Bayesian Nonparametric

Bayesian nonparametric methods offer flexible statistical frameworks for modeling data where the complexity of the underlying structure is unknown, aiming to automatically adapt model complexity to the data. Current research focuses on developing efficient inference algorithms, such as sequential Monte Carlo and variational inference, for various model architectures including Gaussian processes, Dirichlet process mixtures, and Indian buffet processes, often applied to high-dimensional or time-series data. These techniques are proving valuable in diverse fields like robotics, healthcare (e.g., image analysis), and signal processing, enabling improved accuracy, uncertainty quantification, and model interpretability in complex data analysis tasks.

Papers

October 30, 2024

Very fast Bayesian Additive Regression Trees on GPU
Giacomo Petrillo
Decision Tree Single GPU Regression Model XGBoost Model Bayesian Nonparametric Bayesian Additive Regression Tree

October 7, 2024

Online scalable Gaussian processes with conformal prediction for guaranteed coverage
Jinwen Xu, Qin Lu, Georgios B. Giannakis
Gaussian Process Uncertainty Quantification Conformal Prediction Higher Coverage Rate Bayesian Nonparametric

August 15, 2024

Incremental Structure Discovery of Classification via Sequential Monte Carlo
Changze Huang, Di Wang
Classification Code Time Series Gaussian Process Prior Knowledge Sequential Monte Carlo Bayesian Nonparametric Structure Discovery

August 1, 2024

The Harmonic Exponential Filter for Nonparametric Estimation on Motion Groups
Miguel Saavedra-Ruiz, Steven A. Parkison, Ria Arora, James Richard Forbes, Liam Paull
Non Linear Bayesian Nonparametric Multimodal Distribution Bayesian Estimation State Estimation Problem

July 16, 2024

cDP-MIL: Robust Multiple Instance Learning via Cascaded Dirichlet Process
Yihang Chen, Tsai Hor Chan, Guosheng Yin, Yuming Jiang, Lequan Yu
Multiple Instance Learning Bayesian Nonparametric Dirichlet Process Instance Based Learning Slide Representation

March 29, 2024

Bayesian Nonparametrics: An Alternative to Deep Learning
Bahman Moraffah
Deep Learning Multi Object Tracking Bayesian Nonparametric

February 14, 2024

Mixed-Output Gaussian Process Latent Variable Models
James Odgers, Chrysoula Kappatou, Ruth Misener, Sarah Filippi
Latent Variable Clean Speech Bayesian Nonparametric Gaussian Process Latent Variable Model

January 28, 2024

Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization
Nicola Bariletto, Nhat Ho
Empirical Risk Minimization Robust Optimization Distributionally Robust Optimization Bayesian Nonparametric Distributional Uncertainty Data Driven Risk

January 26, 2024

A Nonparametric Bayes Approach to Online Activity Prediction
Mario Beraha, Lorenzo Masoero, Stefano Favaro, Thomas S. Richardson
Temporal Trajectory User Engagement Bayesian Nonparametric Online Experiment Activity Prediction

December 18, 2023

Distributed Collapsed Gibbs Sampler for Dirichlet Process Mixture Models in Federated Learning
Reda Khoufache, Mustapha Lebbah, Hanene Azzag, Etienne Goffinet, Djamel Bouchaffra
Markov Chain Monte Carlo Bayesian Nonparametric Gibbs Sampler Dirichlet Process Mixture Model

November 24, 2023

A Metalearned Neural Circuit for Nonparametric Bayesian Inference
Jake C. Snell, Gianluca Bencomo, Thomas L. Griffiths
Inductive Bias Neural Circuit Bayesian Nonparametric

September 25, 2023

Characterising User Transfer Amid Industrial Resource Variation: A Bayesian Nonparametric Approach
Dongxu Lei, Xiaotian Lin, Xinghu Yu, Zhan Li, Weichao Sun, Jianbin Qiu, Songlin Zhuang, Huijun Gao
Bayesian Nonparametric User Clustering User Trajectory Uncertainty Cluster

August 27, 2023

A Bayesian Non-parametric Approach to Generative Models: Integrating Variational Autoencoder and Generative Adversarial Networks using Wasserstein and Maximum Mean Discrepancy
Forough Fazeli-Asl, Michael Minyi Zhang
Generative Model Generative Adversarial Network Variational Autoencoder Variational Autoencoders Maximum Mean Discrepancy Bayesian Nonparametric Robust Generative

July 13, 2023

Sequential Monte Carlo Learning for Time Series Structure Discovery
Feras A. Saad, Brian J. Patton, Matthew D. Hoffman, Rif A. Saurous, Vikash K. Mansinghka
Time Series Gaussian Process Posterior Approximation Sequential Monte Carlo Bayesian Nonparametric

May 23, 2023

DF2M: An Explainable Deep Bayesian Nonparametric Model for High-Dimensional Functional Time Series
Yirui Liu, Xinghao Qiao, Yulong Pei, Liying Wang
Deep Learning Model High Dimensional Time Series Factor Model Bayesian Nonparametric Deep Kernel

March 17, 2023

Robust probabilistic inference via a constrained transport metric
Abhisek Chakraborty, Anirban Bhattacharya, Debdeep Pati
Bayesian Nonparametric Robust Bayesian Transport Metric

November 15, 2022

Bayesian Federated Neural Matching that Completes Full Information
Peng Xiao, Samuel Cheng
Full Information Kullback Leibler Divergence Bayesian Nonparametric Permutation Invariance Federated Ensemble

October 20, 2022

Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning
Zeel B Patel, Nipun Batra, Kevin Murphy
Active Learning Gaussian Process Epistemic Uncertainty Bayesian Nonparametric Uncertainty Disentanglement

October 13, 2022

Dirichlet process mixture models for non-stationary data streams
Ioar Casado, Aritz Pérez
Variational Inference Non Stationary Bayesian Nonparametric Inference Algorithm Streaming Inference Dirichlet Process Mixture Model

June 16, 2022

The convergent Indian buffet process
Ilsang Ohn
Latent Representation Latent Feature Bayesian Nonparametric Menu Design