Image de couverture de livre

Oh SnapMMD! Forecasting Stochastic Dynamics Beyond the Schrödinger Bridge's End

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Berlinghieri, Renato, Shen, Yunyi, Jiang, Jialong, Broderick, Tamara
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Methodology
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Multi-marginal Schrödinger Bridges with Iterative Reference Refinement
par: Shen, Yunyi, et autres
Publié: (2024)

Wrong Model, Right Uncertainty: Spatial Associations for Discrete Data with Misspecification
par: Burt, David R., et autres
Publié: (2025)

Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Association
par: Burt, David R., et autres
Publié: (2025)

Consistent Validation for Predictive Methods in Spatial Settings
par: Burt, David R., et autres
Publié: (2024)

Wild posteriors in the wild
par: Shen, Yunyi, et autres
Publié: (2025)

Gaussian processes at the Helm(holtz): A more fluid model for ocean currents
par: Berlinghieri, Renato, et autres
Publié: (2023)

Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box
par: Giordano, Ryan, et autres
Publié: (2023)

ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting
par: Wang, Tingting, et autres
Publié: (2026)

MMD-based Variable Importance for Distributional Random Forest
par: Bénard, Clément, et autres
Publié: (2023)

Common Functional Decompositions Can Mis-attribute Differences in Outcomes Between Populations
par: Quintero, Manuel, et autres
Publié: (2025)

Do covariates explain why these groups differ? The choice of reference group can reverse conclusions in the Oaxaca-Blinder decomposition
par: Quintero, Manuel, et autres
Publié: (2026)

Are Hourly PM2.5 Forecasts Sufficiently Accurate to Plan Your Day? Individual Decision Making in the Face of Increasing Wildfire Smoke
par: Berlinghieri, Renato, et autres
Publié: (2024)

One-shot Conditional Sampling: MMD meets Nearest Neighbors
par: Chatterjee, Anirban, et autres
Publié: (2025)

Double trouble: Predicting new variant counts across two heterogeneous populations
par: Shen, Yunyi, et autres
Publié: (2024)

Finite sample properties of parametric MMD estimation: robustness to misspecification and dependence
par: Chérief-Abdellatif, Badr-Eddine, et autres
Publié: (2019)

A Large Scale Heterogeneous Treatment Effect Estimation Framework and Its Applications of Users' Journey at Snap
par: Pan, Jing, et autres
Publié: (2025)

On Statistical Inference for High-Dimensional Binary Time Series
par: Dai, Dehao, et autres
Publié: (2025)

Dropping Just a Handful of Preferences Can Change Top Large Language Model Rankings
par: Huang, Jenny Y., et autres
Publié: (2025)

Adaptive Bayesian Optimization for Robust Identification of Stochastic Dynamical Systems
par: Xu, Jinwen, et autres
Publié: (2025)

Bridging Constraints and Stochasticity: A Fully First-Order Method for Stochastic Bilevel Optimization with Linear Constraints
par: Phan, Cac, et autres
Publié: (2025)

Approximations to worst-case data dropping: unmasking failure modes
par: Huang, Jenny Y., et autres
Publié: (2024)

Autoregressive with Slack Time Series Model for Forecasting a Partially-Observed Dynamical Time Series
par: Okuno, Akifumi, et autres
Publié: (2023)

The Stochastic Proximal Distance Algorithm
par: Jiang, Haoyu, et autres
Publié: (2022)

Learning Continuous Network Emerging Dynamics from Scarce Observations via Data-Adaptive Stochastic Processes
par: Cui, Jiaxu, et autres
Publié: (2023)

Forecasting with Hyper-Trees
par: März, Alexander, et autres
Publié: (2024)

Improving Forecasts for Heterogeneous Time Series by "Averaging", with Application to Food Demand Forecast
par: Neubauer, Lukas, et autres
Publié: (2023)

Kernel-Based Ensemble Gaussian Mixture Probability Hypothesis Density Filter
par: Durant, Dalton, et autres
Publié: (2025)

Coupling Generative Modeling and an Autoencoder with the Causal Bridge
par: Meng, Ruolin, et autres
Publié: (2025)

Robust and Sparse Regression in GLM by Stochastic Optimization
par: Kawashima, Takayuki, et autres
Publié: (2018)

Robust, Accurate Stochastic Optimization for Variational Inference
par: Dhaka, Akash Kumar, et autres
Publié: (2020)

Composite Goodness-of-fit Tests with Kernels
par: Key, Oscar, et autres
Publié: (2021)

Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemics
par: Pathak, Rajdeep, et autres
Publié: (2026)

Statistical Inference for Differentially Private Stochastic Gradient Descent
par: Xia, Xintao, et autres
Publié: (2025)

Adaptive Meta-Learning Stochastic Gradient Hamiltonian Monte Carlo Simulation for Bayesian Updating of Structural Dynamic Models
par: Meng, Xianghao, et autres
Publié: (2026)

Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data
par: Fisher, Zachary F., et autres
Publié: (2020)

Causal Interventional Prediction System for Robust and Explainable Effect Forecasting
par: Chu, Zhixuan, et autres
Publié: (2024)

Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms
par: Hong, Sungchul, et autres
Publié: (2023)

End-to-End Deep Learning for Predicting Metric Space-Valued Outputs
par: Zhou, Yidong, et autres
Publié: (2025)

High-Dimensional Linear Bandits under Stochastic Latent Heterogeneity
par: Chen, Elynn, et autres
Publié: (2025)

Asymptotic Time-Uniform Inference for Parameters in Averaged Stochastic Approximation
par: Xie, Chuhan, et autres
Publié: (2024)