ICML 2026 papers

Author: allemanenti

_data/news.yml

@@ -1,3 +1,10 @@
+- date: 2026/05
+  text: >
+    Three papers accepted at <strong><a href="https://icml.cc/Conferences/2026">ICML 2026</a></strong>:
+    <a href="https://arxiv.org/abs/2512.22702">What Matters in Deep Learning for Time Series Forecasting?</a> (Moretti et al.), 
+    <a href="https://arxiv.org/abs/2509.24728">Beyond Softmax: A Natural Parameterization for Categorical Random Variables</a> (Manenti and Alippi)
+    and 
+    <a href="https://arxiv.org/abs/2602.12703">SWING: Unlocking Implicit Graph Representations for Graph Random Features</a> (Manenti et al).
 - date: 2026/02
   text: >
     Two new preprints out!

_data/publications.yaml

@@ -1,6 +1,6 @@
 ---
 - title: "SWING: Unlocking Implicit Graph Representations for Graph Random Features"
-  venue: Preprint
+  venue: To appear in International Conference on Machine Learning
   year: 2026
   authors:
   - id:amanenti
@@ -23,7 +23,7 @@
   links:
     paper: https://arxiv.org/abs/2602.12703
 - title: "What Matters in Deep Learning for Time Series Forecasting?"
-  venue: preprint
+  venue: To appear in International Conference on Machine Learning
   year: 2025
   authors:
   - V. Moretti
@@ -100,7 +100,7 @@
   links:
     paper: https://arxiv.org/abs/2510.06819
 - title: "Beyond Softmax: A Natural Parameterization for Categorical Random Variables"
-  venue: Preprint
+  venue: To appear in International Conference on Machine Learning
   year: 2025
   authors:
   - id:amanenti