Transformers from Diffusion: A Unified Framework for Neural Message Passing

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Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning

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Actor-Critic learning for mean-field control in continuous time

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Modelling Populations of Interaction Networks via Distance Metrics

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BitNet: 1-bit Pre-training for Large Language Models

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Physics-informed Kernel Learning

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Last-iterate Convergence of Shuffling Momentum Gradient Method under the Kurdyka-Lojasiewicz Inequality

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Posterior and Variational Inference for Deep Neural Networks with Heavy-Tailed Weights

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Maximum Causal Entropy IRL in Mean-Field Games and GNEP Framework for Forward RL

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Degree of Interference: A General Framework For Causal Inference Under Interference

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Quantifying the Effectiveness of Linear Preconditioning in Markov Chain Monte Carlo

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Sparse SVM with Hard-Margin Loss: a Newton-Augmented Lagrangian Method in Reduced Dimensions

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On Model Identification and Out-of-Sample Prediction of PCR with Applications to Synthetic Controls

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Bayesian Scalar-on-Image Regression with a Spatially Varying Single-layer Neural Network Prior

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DRM Revisited: A Complete Error Analysis

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Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF

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Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers

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Score-based Causal Representation Learning: Linear and General Transformations

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On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension

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Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms

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