Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching

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Deep Variational Multivariate Information Bottleneck - A Framework for Variational Losses

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Diffeomorphism-based feature learning using Poincaré inequalities on augmented input space

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Finite Expression Method for Solving High-Dimensional Partial Differential Equations

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Randomly Projected Convex Clustering Model: Motivation, Realization, and Cluster Recovery Guarantees

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Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary

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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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