"variational inference" Papers
71 papers found • Page 1 of 2
Conference
Act to See, See to Act: Diffusion-Driven Perception-Action Interplay for Adaptive Policies
Jing Wang, Weiting Peng, Jing Tang et al.
Approximated Variational Bayesian Inverse Reinforcement Learning for Large Language Model Alignment
Yuang Cai, Yuyu Yuan, Jinsheng Shi et al.
Bayesian Image Regression with Soft-thresholded Conditional Autoregressive Prior
Yuliang Xu, Jian Kang
Brain-like Variational Inference
Hadi Vafaii, Dekel Galor, Jacob Yates
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.
Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors
Wasu Top Piriyakulkij, Yingheng Wang, Volodymyr Kuleshov
Efficient Reinforcement Learning with Large Language Model Priors
Xue Yan, Yan Song, Xidong Feng et al.
Enhancing Uncertainty Estimation and Interpretability with Bayesian Non-negative Decision Layer
XINYUE HU, Zhibin Duan, Bo Chen et al.
FisherTune: Fisher-Guided Robust Tuning of Vision Foundation Models for Domain Generalized Segmentation
Dong Zhao, Jinlong Li, Shuang Wang et al.
HoT-VI: Reparameterizable Variational Inference for Capturing Instance-Level High-Order Correlations
Junxi Xiao, Qinliang Su, Zexin Yuan
Injective flows for star-like manifolds
Marcello Negri, Jonathan Aellen, Volker Roth
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.
Progressive Compression with Universally Quantized Diffusion Models
Yibo Yang, Justus Will, Stephan Mandt
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.
Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics
Sebastian Sanokowski, Wilhelm Berghammer, Haoyu Wang et al.
Seal Your Backdoor with Variational Defense
Ivan Sabolic, Matej Grcic, Siniša Šegvić
SFESS: Score Function Estimators for $k$-Subset Sampling
Klas Wijk, Ricardo Vinuesa, Hossein Azizpour
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.
Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold
Hoang Phuc Hau Luu, Hanlin Yu, Bernardo Williams 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 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 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 Search Distributions
Dan Steinberg, Rafael Oliveira, Cheng Soon Ong 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.
$\mathtt{VITS}$ : 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.