Continuously evolving rewards in an open-ended environment

Subjects: “…Artificial Intelligence & Machine Learning…”
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Recursive Causal Discovery

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Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings

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On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations

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Determine the Number of States in Hidden Markov Models via Marginal Likelihood

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Variance-Aware Estimation of Kernel Mean Embedding

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Scaling ResNets in the Large-depth Regime

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A Comparative Evaluation of Quantification Methods

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Lightning UQ Box: Uncertainty Quantification for Neural Networks

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Scaling Data-Constrained Language Models

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Curvature-based Clustering on Graphs

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Composite Goodness-of-fit Tests with Kernels

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DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning

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Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization

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Manifold Fitting under Unbounded Noise

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Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models

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Extremal graphical modeling with latent variables via convex optimization

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On the Approximation of Kernel functions

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Efficient and Robust Semi-supervised Estimation of Average Treatment Effect with Partially Annotated Treatment and Response

Subjects: “…Artificial Intelligence & Machine Learning…”
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Unbalanced Kantorovich-Rubinstein distance, plan, and barycenter on nite spaces: A statistical perspective

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