Image de couverture de livre

Minimizing $f$-Divergences by Interpolating Velocity Fields

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Liu, Song, Yu, Jiahao, Simons, Jack, Yi, Mingxuan, Beaumont, Mark
Format:	Preprint
Publié:	2023
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models
par: Sharrock, Louis, et autres
Publié: (2022)

Empirical Risk Minimization with $f$-Divergence Regularization
par: Daunas, Francisco, et autres
Publié: (2026)

Direct Fisher Score Estimation for Likelihood Maximization
par: Khoo, Sherman, et autres
Publié: (2025)

Minimum Distance Summaries for Robust Neural Posterior Estimation
par: Khoo, Sherman, et autres
Publié: (2026)

Equivalence of the Empirical Risk Minimization to Regularization on the Family of f-Divergences
par: Daunas, Francisco, et autres
Publié: (2024)

A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization
par: Daunas, Francisco, et autres
Publié: (2025)

On Weak-to-Strong Generalization and f-Divergence
par: Yao, Wei, et autres
Publié: (2025)

SoftCVI: Contrastive variational inference with self-generated soft labels
par: Ward, Daniel, et autres
Publié: (2024)

Loss Functions and Operators Generated by f-Divergences
par: Roulet, Vincent, et autres
Publié: (2025)

Regularized $f$-Divergence Kernel Tests
par: Ribero, Mónica, et autres
Publié: (2026)

$f$-PO: Generalizing Preference Optimization with $f$-divergence Minimization
par: Han, Jiaqi, et autres
Publié: (2024)

f-GRPO and Beyond: Divergence-Based Reinforcement Learning Algorithms for General LLM Alignment
par: Haldar, Rajdeep, et autres
Publié: (2026)

Variational f-divergence Minimization
par: Zhang, Mingtian, et autres
Publié: (2019)

Partition Function Estimation under Bounded f-Divergence
par: Block, Adam, et autres
Publié: (2026)

Displacement-Resistant Extensions of DPO with Nonconvex $f$-Divergences
par: Pipano, Idan, et autres
Publié: (2026)

Robust Semi-supervised Learning via $f$-Divergence and $α$-Rényi Divergence
par: Aminian, Gholamali, et autres
Publié: (2024)

Generalization Error of $f$-Divergence Stabilized Algorithms via Duality
par: Daunas, Francisco, et autres
Publié: (2025)

Bounding Neyman-Pearson Region with $f$-Divergences
par: Mullhaupt, Andrew, et autres
Publié: (2025)

Regularization via f-Divergence: An Application to Multi-Oxide Spectroscopic Analysis
par: Li, Weizhi, et autres
Publié: (2025)

Robust Offline Reinforcement Learning with Linearly Structured f-Divergence Regularization
par: Tang, Cheng, et autres
Publié: (2024)

Sampling via Stochastic Interpolants by Langevin-based Velocity and Initialization Estimation in Flow ODEs
par: Duan, Chenguang, et autres
Publié: (2026)

$f$-Divergence Based Classification: Beyond the Use of Cross-Entropy
par: Novello, Nicola, et autres
Publié: (2024)

Convergence Properties of Natural Gradient Descent for Minimizing KL Divergence
par: Datar, Adwait, et autres
Publié: (2025)

MiniDisc: Minimal Distillation Schedule for Language Model Compression
par: Zhang, Chen, et autres
Publié: (2022)

Finite-Time Analysis of Discrete-Time Stochastic Interpolants
par: Liu, Yuhao, et autres
Publié: (2025)

Self-sufficient Independent Component Analysis via KL Minimizing Flows
par: Liu, Song
Publié: (2025)

On $f$-Divergence Principled Domain Adaptation: An Improved Framework
par: Wang, Ziqiao, et autres
Publié: (2024)

Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization
par: Kim, Kyurae, et autres
Publié: (2025)

Divergence Minimization Preference Optimization for Diffusion Model Alignment
par: Li, Binxu, et autres
Publié: (2025)

Generative Anchored Fields: Controlled Data Generation via Emergent Velocity Fields and Transport Algebra
par: Deressa, Deressa Wodajo, et autres
Publié: (2025)

Robust Generative Learning with Lipschitz-Regularized $α$-Divergences Allows Minimal Assumptions on Target Distributions
par: Chen, Ziyu, et autres
Publié: (2024)

Representing Flow Fields with Divergence-Free Kernels for Reconstruction
par: Ni, Xingyu, et autres
Publié: (2025)

Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
par: Chen, Yifan, et autres
Publié: (2024)

Stochastic Interpolants in Hilbert Spaces
par: Yu, James Boran, et autres
Publié: (2026)

Mutual Information Estimation via $f$-Divergence and Data Derangements
par: Letizia, Nunzio A., et autres
Publié: (2023)

A Unified Framework for Diffusion Model Unlearning with f-Divergence
par: Novello, Nicola, et autres
Publié: (2025)

Domain Adaptation with Cauchy-Schwarz Divergence
par: Yin, Wenzhe, et autres
Publié: (2024)

Direct Debiased Machine Learning via Bregman Divergence Minimization
par: Kato, Masahiro
Publié: (2025)

Differentiable Annealed Importance Sampling Minimizes The Symmetrized Kullback-Leibler Divergence Between Initial and Target Distribution
par: Zenn, Johannes, et autres
Publié: (2024)

Unsupervised 4D Flow MRI Velocity Enhancement and Unwrapping Using Divergence-Free Neural Networks
par: Bisbal, Javier, et autres
Publié: (2026)