:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Dayta, Dominic B.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2405.05485
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

You Only Accept Samples Once: Fast, Self-Correcting Stochastic Variational Inference
by: Dayta, Dominic B.
Published: (2024)

Semiparametric Latent Topic Modeling on Consumer-Generated Corpora
by: Dayta, Dominic B., et al.
Published: (2021)

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

Dual Online Stein Variational Inference for Control and Dynamics
by: Barcelos, Lucas, et al.
Published: (2021)

On the Convergence of Black-Box Variational Inference
by: Kim, Kyurae, et al.
Published: (2023)

Efficient Mixture Learning in Black-Box Variational Inference
by: Hotti, Alexandra, et al.
Published: (2024)

Pathwise Gradient Variance Reduction with Control Variates in Variational Inference
by: Ng, Kenyon, et al.
Published: (2024)

Provably Scalable Black-Box Variational Inference with Structured Variational Families
by: Ko, Joohwan, et al.
Published: (2024)

Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box
by: Giordano, Ryan, et al.
Published: (2023)

Adaptive Exponential Integration for Stable Gaussian Mixture Black-Box Variational Inference
by: Che, Baojun, et al.
Published: (2026)

A Trust-Region Method for Graphical Stein Variational Inference
by: Pavlovic, Liam, et al.
Published: (2024)

Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?
by: Kim, Kyurae, et al.
Published: (2023)

Dynamic Factor Analysis of Price Movements in the Philippine Stock Exchange
by: Lim, Brian Godwin, et al.
Published: (2025)

Towards Unsupervised Causal Representation Learning via Latent Additive Noise Model Causal Autoencoders
by: Ong, Hans Jarett J., et al.
Published: (2025)

Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference
by: Kim, Kyurae, et al.
Published: (2025)

Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians
by: Huix, Tom, et al.
Published: (2024)

Constrained Gaussian Process Motion Planning via Stein Variational Newton Inference
by: Li, Jiayun, et al.
Published: (2025)

Bayesian Quantification with Black-Box Estimators
by: Ziegler, Albert, et al.
Published: (2023)

Targeted Variance Reduction: Robust Bayesian Optimization of Black-Box Simulators with Noise Parameters
by: Miller, John Joshua, et al.
Published: (2024)

Black Box Causal Inference: Effect Estimation via Meta Prediction
by: Bynum, Lucius E. J., et al.
Published: (2025)

Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes
by: Xu, Jian, et al.
Published: (2023)

Optimal Vector Compressed Sensing Using James Stein Shrinkage
by: Dey, Apratim, et al.
Published: (2025)

Constrained Stein Variational Trajectory Optimization
by: Power, Thomas, et al.
Published: (2023)

A Framework for Controllable Multi-objective Learning with Annealed Stein Variational Hypernetworks
by: Nguyen, Minh-Duc, et al.
Published: (2025)

Stein Variational Newton Neural Network Ensembles
by: Flöge, Klemens, et al.
Published: (2024)

Stein-Encoder: A White-Box Supervised Encoder via Stein Identities in Multi-Modal Studies
by: Zhang, Jiarui, et al.
Published: (2026)

Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent
by: Della Libera, Luca, et al.
Published: (2024)

Stein Variational Evolution Strategies
by: Braun, Cornelius V., et al.
Published: (2024)

SGD for Variational Inference: Tackling Unbounded Variance via Preconditioning and Dynamic Batching
by: Labarrière, Hippolyte, et al.
Published: (2026)

Adaptive Kernel Selection for Stein Variational Gradient Descent
by: Melcher, Moritz, et al.
Published: (2025)

Gradient Estimation with Discrete Stein Operators
by: Shi, Jiaxin, et al.
Published: (2022)

Zero-Variance Gradients for Variational Autoencoders
by: Shao, Zilei, et al.
Published: (2025)

Probabilistic Inference and Learning with Stein's Method
by: Liu, Qiang, et al.
Published: (2026)

Privately Estimating Black-Box Statistics
by: Steinke, Günter F., et al.
Published: (2025)

Support-Proximity Augmented Diffusion Estimation for Offline Black-Box Optimization
by: Yang, Yonghan, et al.
Published: (2026)

Black-Box Forgetting
by: Kuwana, Yusuke, et al.
Published: (2024)

Annealed Stein Variational Gradient Descent for Improved Uncertainty Estimation in Full-Waveform Inversion
by: Corrales, Miguel, et al.
Published: (2024)

Smoothing the Black-Box: Signed-Distance Supervision for Black-Box Model Copying
by: Jiménez, Rubén, et al.
Published: (2026)

Auditing Training Data in Generative Music Models via Black-Box Membership Inference
by: Liu, Yi Chen, et al.
Published: (2026)

Safety Game: Inference-Time Alignment of Black-Box LLMs via Constrained Optimization
by: Nguyen, Tuan, et al.
Published: (2025)