Cover Image

Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lin, Wu, Khan, Mohammad Emtiyaz, Schmidt, Mark
Format:	Preprint
Published:	2019
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

DiVeQ: Differentiable Vector Quantization Using the Reparameterization Trick
by: Vali, Mohammad Hassan, et al.
Published: (2025)

Knowledge Adaptation as Posterior Correction
by: Khan, Mohammad Emtiyaz
Published: (2025)

Information Geometry of Variational Bayes
by: Khan, Mohammad Emtiyaz
Published: (2025)

Importance Weighted Variational Inference without the Reparameterization Trick
by: Daudel, Kamélia, et al.
Published: (2026)

A Stein Identity for q-Gaussians with Bounded Support
by: Sklaviadis, Sophia, et al.
Published: (2026)

Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning
by: Lin, Wu, et al.
Published: (2023)

The Bayesian Learning Rule
by: Khan, Mohammad Emtiyaz, et al.
Published: (2021)

End-to-End Training for Back-Translation with Categorical Reparameterization Trick
by: Heo, DongNyeong, et al.
Published: (2022)

Connecting Federated ADMM to Bayes
by: Swaroop, Siddharth, et al.
Published: (2025)

ELBOing Stein: Variational Bayes with Stein Mixture Inference
by: Rønning, Ola, et al.
Published: (2024)

Optimization Guarantees for Square-Root Natural-Gradient Variational Inference
by: Kumar, Navish, et al.
Published: (2025)

Conformal Prediction via Regression-as-Classification
by: Guha, Etash, et al.
Published: (2024)

Federated ADMM from Bayesian Duality
by: Möllenhoff, Thomas, et al.
Published: (2025)

SVRG and Beyond via Posterior Correction
by: Daheim, Nico, et al.
Published: (2025)

Log-Normal Multiplicative Dynamics for Stable Low-Precision Training of Large Networks
by: Nishida, Keigo, et al.
Published: (2025)

Joint Model and Data Sparsification via the Marginal Likelihood
by: Timans, Alexander, et al.
Published: (2026)

RepLoRA: Reparameterizing Low-Rank Adaptation via the Perspective of Mixture of Experts
by: Truong, Tuan, et al.
Published: (2025)

Variational Learning Induces Adaptive Label Smoothing
by: Yang, Sin-Han, et al.
Published: (2025)

The Memory Perturbation Equation: Understanding Model's Sensitivity to Data
by: Nickl, Peter, et al.
Published: (2023)

Exponential Family Attention
by: Wibisono, Kevin Christian, et al.
Published: (2025)

Natural Variational Annealing for Multimodal Optimization
by: LeMinh, Tâm, et al.
Published: (2025)

How to Weight Multitask Finetuning? Fast Previews via Bayesian Model-Merging
by: Maldonado, Hugo Monzón, et al.
Published: (2024)

Compact Memory for Continual Logistic Regression
by: Jung, Yohan, et al.
Published: (2025)

Model Merging by Uncertainty-Based Gradient Matching
by: Daheim, Nico, et al.
Published: (2023)

Categorical Reparameterization with Denoising Diffusion models
by: Gourevitch, Samson, et al.
Published: (2026)

Reparameterization invariance in approximate Bayesian inference
by: Roy, Hrittik, et al.
Published: (2024)

A Reparameterized Discrete Diffusion Model for Text Generation
by: Zheng, Lin, et al.
Published: (2023)

Bridging the Gap Between Target Networks and Functional Regularization
by: Piche, Alexandre, et al.
Published: (2022)

Diffusion Models for Inverse Problems in the Exponential Family
by: Micheli, Alessandro, et al.
Published: (2025)

Reparameterization Proximal Policy Optimization
by: Zhong, Hai, et al.
Published: (2025)

Reparameterization Flow Policy Optimization
by: Zhong, Hai, et al.
Published: (2026)

Improving LoRA with Variational Learning
by: Cong, Bai, et al.
Published: (2025)

MCNC: Manifold-Constrained Reparameterization for Neural Compression
by: Thrash, Chayne, et al.
Published: (2024)

Reparameterization through Coverings and Topological Weight Priors
by: Beketov, Maxim, et al.
Published: (2026)

Extending Kernel Trick to Influence Functions
by: Sun, Zhenhuan, et al.
Published: (2026)

Variational Learning Finds Flatter Solutions at the Edge of Stability
by: Ghosh, Avrajit, et al.
Published: (2025)

Learning Overspecified Gaussian Mixtures Exponentially Fast with the EM Algorithm
by: Assylbekov, Zhenisbek, et al.
Published: (2025)

Learning Intractable Multimodal Policies with Reparameterization and Diversity Regularization
by: Wang, Ziqi, et al.
Published: (2025)

Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization
by: Ye, Zhenzhang, et al.
Published: (2024)

Performative Prediction with Bandit Feedback: Learning through Reparameterization
by: Chen, Yatong, et al.
Published: (2023)