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Plug-in estimation of Schr\" odinger bridges
We propose a procedure for estimating the Schr\" odinger bridge between two probability
distributions. Unlike existing approaches, our method does not require iteratively simulating …
distributions. Unlike existing approaches, our method does not require iteratively simulating …
Recurrent Interpolants for Probabilistic Time Series Prediction
Sequential models like recurrent neural networks and transformers have become standard
for probabilistic multivariate time series forecasting across various domains. Despite their …
for probabilistic multivariate time series forecasting across various domains. Despite their …
A Simulation-Free Deep Learning Approach to Stochastic Optimal Control
We propose a simulation-free algorithm for the solution of generic problems in stochastic
optimal control (SOC). Unlike existing methods, our approach does not require the solution …
optimal control (SOC). Unlike existing methods, our approach does not require the solution …
Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting
Flow matching has recently emerged as a powerful paradigm for generative modeling and
has been extended to probabilistic time series forecasting in latent spaces. However, the …
has been extended to probabilistic time series forecasting in latent spaces. However, the …
A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data
Analyses of the cosmic 21-cm signal are hampered by astrophysical foregrounds that are far
stronger than the signal itself. These foregrounds, typically confined to a wedge-shaped …
stronger than the signal itself. These foregrounds, typically confined to a wedge-shaped …
Flow Map Matching
Generative models based on dynamical transport of measure, such as diffusion models, flow
matching models, and stochastic interpolants, learn an ordinary or stochastic differential …
matching models, and stochastic interpolants, learn an ordinary or stochastic differential …
Stochastic Flow Matching for Resolving Small-Scale Physics
Conditioning diffusion and flow models have proven effective for super-resolving small-scale
details in natural images. However, in physical sciences such as weather, super-resolving …
details in natural images. However, in physical sciences such as weather, super-resolving …
Harmonic Path Integral Diffusion
In this manuscript, we present a novel approach for sampling from a continuous multivariate
probability distribution, which may either be explicitly known (up to a normalization factor) or …
probability distribution, which may either be explicitly known (up to a normalization factor) or …
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling
M Shen, C Cheng - arxiv preprint arxiv:2501.18871, 2025 - arxiv.org
Inspired by the ubiquitous use of differential equations to model continuous dynamics across
diverse scientific and engineering domains, we propose a novel and intuitive approach to …
diverse scientific and engineering domains, we propose a novel and intuitive approach to …
Variational Inference for Interacting Particle Systems with Discrete Latent States
G Migliorini, P Smyth - NeurIPS 2024 Workshop on Bayesian Decision … - openreview.net
We present a novel Bayesian learning framework for interacting particle systems with
discrete latent states, addressing the challenge of inferring dynamics from partial, noisy …
discrete latent states, addressing the challenge of inferring dynamics from partial, noisy …