Score-Based Diffusion Models in Function Space

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Regularized Rényi Divergence Minimization through Bregman Proximal Gradient Algorithms

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WEFE: A Python Library for Measuring and Mitigating Bias in Word Embeddings

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Frontiers to the learning of nonparametric hidden Markov models

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On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes

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Classification in the high dimensional Anisotropic mixture framework: A new take on Robust Interpolation

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Universal Online Convex Optimization Meets Second-order Bounds

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Sample Complexity of the Linear Quadratic Regulator: A Reinforcement Learning Lens

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Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests

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skglm: Improving scikit-learn for Regularized Generalized Linear Models

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Losing Momentum in Continuous-time Stochastic Optimisation

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Latent Process Models for Functional Network Data

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Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models

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On the Ability of Deep Networks to Learn Symmetries from Data: A Neural Kernel Theory

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Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems

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Deep Generative Models: Complexity, Dimensionality, and Approximation

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ClimSim-Online: A Large Multi-Scale Dataset and Framework for Hybrid Physics-ML Climate Emulation

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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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