Update Marco Pegoraro's profile and publications (#170)

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Author: Marco-Peg

content/authors/pegoraro/avatar.jpg

@@ -0,0 +1,38 @@
+---
+title: "Paper accepted at AISTATS 2024"
+
+event: ""
+event_url: ""
+
+authors:
+    - pegoraro
+
+summary: ""
+abstract: ""
+
+
+date: '2024-01-20T17:00:00Z'  # REPLACE WITH THE CORRECT DATE HERE 
+
+all_day: true
+
+publishDate: '2024-01-20T17:00:00Z'
+
+tags: []
+
+featured: false
+
+url_code: ''
+url_pdf: ''
+url_slides: ''
+url_video: ''
+slides: ""
+
+# Projects (optional).
+#   Associate this post with one or more of your projects.
+#   Simply enter your project's folder or file name without extension.
+#   E.g. `projects = ["internal-project"]` references `content/project/deep-learning/index.md`.
+#   Otherwise, set `projects = []`.
+projects: []
+---
+
+Our paper ["*Vector Quantile Regression on Manifolds*"](https://gladia.di.uniroma1.it/publication/pegoraro-2023-mvqr/) was accepted to **AISTATS 2024** main conference!

content/news/2024-01-20/index.md

@@ -19,7 +19,7 @@ date: '2023-07-03'
 lastmod: 2023-02-05T10:57:53+01:00
 featured: false
 draft: false
-publication_short: "Preprint"
+publication_short: "NeuReps Workshop 2023"
 
 # Featured image
 # To use, add an image named `featured.jpg/png` to your page's folder.

content/publication/pegoraro-2023-GEP/index.md

@@ -15,11 +15,11 @@ tags:
 - 'Machine Learning'
 - 'Probability and Statistics'
 categories: []
-date: '2023-07-03'
-lastmod: 2023-02-05T10:57:53+01:00
+date: '2024-01-20'
+lastmod: 2024-01-20T10:57:53+01:00
 featured: false
 draft: false
-publication_short: "frontiers4lcd"
+publication_short: "AISTATS 2024"
 
 # Featured image
 # To use, add an image named `featured.jpg/png` to your page's folder.
@@ -35,7 +35,7 @@ image:
 #   E.g. `projects = ["internal-project"]` references `content/project/deep-learning/index.md`.
 #   Otherwise, set `projects = []`.
 projects: []
-publishDate: '2023-02-05T09:57:52.951304Z'
+publishDate: '2024-01-20T09:57:52.951304Z'
 abstract: Quantile regression (QR) is a statistical tool for distribution-free estimation of conditional quantiles of a target variable given explanatory features.
   QR is limited by the assumption that the target distribution is univariate and defined on an Euclidean domain.
   Although the notion of quantiles was recently extended to multi-variate distributions,
@@ -44,7 +44,7 @@ abstract: Quantile regression (QR) is a statistical tool for distribution-free e
   By leveraging optimal transport theory and the notion of $c$-concave functions, we meaningfully define conditional vector quantile functions of high-dimensional variables on manifolds (M-CVQFs).
   Our approach allows for quantile estimation, regression, and computation of conditional confidence sets.
   We demonstrate the approach's efficacy and provide insights regarding the meaning of non-Euclidean quantiles through preliminary synthetic data experiments.
-publication: '*Workshop on New Frontiers in Learning, Control, and Dynamical Systems at the International Conference on Machine Learning (2023)*'
+publication: '*AISTATS 2024*'
 links:
 - name: arXiv
   url: https://arxiv.org/abs/2307.01037

content/publication/pegoraro-2023-MVQR/featured.png

@@ -18,10 +18,10 @@ tags:
 - 'Subgraphs'
 categories: []
 date: '2023-01-01'
-lastmod: 2023-02-05T10:57:52+01:00
+lastmod: 2023-12-16T10:57:52+01:00
 featured: false
 draft: false
-publication_short: "Preprint"
+publication_short: "NeuReps"
 
 # Featured image
 # To use, add an image named `featured.jpg/png` to your page's folder.
@@ -55,7 +55,7 @@ abstract: In graph learning, maps between graphs and their subgraphs frequently
   Our approach bears practical benefits in knowledge distillation and hierarchical
   learning, where we show comparable or improved performance at a fraction of the
   computational cost.
-publication: '*arXiv preprint arXiv:2108.02161*'
+publication: '*NeuReps Workshop 2023*'
 links:
 - name: URL
   url: https://arxiv.org/pdf/2205.14938.pdf