updated publications and news

Author: andreacini

_data/news.yml

@@ -1,3 +1,5 @@
+- date: 2023/08
+  text: Our paper <a href="https://jmlr.org/papers/v24/22-1154.html">Sparse Graph Learning from Spatiotemporal Time Series</a> (Cini et al.) has been published in <strong>JMLR</strong>!
 - date: 2023/05
   text: 'We uploaded <strong>8 new preprints</strong> about our latest research! Check them out: 
  learning graph structures from data [<a href="https://arxiv.org/abs/2305.19183">1</a>,<a href="https://arxiv.org/abs/2304.05099">2</a>,<a href="https://arxiv.org/abs/2205.13492">3</a>],

_data/publications.yaml

@@ -247,35 +247,20 @@
     the practical value of the test on both synthetic and real-world problems, and
     show how the test can be employed to assess the quality of spatio-temporal forecasting
     models by analyzing the prediction residuals appended to the graphs stream.
-- title: Sparse Graph Learning for Spatiotemporal Time Series
+- title: Sparse Graph Learning from Spatiotemporal Time Series
   links:
-    paper: https://arxiv.org/abs/2205.13492
-  venue: Preprint
-  year: 2022
+    paper: https://jmlr.org/papers/v24/22-1154.html
+  venue: Journal of Machine Learning Research
+  year: 2023
   authors:
   - id:acini
   - id:dzambon
   - id:calippi
   keywords:
     - spatiotemporal graphs
     - graph learning
-  abstract: Outstanding achievements of graph neural networks for spatiotemporal time
-    series prediction show that relational constraints introduce a positive inductive
-    bias into neural forecasting architectures. Often, however, the relational information
-    characterizing the underlying data generating process is unavailable; the practitioner
-    is then left with the problem of inferring from data which relational graph to
-    use in the subsequent processing stages. We propose novel, principled -- yet practical
-    -- probabilistic methods that learn the relational dependencies by modeling distributions
-    over graphs while maximizing, at the same time, end-to-end the forecasting accuracy.
-    Our novel graph learning approach, based on consolidated variance reduction techniques
-    for Monte Carlo score-based gradient estimation, is theoretically grounded and
-    effective. We show that tailoring the gradient estimators to the graph learning
-    problem allows us also for achieving state-of-the-art forecasting performance
-    while controlling, at the same time, both the sparsity of the learned graph and
-    the computational burden. We empirically assess the effectiveness of the proposed
-    method on synthetic and real-world benchmarks, showing that the proposed solution
-    can be used as a stand-alone graph identification procedure as well as a learned
-    component of an end-to-end forecasting architecture.
+    - forecasting
+  abstract: Outstanding achievements of graph neural networks for spatiotemporal time series analysis show that relational constraints introduce an effective inductive bias into neural forecasting architectures. Often, however, the relational information characterizing the underlying data-generating process is unavailable and the practitioner is left with the problem of inferring from data which relational graph to use in the subsequent processing stages. We propose novel, principled - yet practical - probabilistic score-based methods that learn the relational dependencies as distributions over graphs while maximizing end-to-end the performance at task. The proposed graph learning framework is based on consolidated variance reduction techniques for Monte Carlo score-based gradient estimation, is theoretically grounded, and, as we show, effective in practice. In this paper, we focus on the time series forecasting problem and show that, by tailoring the gradient estimators to the graph learning problem, we are able to achieve state-of-the-art performance while controlling the sparsity of the learned graph and the computational scalability. We empirically assess the effectiveness of the proposed method on synthetic and real-world benchmarks, showing that the proposed solution can be used as a stand-alone graph identification procedure as well as a graph learning component of an end-to-end forecasting architecture.
 - title: Deep learning for graphs (special session at ESANN 2022)
   links:
     paper: https://doi.org/10.14428/esann/2022.ES2022-7