Fwd: [Jmlr-announce] Classification of Time Sequences using Graphs of Temporal Constraints

[Artur Dubrawski]

This is probably the longest-reviewed paper ever submitted by an Autonian!
It feels like we were submitting it sometime a century ago :P

But the ever-elusive GTC paper is finally  out, and I am very proud of 
Mathieu
who persevered through this challenging experience!

Cheers,
Artur

PS If this paper is setting the record for the amount of time spent in 
the editor's hands,
let us keep it this way and avoid challenging it with another submission :)

On 11/16/2017 2:15 PM, Lujie Chen wrote:
> Finally saw it in press!
> ---------- Forwarded message ----------
> From: *Charles Sutton* <production at jmlr.org <mailto:production at jmlr.org>>
> Date: Thu, Nov 16, 2017 at 1:11 PM
> Subject: [Jmlr-announce] Classification of Time Sequences using Graphs 
> of Temporal Constraints
> To: jmlr-announce at csail.mit.edu <mailto:jmlr-announce at csail.mit.edu>
> Cc: production at jmlr.org <mailto:production at jmlr.org>
>
>
> The Journal of Machine Learning Research (www.jmlr.org 
> <http://www.jmlr.org>) is pleased to announce the publication of a new 
> paper:
> ------------------------------------------------------------------------------
> Classification of Time Sequences using Graphs of Temporal Constraints
> Mathieu Guillame-Bert, Artur Dubrawski
> JMLR 18(121):1-34, 2017.
> Link: http://jmlr.org/papers/v18/15-403.html 
> <http://jmlr.org/papers/v18/15-403.html>
>
> Abstract
> We introduce two algorithms that learn to classify Symbolic and Scalar 
> Time Sequences (SSTS); an extension of multivariate time series. An 
> SSTS is a set of \emph{events} and a set of <em>scalars</em>. An 
> <em>event</em> is defined by a symbol and a time-stamp. A 
> <em>scalar</em> is defined by a symbol and a function mapping a number 
> for each possible time stamp of the data. The proposed algorithms rely 
> on temporal patterns called Graph of Temporal Constraints (GTC). A GTC 
> is a directed graph in which vertices express occurrences of specific 
> events, and edges express temporal constraints between occurrences of 
> pairs of events. Additionally, each vertex of a GTC can be augmented 
> with numeric constraints on scalar values. We allow GTCs to be cyclic 
> and/or disconnected. The first of the introduced algorithms extracts 
> sets of co-dependent GTCs to be used in a voting mechanism. The second 
> algorithm builds decision forest like representations where each node 
> is a GTC. In both algorit!
>  hms, extraction of GTCs and model building are interleaved. Both 
> algorithms are closely related to each other and they exhibit 
> complementary properties including complexity, performance, and 
> interpretability. The main novelties of this work reside in direct 
> building of the model and efficient learning of GTC structures. We 
> explain the proposed algorithms and evaluate their performance against 
> a diverse collection of 59 benchmark data sets. In these experiments, 
> our algorithms come across as highly competitive and in most cases 
> closely match or outperform state-of-the-art alternatives in terms of 
> the computational speed while dominating in terms of the accuracy of 
> classification of time sequences.
>
> ------------------------------------------------------------------------------
> This paper and previous papers are available electronically at 
> http://www.jmlr.org in PDF format. The papers of Volumes 1-4 were also 
> published in hardcopy by MIT Press; please see 
> http://mitpress.mit.edu/JMLR for details. Volume 5 and subsequent 
> volumes are being printed in hardcopy by Microtome Publishing. Please 
> see http://www.mtome.com/Publications/JMLR/jmlr.html 
> <http://www.mtome.com/Publications/JMLR/jmlr.html> for details and 
> ordering information.
>
> Charles Sutton
> production at jmlr.org <mailto:production at jmlr.org>
>
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> <https://lists.csail.mit.edu/mailman/listinfo/jmlr-announce>
>
>
>
> -- 
>
> ==================
>
> Karen (Lujie) Chen
>
> Ph.D. Candidate in Information Systems, Heinz College
> PIER (Program of Interdisciplinary Educational Research)
> Member of Auton Lab, Robotics Institute
> Newell-Simon Hall 3124
> Carnegie Mellon University
> Pittsburgh, PA 15213
>
>

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