Poster "stochastic differential equations" Papers
31 papers found
Conference
Adam Reduces a Unique Form of Sharpness: Theoretical Insights Near the Minimizer Manifold
Xinghan Li, Haodong Wen, Kaifeng Lyu
Adaptive Methods through the Lens of SDEs: Theoretical Insights on the Role of Noise
Enea Monzio Compagnoni, Tianlin Liu, Rustem Islamov et al.
Beyond Scores: Proximal Diffusion Models
Zhenghan Fang, Mateo Diaz, Sam Buchanan et al.
Continuous Q-Score Matching: Diffusion Guided Reinforcement Learning for Continuous-Time Control
Chengxiu HUA, Jiawen Gu, Yushun Tang
Continuous-time Riemannian SGD and SVRG Flows on Wasserstein Probabilistic Space
Mingyang Yi, Bohan Wang
Cross-fluctuation phase transitions reveal sampling dynamics in diffusion models
Sai Niranjan Ramachandran, Manish Krishan Lal, Suvrit Sra
Denoising Levy Probabilistic Models
Dario Shariatian, Umut Simsekli, Alain Oliviero Durmus
Improved Sampling Algorithms for Lévy-Itô Diffusion Models
Vadim Popov, Assel Yermekova, Tasnima Sadekova et al.
In-Context Learning of Stochastic Differential Equations with Foundation Inference Models
Patrick Seifner, Kostadin Cvejoski, David Berghaus et al.
Inference-Time Scaling for Flow Models via Stochastic Generation and Rollover Budget Forcing
Jaihoon Kim, Taehoon Yoon, Jisung Hwang et al.
MaRS: A Fast Sampler for Mean Reverting Diffusion based on ODE and SDE Solvers
Ao Li, Wei Fang, Hongbo Zhao et al.
Parameter Dynamics of Online Machine Learning and Test-time Adaptation
Jae-Hong Lee
Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations
Litu Rout, Yujia Chen, Nataniel Ruiz et al.
Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes
Georg Manten, Cecilia Casolo, Emilio Ferrucci et al.
Statistical Analysis of the Sinkhorn Iterations for Two-Sample Schr\"{o}dinger Bridge Estimation
Ibuki Maeda, Yao, Atsushi Nitanda
System-Embedded Diffusion Bridge Models
Bartlomiej Sobieski, Matthew Tivnan, Yuang Wang et al.
TADA: Improved Diffusion Sampling with Training-free Augmented DynAmics
Tianrong Chen, Huangjie Zheng, David Berthelot et al.
Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations
Richard Bergna, Sergio Calvo Ordoñez, Felix Opolka et al.
Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models
Ludwig Winkler, Lorenz Richter, Manfred Opper
FESSNC: Fast Exponentially Stable and Safe Neural Controller
Jingdong Zhang, Luan Yang, Qunxi Zhu et al.
Generalization Bounds for Heavy-Tailed SDEs through the Fractional Fokker-Planck Equation
Benjamin Dupuis, Umut Simsekli
Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge
Yue Conghan, Zhengwei Peng, Junlong Ma et al.
Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD
Yijun Wan, Melih Barsbey, Abdellatif Zaidi et al.
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks
Dongyoung Lim, Sotirios Sabanis
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
Yifan Chen, Mark Goldstein, Mengjian Hua et al.
Probabilistic Time Series Modeling with Decomposable Denoising Diffusion Model
Tijin Yan, Hengheng Gong, Yongping He et al.
Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference
JIAN XU, Delu Zeng, John Paisley
Stabilizing Policy Gradients for Stochastic Differential Equations via Consistency with Perturbation Process
Xiangxin Zhou, Liang Wang, Yichi Zhou
TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors
Yichuan Mo, Hui Huang, Mingjie Li et al.
Understanding Diffusion Models by Feynman's Path Integral
Yuji Hirono, Akinori Tanaka, Kenji Fukushima
Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations
Kaiwen Xue, Yuhao Zhou, Shen Nie et al.