Imagen de Portada

Deep conditional distribution learning via conditional Föllmer flow

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Chang, Jinyuan, Ding, Zhao, Jiao, Yuling, Li, Ruoxuan, Yang, Jerry Zhijian
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Provable Diffusion Posterior Sampling for Bayesian Inversion
por: Chang, Jinyuan, et al.
Publicado: (2025)

DRM Revisited: A Complete Error Analysis
por: Jiao, Yuling, et al.
Publicado: (2024)

Inference-Time Alignment for Diffusion Models via Variationally Stable Doob's Matching
por: Chang, Jinyuan, et al.
Publicado: (2026)

Characteristic Learning for Provable One Step Generation
por: Ding, Zhao, et al.
Publicado: (2024)

Semi-Supervised Deep Sobolev Regression: Estimation and Variable Selection by ReQU Neural Network
por: Ding, Zhao, et al.
Publicado: (2024)

Deep Transfer Learning: Model Framework and Error Analysis
por: Jiao, Yuling, et al.
Publicado: (2024)

Deep Bootstrap
por: Chang, Jinyuan, et al.
Publicado: (2026)

Approximation Error Upper and Lower Bounds for Hölder Class with Transformers
por: He, Xin, et al.
Publicado: (2026)

Nonlinear Assimilation via Score-based Sequential Langevin Sampling
por: Ding, Zhao, et al.
Publicado: (2024)

Adv-SSL: Adversarial Self-Supervised Representation Learning with Theoretical Guarantees
por: Duan, Chenguang, et al.
Publicado: (2024)

Sampling via Stochastic Interpolants by Langevin-based Velocity and Initialization Estimation in Flow ODEs
por: Duan, Chenguang, et al.
Publicado: (2026)

GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs
por: Jiao, Yuling, et al.
Publicado: (2023)

Non-asymptotic Approximation Error Bounds of Parameterized Quantum Circuits
por: Yu, Zhan, et al.
Publicado: (2023)

Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
por: Chen, Yifan, et al.
Publicado: (2024)

Path Integral Optimiser: Global Optimisation via Neural Schrödinger-Föllmer Diffusion
por: McGuinness, Max, et al.
Publicado: (2025)

Variational Optimality of Föllmer Processes in Generative Diffusions
por: Chen, Yifan, et al.
Publicado: (2026)

Deep learning-based fault identification in condition monitoring
por: Dhungana, Hariom, et al.
Publicado: (2024)

The conditional-mean barrier: From deterministic regression to conditional distribution learning
por: Chen, Junfeng
Publicado: (2026)

Approximation Bounds for Recurrent Neural Networks with Application to Regression
por: Jiao, Yuling, et al.
Publicado: (2024)

Distributional Off-Policy Evaluation with Deep Quantile Process Regression
por: Kuang, Qi, et al.
Publicado: (2026)

AdsorbFlow: energy-conditioned flow matching enables fast and realistic adsorbate placement
por: Qiu, Jiangjie, et al.
Publicado: (2026)

Distance metric learning for conditional anomaly detection
por: Valko, Michal, et al.
Publicado: (2026)

Learning conditional distributions on continuous spaces
por: Bénézet, Cyril, et al.
Publicado: (2024)

Machine learning-based condition monitoring of powertrains in modern electric drives
por: Li, Dinan, et al.
Publicado: (2025)

Wasserstein bounds for denoising diffusion probabilistic models via the Föllmer process
por: Koike, Yuta
Publicado: (2026)

Convergence Analysis of Flow Matching in Latent Space with Transformers
por: Jiao, Yuling, et al.
Publicado: (2024)

Constructive conditional normalizing flows
por: Geshkovski, Borjan, et al.
Publicado: (2026)

Deep learning and the rate of approximation by flows
por: Cheng, Jingpu, et al.
Publicado: (2026)

Schrödinger bridge based deep conditional generative learning
por: Huang, Hanwen
Publicado: (2024)

Kernel conditional tests from learning-theoretic bounds
por: Massiani, Pierre-François, et al.
Publicado: (2025)

Controllability in preference-conditioned multi-objective reinforcement learning
por: Molins, Pau de las Heras, et al.
Publicado: (2026)

Generative modeling of granular flow on inclined planes using conditional flow matching
por: Li, Xuyang, et al.
Publicado: (2026)

Robust Fuzzy local k-plane clustering with mixture distance of hinge loss and L1 norm
por: Huang, Junjun, et al.
Publicado: (2026)

In-Context Deep Learning via Transformer Models
por: Wu, Weimin, et al.
Publicado: (2024)

Optimal structure learning and conditional independence testing
por: Gao, Ming, et al.
Publicado: (2025)

Orthogonal machine learning for conditional odds and risk ratios
por: Ge, Jiacheng, et al.
Publicado: (2026)

Calibrating conditional risk
por: Vasilyev, Andrey, et al.
Publicado: (2026)

Nonparametric estimation of conditional probability distributions using a generative approach based on conditional push-forward neural networks
por: Franco, Nicola Rares, et al.
Publicado: (2025)

Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows
por: Baumgartner, David, et al.
Publicado: (2026)

Middle-mile logistics through the lens of goal-conditioned reinforcement learning
por: Eberhard, Onno, et al.
Publicado: (2026)