EPINN: Enhanced Physics-Informed Neural Network for Solving Continuous Integral Equations

Subjects: “…Artificial Intelligence & Machine Learning…”
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Monitoring Teams of AI Agents

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Scaling Safe Policy Improvement: Monte Carlo Tree Search and Policy Iteration Strategies

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Detecting Generative Model Inversion Attacks for Protecting Intellectual Property of Deep Neural Networks

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Finding Minimal Plan Reductions Using Classical Planning

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Regret Analysis for Randomized Gaussian Process Upper Confidence Bound

Subjects: “…Artificial Intelligence & Machine Learning…”
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Towards an Ontology-Driven Approach to Document Bias

Subjects: “…Artificial Intelligence & Machine Learning…”
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Prioritised Planning: Completeness, Optimality, and Complexity

Subjects: “…Artificial Intelligence & Machine Learning…”
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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

Subjects: “…Artificial Intelligence & Machine Learning…”
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Hopfield-Fenchel-Young Networks: A Unified Framework for Associative Memory Retrieval

Subjects: “…Artificial Intelligence & Machine Learning…”
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Identifiability of Causal Graphs under Non-Additive Conditionally Parametric Causal Models

Subjects: “…Artificial Intelligence & Machine Learning…”
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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

Subjects: “…Artificial Intelligence & Machine Learning…”
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Fast Computation of Superquantile-Constrained Optimization Through Implicit Scenario Reduction

Subjects: “…Artificial Intelligence & Machine Learning…”
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Collaborative likelihood-ratio estimation over graphs

Subjects: “…Artificial Intelligence & Machine Learning…”
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On the Utility of Equal Batch Sizes for Inference in Stochastic Gradient Descent

Subjects: “…Artificial Intelligence & Machine Learning…”
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Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies

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