Cover Image

Maxitive Donsker-Varadhan Formulation for Possibilistic Variational Inference

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Singh, Jasraj, Wongso, Shelvia, Houssineau, Jeremie, Chérief-Abdellatif, Badr-Eddine
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Finite sample properties of parametric MMD estimation: robustness to misspecification and dependence
by: Chérief-Abdellatif, Badr-Eddine, et al.
Published: (2019)

Bayes meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes
by: Riou, Charles, et al.
Published: (2023)

Possibilistic Predictive Uncertainty for Deep Learning
by: Ni, Yao, et al.
Published: (2026)

Recursive PAC-Bayes: A Frequentist Approach to Sequential Prior Updates with No Information Loss
by: Wu, Yi-Shan, et al.
Published: (2024)

Predictively Oriented Posteriors
by: McLatchie, Yann, et al.
Published: (2025)

On the Asymptotics of Importance Weighted Variational Inference
by: Cherief-Abdellatif, Badr-Eddine, et al.
Published: (2025)

Parametric MMD Estimation with Missing Values: Robustness to Missingness and Data Model Misspecification
by: Chérief-Abdellatif, Badr-Eddine, et al.
Published: (2025)

Asymptotics of Nonparametric Estimation under general non-monotone MAR missingness: A Bayesian Approach
by: Chérief-Abdellatif, Badr-Eddine, et al.
Published: (2026)

Improving Active Learning with a Bayesian Representation of Epistemic Uncertainty
by: Thomas, Jake, et al.
Published: (2024)

Possibilistic Instrumental Variable Regression
by: Steiner, Gregor, et al.
Published: (2025)

Investigating Relational State Abstraction in Collaborative MARL
by: Utke, Sharlin, et al.
Published: (2024)

Evaluation-Time Policy Switching for Offline Reinforcement Learning
by: Neggatu, Natinael Solomon, et al.
Published: (2025)

Action-Free Offline-to-Online RL via Discretised State Policies
by: Neggatu, Natinael Solomon, et al.
Published: (2026)

The Donsker-Varadhan Action Functional for Probabilistic Cellular Automata
by: Eizenberg, Alex
Published: (2025)

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning
by: Zhu, Ting, et al.
Published: (2024)

Large deviations for diffusions: Donsker-Varadhan meet Freidlin-Wentzell
by: Bertini, Lorenzo, et al.
Published: (2022)

Effects of Dropout on Performance in Long-range Graph Learning Tasks
by: Singh, Jasraj, et al.
Published: (2025)

Single-pass Possibilistic Clustering with Damped Window Footprints
by: Dale, Jeffrey, et al.
Published: (2026)

Donsker-Varadhan large deviation principle for locally damped and randomly forced NLS equations
by: Chen, Yuxuan, et al.
Published: (2025)

Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks
by: Wongso, Wilson, et al.
Published: (2025)

UNFIS: A Novel Neuro-Fuzzy Inference System with Unstructured Fuzzy Rules for Classification
by: Salimi-Badr, Armin
Published: (2022)

Variational Formulation of Particle Flow
by: Yi, Yinzhuang, et al.
Published: (2025)

A Context-Contrastive Inference Approach To Partial Diacritization
by: ElNokrashy, Muhammad, et al.
Published: (2024)

Low-Cost Generation and Evaluation of Dictionary Example Sentences
by: Cai, Bill, et al.
Published: (2024)

$Π$-NeSy: A Possibilistic Neuro-Symbolic Approach
by: Baaj, Ismaïl, et al.
Published: (2025)

Variational Autoencoders for Efficient Simulation-Based Inference
by: Nautiyal, Mayank, et al.
Published: (2024)

CCBNet: Confidential Collaborative Bayesian Networks Inference
by: Mălan, Abele, et al.
Published: (2024)

An Active Inference perspective on Neurofeedback Training
by: Annicchiarico, Côme, et al.
Published: (2025)

Variational Diffusion Posterior Sampling with Midpoint Guidance
by: Moufad, Badr, et al.
Published: (2024)

Enhancing Kernel Power K-means: Scalable and Robust Clustering with Random Fourier Features and Possibilistic Method
by: Chen, Yixi, et al.
Published: (2025)

Globally Convergent Variational Inference
by: McNamara, Declan, et al.
Published: (2025)

Censor Dependent Variational Inference
by: Liu, Chuanhui, et al.
Published: (2025)

Variational Inference with Mixtures of Isotropic Gaussians
by: Petit-Talamon, Marguerite, et al.
Published: (2025)

Amortized Variational Inference: When and Why?
by: Margossian, Charles C., et al.
Published: (2023)

Deconfounding Imitation Learning with Variational Inference
by: Vuorio, Risto, et al.
Published: (2022)

Learning with Importance Weighted Variational Inference
by: Daudel, Kamélia, et al.
Published: (2024)

Particle Semi-Implicit Variational Inference
by: Lim, Jen Ning, et al.
Published: (2024)

Particle-based Energetic Variational Inference
by: Wang, Yiwei, et al.
Published: (2020)

Kernel Semi-Implicit Variational Inference
by: Cheng, Ziheng, et al.
Published: (2024)

Stepwise Variational Inference with Vine Copulas
by: Griesbauer, Elisabeth, et al.
Published: (2026)