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--- trans_learn:reading_group_2023 [2023/10/23 10:40] – wuyr
+++ trans_learn:reading_group_2023 [2023/10/23 10:47] (current) – wuyr
@@ Line 2: / Line 2: @@
 A biweekly reading group to discuss recent or classical papers on transfer learning methodologies and their applications on medical tasks. Other interesting theory topics are also welcomed.
-**Time:  Wednesday 8:30 p.m. (GMT+8)**
+**Time:  Monday 10:30 a.m. (GMT+8)**
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@@ Line 68: / Line 68: @@
   * Presenter 1: Xiangyu Chen
   * Paper: Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure (Sato, ICML 2023)
-  * Slides: {{ :trans_learn: GRAPH_Recove.pptx |}}
+  * Slides: {{ :trans_learn: graph_recover.pptx |}}
   * Presenter 2: Yanru Wu
@@ Line 76: / Line 76: @@
 ===10/11/2023===
   * Presenter 1: Hanbing Liu
-  * Paper: Active Gradual Domain Adaptation: Dataset
+  * Paper: Active Gradual Domain Adaptation: Dataset and Approach (Zhou, IEEE Transactions on Multimedia 2022)
-and Approach (Zhou, IEEE Transactions on Multimedia 2022)
+  * Slides: {{ :trans_learn: o |}}
-  * Slides: {{ :trans_learn: GRAPH_Recove.pptx |}}
   * Presenter 2: Jiahao Lai
   * Paper: Hypergraph Neural Networks (Feng, AAAI 2019)
   * Slides: {{ :trans_learn: hypergnn.pdf |}}
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+===10/23/2023===
+  * Presenter: Haohua Wang
+  * Paper: Fine-Tuning Language Models with Advantage-Induced Policy Alignment (Zhu, arXiv:2306.02231)
+  * Slides: {{ :trans_learn: fine-tuning_language_models_with_advantage-induced_policy_alignment_2_.pptx |}}
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