# Streaming Inference for Infinite Non-Stationary Clustering

arXiv: https://arxiv.org/abs/2205.01212 · HTML: https://mlsanity.com/paper/2205.01212/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 2 May 2022.

## Builds on (most cited first, 10 of 10)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning](https://mlsanity.com/paper/2001.00689/) (2020)
- [Hyperbolic Discounting and Learning over Multiple Horizons](https://mlsanity.com/paper/1902.06865/) (2019)
- [Streaming Variational Bayes](https://mlsanity.com/paper/1307.6769/) (2013)
- [Advances in Inference and Representation for Simultaneous Localization and Mapping](https://mlsanity.com/paper/2103.05041/) (2021)
- [Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data](https://mlsanity.com/paper/2202.13312/) (2022)
- [Streaming, Distributed Variational Inference for Bayesian Nonparametrics](https://mlsanity.com/paper/1510.09161/) (2015)
- [Streaming Variational Inference for Bayesian Nonparametric Mixture Models](https://mlsanity.com/paper/1412.0694/) (2014)

## Built on by (most cited first, 1 of 1)

- [Bridging Associative Memory and Probabilistic Modeling](https://mlsanity.com/paper/2402.10202/) (2024)

## Abstract (arXiv)

Learning from a continuous stream of non-stationary data in an unsupervised manner is arguably one of the most common and most challenging settings facing intelligent agents. Here, we attack learning under all three conditions (unsupervised, streaming, non-stationary) in the context of clustering, also known as mixture modeling. We introduce a novel clustering algorithm that endows mixture models with the ability to create new clusters online, as demanded by the data, in a probabilistic, time-varying, and principled manner. To achieve this, we first define a novel stochastic process called the Dynamical Chinese Restaurant Process (Dynamical CRP), which is a non-exchangeable distribution over partitions of a set; next, we show that the Dynamical CRP provides a non-stationary prior over cluster assignments and yields an efficient streaming variational inference algorithm. We conclude with experiments showing that the Dynamical CRP can be applied on diverse synthetic and real data with Gaussian and non-Gaussian likelihoods.