"variational inference" Papers
43 papers found
Bayesian Image Regression with Soft-thresholded Conditional Autoregressive Prior
Yuliang Xu, Jian Kang
Bridging the Gap between Variational Inference and Stochastic Gradient MCMC in Function Space
Mengjing Wu, Junyu Xuan, Jie Lu
Deep Taxonomic Networks for Unsupervised Hierarchical Prototype Discovery
Zekun Wang, Ethan Haarer, Tianyi Zhu et al.
FisherTune: Fisher-Guided Robust Tuning of Vision Foundation Models for Domain Generalized Segmentation
Dong Zhao, Jinlong Li, Shuang Wang et al.
Injective flows for star-like manifolds
Marcello Negri, Jonathan Aellen, Volker Roth
Latent Chain-of-Thought for Visual Reasoning
Guohao Sun, Hang Hua, Jian Wang et al.
Model-Informed Flows for Bayesian Inference
Joohwan Ko, Justin Domke
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.
Solving Inverse Problems with FLAIR
Julius Erbach, Dominik Narnhofer, Andreas Dombos et al.
Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold
Hoang Phuc Hau Luu, Hanlin Yu, Bernardo Williams et al.
Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
Haizhou Shi, Yibin Wang, Ligong Han et al.
Variational Bayesian Pseudo-Coreset
Hyungi Lee, Seungyoo Lee, Juho Lee
Variational Best-of-N Alignment
Afra Amini, Tim Vieira, Elliott Ash et al.
Variational Inference with Mixtures of Isotropic Gaussians
Marguerite Petit-Talamon, Marc Lambert, Anna Korba
Variational Regularized Unbalanced Optimal Transport: Single Network, Least Action
Yuhao Sun, Zhenyi Zhang, Zihan Wang et al.
Variational Search Distributions
Dan Steinberg, Rafael Oliveira, Cheng Soon Ong et al.
Variational Task Vector Composition
Boyuan Zhang, Yingjun Du, Xiantong Zhen et al.
$\mathtt{VITS}$ : Variational Inference Thompson Sampling for contextual bandits
Pierre Clavier, Tom Huix, Alain Oliviero Durmus
Accelerating Convergence in Bayesian Few-Shot Classification
Tianjun Ke, Haoqun Cao, Feng Zhou
Adaptive Robust Learning using Latent Bernoulli Variables
Aleksandr Karakulev, Dave Zachariah, Prashant Singh
A Differentiable Partially Observable Generalized Linear Model with Forward-Backward Message Passing
Chengrui Li, Weihan Li, Yule Wang et al.
Amortized Variational Deep Kernel Learning
Alan Matias, César Lincoln Mattos, Joao Paulo Gomes et al.
Bayesian Exploration Networks
Mattie Fellows, Brandon Kaplowitz, Christian Schroeder de Witt et al.
Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling
Denis Blessing, Xiaogang Jia, Johannes Esslinger et al.
Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution
Johannes Zenn, Robert Bamler
Discovering Mixtures of Structural Causal Models from Time Series Data
Sumanth Varambally, Yian Ma, Rose Yu
Improving Adversarial Energy-Based Model via Diffusion Process
Cong Geng, Tian Han, Peng-Tao Jiang et al.
Kernel Semi-Implicit Variational Inference
Ziheng Cheng, Longlin Yu, Tianyu Xie et al.
Online Variational Sequential Monte Carlo
Alessandro Mastrototaro, Jimmy Olsson
On the Convergence of Projected Bures-Wasserstein Gradient Descent under Euclidean Strong Convexity
Junyi FAN, Yuxuan Han, Zijian Liu et al.
PGODE: Towards High-quality System Dynamics Modeling
Xiao Luo, Yiyang Gu, Huiyu Jiang et al.
Reparameterized Importance Sampling for Robust Variational Bayesian Neural Networks
Yunfei Long, Zilin Tian, Liguo Zhang et al.
Sample as you Infer: Predictive Coding with Langevin Dynamics
Umais Zahid, Qinghai Guo, Zafeirios Fountas
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes
Yingyi Chen, Qinghua Tao, Francesco Tonin et al.
Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference
JIAN XU, Delu Zeng, John Paisley
Sparse Variational Student-t Processes
Jian Xu, Delu Zeng
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians
Tom Huix, Anna Korba, Alain Oliviero Durmus et al.
Understanding Stochastic Natural Gradient Variational Inference
Kaiwen Wu, Jacob Gardner
Variational Inference with Coverage Guarantees in Simulation-Based Inference
Yash Patel, Declan McNamara, Jackson Loper et al.
Variational Linearized Laplace Approximation for Bayesian Deep Learning
Luis A. Ortega, Simon Rodriguez Santana, Daniel Hernández-Lobato
Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts
Hyunsu Kim, Ye Gon Kim, Hongseok Yang et al.
Variational Schrödinger Diffusion Models
Wei Deng, Weijian Luo, Yixin Tan et al.