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\newcommand{\hdpflow}{HDP-Flow}


% The \icmltitle you define below is probably too long as a header.
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\icmltitlerunning{Non-parametric hierarchical representation learning for temporal data}



\begin{document}

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\icmltitle{Non-parametric hierarchical representation learning for temporal data}

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\begin{abstract}
We develop HDP-Flow, a Bayesian non-parametric (BNP) sequence model to capture the dynamics of state sequences in real world time series data. HDP-Flow builds on sticky HDP-HMM models, adapting the sticky parameter to be state-specific and introducing Conditional Normalizing Flows to better model the complex dynamic nature of the data.  We derive an efficient stochastic variational inference (VI) algorithm, based on VI for Bayesian non-parametrics and black-box VI. We study HDP-Flow on simulated and real data.  On simulated data, we compare its performance to other Bayesian non-parametric approaches and show that it performs on par on datasets with complex dynamics. When applied to a real-world dataset of human activity, we show that SVI for HDP-Flow scales significantly better then sampling-based inference methods.


\comment{We consider a problem of learning sequences of latent states in time series data, for example, human activity logs or patient's disease trajectory, using non-parametric Bayesian representation learning. Our generative model, called HDP-Flow, builds on HDP-HMM and related models to learn underlying latent states. HDP-Flow models states as a function of time allowing for flexible state lengths even for the same state across individuals, an improvement upon static state distribution as in prior work. We use conditional autoregressive normalizing flows with parameters sampled from a Dirichlet prior to model the distribution of observations in different states as a function of time. The sequence of learned states can be unique to an individual but state characteristics are shared across the full population  allowing for knowledge transfer to the unseen population. We propose a stochastic variational inference approach for efficiency and scalability. We compare the performance of our method to other Bayesian non-parametric approaches on two simulated datasets and show that HDP-Flow performs on par on a datasets with complex dynamics. Further, we show that HDP-Flow performs significantly better on a large real world human activity recognition dataset in terms of both speed and accuracy. }
\end{abstract}


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\appendix
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\section{You \emph{can} have an appendix here.}

You can have as much text here as you want. The main body must be at most $8$ pages long.
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If you want, you can use an appendix like this one, even using the one-column format.
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% This document was modified from the file originally made available by
% Pat Langley and Andrea Danyluk for ICML-2K. This version was created
% by Iain Murray in 2018, and modified by Alexandre Bouchard in
% 2019 and 2021 and by Csaba Szepesvari, Gang Niu and Sivan Sabato in 2022.
% Modified again in 2023 by Sivan Sabato and Jonathan Scarlett.
% Previous contributors include Dan Roy, Lise Getoor and Tobias
% Scheffer, which was slightly modified from the 2010 version by
% Thorsten Joachims & Johannes Fuernkranz, slightly modified from the
% 2009 version by Kiri Wagstaff and Sam Roweis's 2008 version, which is
% slightly modified from Prasad Tadepalli's 2007 version which is a
% lightly changed version of the previous year's version by Andrew
% Moore, which was in turn edited from those of Kristian Kersting and
% Codrina Lauth. Alex Smola contributed to the algorithmic style files.
