Kernel Methods

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TorchCP: A Python Library for Conformal Prediction

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Hopfield-Fenchel-Young Networks: A Unified Framework for Associative Memory Retrieval

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Identifiability of Causal Graphs under Non-Additive Conditionally Parametric Causal Models

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On the Robustness of Kernel Goodness-of-Fit Tests

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Efficient Online Prediction for High-Dimensional Time Series via Joint Tensor Tucker Decomposition

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Fast Computation of Superquantile-Constrained Optimization Through Implicit Scenario Reduction

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Collaborative likelihood-ratio estimation over graphs

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