NEURIPS 2025 "variational inference" Papers
28 papers found
Act to See, See to Act: Diffusion-Driven Perception-Action Interplay for Adaptive Policies
Jing Wang, Weiting Peng, Jing Tang et al.
Brain-like Variational Inference
Hadi Vafaii, Dekel Galor, Jacob Yates
Deep Taxonomic Networks for Unsupervised Hierarchical Prototype Discovery
Zekun Wang, Ethan Haarer, Tianyi Zhu et al.
HoT-VI: Reparameterizable Variational Inference for Capturing Instance-Level High-Order Correlations
Junxi Xiao, Qinliang Su, Zexin Yuan
Large Language Bayes
Justin Domke
Latent Chain-of-Thought for Visual Reasoning
Guohao Sun, Hang Hua, Jian Wang et al.
Least squares variational inference
Yvann Le Fay, Nicolas Chopin, Simon Barthelmé
Model-Informed Flows for Bayesian Inference
Joohwan Ko, Justin Domke
Multi-View Oriented GPLVM: Expressiveness and Efficiency
Zi Yang, Ying Li, Zhidi Lin et al.
Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference
Kyurae Kim, Yian Ma, Trevor Campbell et al.
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
Mélodie Monod, Alessandro Micheli, Samir Bhatt
Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation
Ting Wei, Biao Mei, Junliang Lyu et al.
Quantifying Uncertainty in the Presence of Distribution Shifts
Yuli Slavutsky, David Blei
Rao-Blackwellised Reparameterisation Gradients
Kevin H. Lam, Thang Bui, George Deligiannidis et al.
SING: SDE Inference via Natural Gradients
Amber Hu, Henry Smith, Scott Linderman
Solving and Learning Partial Differential Equations with Variational Q-Exponential Processes
Guangting Yu, Shiwei Lan
Solving Inverse Problems with FLAIR
Julius Erbach, Dominik Narnhofer, Andreas Dombos et al.
Test Time Scaling for Neural Processes
Hyungi Lee, Moonseok Choi, Hyunsu Kim et al.
Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
Haizhou Shi, Yibin Wang, Ligong Han et al.
Training Robust Graph Neural Networks by Modeling Noise Dependencies
Yeonjun In, Kanghoon Yoon, Sukwon Yun et al.
VaMP: Variational Multi-Modal Prompt Learning for Vision-Language Models
Silin Cheng, Kai Han
Variational Inference with Mixtures of Isotropic Gaussians
Marguerite Petit-Talamon, Marc Lambert, Anna Korba
Variational Polya Tree
Lu Xu, Tsai Hor Chan, Lequan Yu et al.
Variational Regularized Unbalanced Optimal Transport: Single Network, Least Action
Yuhao Sun, Zhenyi Zhang, Zihan Wang et al.
Variational Task Vector Composition
Boyuan Zhang, Yingjun Du, Xiantong Zhen et al.
Variational Uncertainty Decomposition for In-Context Learning
I. Shavindra Jayasekera, Jacob Si, Filippo Valdettaro et al.
VERA: Variational Inference Framework for Jailbreaking Large Language Models
Anamika Lochab, Lu Yan, Patrick Pynadath et al.
VIKING: Deep variational inference with stochastic projections
Samuel Matthiesen, Hrittik Roy, Nicholas Krämer et al.