Optimal and Efficient Algorithms for Decentralized Online Convex Optimization

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Characterizing Dynamical Stability of Stochastic Gradient Descent in Overparameterized Learning

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PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks

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Score-Aware Policy-Gradient and Performance Guarantees using Local Lyapunov Stability

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On the O(sqrt(d)/T^(1/4)) Convergence Rate of RMSProp and Its Momentum Extension Measured by l_1 Norm

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Categorical Semantics of Compositional Reinforcement Learning

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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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