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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PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark

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The Effect of SGD Batch Size on Autoencoder Learning: Sparsity, Sharpness, and Feature Learning

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Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data

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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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Learning Global Nash Equilibrium in Team Competitive Games with Generalized Fictitious Cross-Play

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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

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