Change ICLR link to OpenReview

Author: d31003

_data/news.yml

@@ -1,9 +1,9 @@
 - date: 2025-24-01
   details: >-
-    Po-han's and Omama's papers <a href="https://arxiv.org/abs/2410.07610"> CSA: Data-efficient Mapping of Unimodal Features to Multimodal Features </a> and <a href="https://arxiv.org/abs/2410.07022"> Exploiting Distribution Constraints for Scalable and Efficient Image Retrieval </a> were accepted to ICLR 2025!
+    Po-han's and Omama's papers <a href="https://openreview.net/forum?id=6Mg7pjG7Sw"> CSA: Data-efficient Mapping of Unimodal Features to Multimodal Features </a> and <a href="https://openreview.net/forum?id=d0tlL0ZWlu"> Exploiting Distribution Constraints for Scalable and Efficient Image Retrieval </a> were accepted to ICLR 2025!
 - date: 2024-19-12
   details: >-
     Philip's papers <a href="https://arxiv.org/abs/2410.18242"> Human-Agent Coordination in Games under Incomplete Information via Multi-Step Intent </a> was accepted to AAMAS 2025!
 - date: 2024-19-09
   details: >-
-    Philip's and Oguzhan A.'s' papers <a href="https://philipzrh.com/lane/"> Accelerating Visual Sparse-Reward Learning with Latent Nearest-Demonstration-Guided Exploration </a> and <a href="https://openreview.net/forum?id=9dsBQhoqVr"> Fleet Supervisor Allocation: A Submodular Maximization Approach</a> were accepted to CoRL 2024.
+    Philip's and Oguzhan A.'s' papers <a href="https://philipzrh.com/lane/"> Accelerating Visual Sparse-Reward Learning with Latent Nearest-Demonstration-Guided Exploration </a> and <a href="https://openreview.net/forum?id=9dsBQhoqVr"> Fleet Supervisor Allocation: A Submodular Maximization Approach</a> were accepted to CoRL 2024.