Sparse Variational

Sparse variational methods aim to efficiently approximate complex probability distributions by focusing on a small subset of relevant parameters, thereby improving computational tractability and scalability for large datasets. Current research emphasizes developing novel algorithms and architectures, such as sparse inverse Cholesky approximations and variational sparse gating, to enhance the accuracy and speed of inference in Bayesian neural networks and Gaussian processes, particularly for applications involving high-dimensional data like images and time series. These advancements are significant because they enable the application of powerful probabilistic models to previously intractable problems in diverse fields, including machine learning, signal processing, and control systems, leading to improved model accuracy and uncertainty quantification.

Papers

September 27, 2024

Robust Network Learning via Inverse Scale Variational Sparsification
Zhiling Zhou, Zirui Liu, Chengming Xu, Yanwei Fu, Xinwei Sun
Variational Method Adversarial Noise Enhanced Robustness Scale Space Robust Network Sparse Variational

August 1, 2024

Aggregation Models with Optimal Weights for Distributed Gaussian Processes
Haoyuan Chen, Rui Tuo
Gaussian Process Model Aggregation Consistent Aggregation Sparse Variational Optimal Weight Prediction Stability

April 27, 2024

Implicit Generative Prior for Bayesian Neural Networks
Yijia Liu, Xiao Wang
Bayesian Neural Network Empirical Bayes Implicit Generative Sparse Variational Predictive Uncertainty Quantification

April 15, 2024

Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control
Qi Zhang, Lei Wang, Weihua Xu, Hongye Su, Lei Xie
Full Model Predictive Control Thermal Control Fuel Cell Sparse Variational Sparse Learning Method

February 21, 2024

Motion Code: Robust Time series Classification and Forecasting via Sparse Variational Multi-Stochastic Processes Learning
Chandrajit Bajaj, Minh Nguyen
Time Series Time Series Classification State of the Art Forecasting Noisy Data Temporal Shift Sparse Variational Motion Code

February 16, 2024

Training Bayesian Neural Networks with Sparse Subspace Variational Inference
Junbo Li, Zichen Miao, Qiang Qiu, Ruqi Zhang
Bayesian Neural Network Sparse Variational Sparse Subspace Sparse Bayesian

January 15, 2024

Efficient Nonparametric Tensor Decomposition for Binary and Count Data
Zerui Tao, Toshihisa Tanaka, Qibin Zhao
Raw Data Tensor Decomposition Binary Code Tensor Completion Order Tensor Sparse Variational

August 27, 2023

Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes
Talay M Cheema, Carl Edward Rasmussen
Gaussian Process Many Sparse Fourier Feature Variational Approximation Sparse Variational Covariance Function Spatial Modelling

August 24, 2023

Variational Information Pursuit with Large Language and Multimodal Models for Interpretable Predictions
Kwan Ho Ryan Chan, Aditya Chattopadhyay, Benjamin David Haeffele, Rene Vidal
Multimodal Model Large Language Concept Bottleneck Model Interpretable Concept Built in Interpretability Interpretable Prediction Sparse Variational Concept Filtering

February 6, 2023

Variational Information Pursuit for Interpretable Predictions
Aditya Chattopadhyay, Kwan Ho Ryan Chan, Benjamin D. Haeffele, Donald Geman, René Vidal
Generative Model Generative Modeling Interpretable Prediction Information Acquisition Sparse Variational Interpretable Knowledge

January 30, 2023

Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization
Jian Cao, Myeongjong Kang, Felix Jimenez, Huiyan Sang, Florian Schafer, Matthias Katzfuss
Gaussian Process Posterior Approximation Variational Approximation Sparse Variational Kullback Leibler Cholesky Factorization

October 21, 2022

Learning Robust Dynamics through Variational Sparse Gating
Arnav Kumar Jain, Shivakanth Sujit, Shruti Joshi, Vincent Michalski, Danijar Hafner, Samira Ebrahimi-Kahou
Robust Learning Latent Dynamic Sparse High Order Interaction Sparse Variational

July 6, 2022

Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition
Zheng Wang, Shandian Zhe
Tensor Decomposition Tensor Data Sparse Variational Trajectory Regularization

May 7, 2022

Variational Sparse Coding with Learned Thresholding
Kion Fallah, Christopher J. Rozell
Variational Inference Variational Autoencoders Sparse Regression Automatic Thresholding Sparse Variational Sparse Code