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Second-order Poincaré inequalities and localization on the Poisson space

Maximum Probability of Independence in Transitive Matroids

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The Poisson Tail Conjecture for Primes in Short Intervals

A Complete Spectral Analysis of the CEV Operator with Applications to Arbitrage

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From Betting to Empirical Bernstein LIL

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Expected Batch Optimal Transport Plans and Consequences for Flow Matching

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Uniform Scaling Limits in AdamW-Trained Transformers

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Scaling Limits of Long-Context Transformers

Generalized Wasserstein Flow Matching: Transport Plans, Everywhere, All at Once

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Convergent Stochastic Training of Attention and Understanding LoRA

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Expressivity of Bi-Lipschitz Normalizing Flows: A Score-Based Diffusion Perspective

Time-Inhomogeneous Preconditioned Langevin Dynamics

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Convex-Geometric Error Bounds for Positive-Weight Kernel Quadrature

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Universality in Deep Neural Networks: An approach via the Lindeberg exchange principle

Soft-to-Hard Routing in Sparse Mixture-of-Experts Models

Learning Discriminators for Resampling in the Ensemble Gaussian Mixture Filter through a Normalizing Flow Approach

Decentralized Proximal Stochastic Gradient Langevin Dynamics

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Stochastic Scaling Limits and Synchronization by Noise in Deep Transformer Models

Well-Conditioned Oblivious Perturbations in Linear Space

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Diffusion Processes on Implicit Manifolds

Degrees, Levels, and Profiles of Contextuality

High-accuracy log-concave sampling with stochastic queries

Variational Optimality of Föllmer Processes in Generative Diffusions

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Dimension-Free Multimodal Sampling via Preconditioned Annealed Langevin Dynamics

A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarios

Feature Learning Dynamics in Infinite-Depth Neural Networks

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Fast and Robust Diffusion Posterior Sampling for MR Image Reconstruction Using the Preconditioned Unadjusted Langevin Algorithm

Normalizing Flows on Quotient Manifolds via Boundary Quotients

Differentiable Filtering for Learning Hidden Markov Models

Limit Theorems for Stochastic Gradient Descent in High-Dimensional Single-Layer Networks

Posterior Bayesian Neural Networks with Dependent Weights

Exponentially Fading Memory Signature

A decision-theoretic approach to dealing with uncertainty in quantum mechanics

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Efficiency of Parallel and Restart Exploration Strategies in Model Free Stochastic Simulations

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Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent

Mean-field limit from general mixtures of experts to quantum neural networks

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Mirror Descent-Ascent for mean-field min-max problems

Universal approximation property of Banach space-valued random feature models including random neural networks

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense

Conditional stochastic differential equations driven by fractional Brownian motion

Large deviations for the mean-field limit of Hawkes processes

Distribution-Free Stochastic Analysis and Robust Multilevel Vector Field Anomaly Detection

Change of measure through the Legendre transform

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