Update papers.bib

_bibliography/papers.bib

@@ -1,6 +1,24 @@
 ---
 ---
 
+@article{SociallyEquitablePUblicModel_ICML_2024,
+  abbr={ICML},
+  title={Building Socially-Equitable Public Models},
+  author={Jianyi Yang and Pengfei Li and Mohammad J. Islam and Shaolei Ren},
+  abstract={Public models have played a crucial role in various AI applications, showcasing their proficiency in accurate predictions. However, their exclusive emphasis on prediction accuracy may not align with the diverse end objectives of downstream agents when utilized. Recognizing the public model's predictions as a service, we advocate for integrating the objectives of downstream agents into the optimization process. Concretely, to address performance disparities and foster fairness among heterogeneous agents in training, we propose a novel q-Equitable objective. This objective, coupled with a policy gradient algorithm, is crafted to train the public model to produce a more equitable/uniform performance distribution across downstream agents, each with their unique concerns. Both theoretical analysis and empirical case studies have proven the effectiveness of our method in advancing performance equity across diverse downstream agents utilizing the public model for their decision-making.},
+  journal={ICML},
+  month = {},
+  year={2024},
+  url={},
+  html={},
+  bibtex_show = {true},
+  selected={true},
+  recenthighlights={true},
+  topic = {ai4sustainability},
+  timerange = {21to25},
+  show = {true}
+}
+
 @article{OnlineAllocation_Replenish_SIGMETRICS_2024,
   abbr={SIGMETRICS},
   title={Online Allocation with Replenishable Budgets: Worst Case and Beyond},
@@ -95,27 +113,6 @@ @article{CAFE_Carbon_FederatedLearning_eEnergy_2024
 }
 
 
-@article{Learning_Equitable_PublicModel_2023,
-  abbr={Preprint},
-  title={Building Socially-Equitable Public Models for Environmental Sustainability},
-  author={Yejia Liu and Pengfei Li and Jianyi Yang and Tongxin Li and Shaolei Ren},
-  abstract={Public models have emerged as crucial components in a wide range of AI
-    applications, offering extensive general knowledge and accurate prediction capabilities. However, relying solely on prediction accuracy may not be optimal
-when dealing with diverse downstream tasks facing specific business objectives. In this study, we incorporate the objective of downstream agents into the
-learning process and focus on the application of environmental sustainability. We introduce a novel socially-equitable objective and propose a decision-oriented policy gradient algorithm to handle non-differentiable
-cost functions.},
-  journal={Preprint},
-  month = {},
-  year={2023},
-  url={},
-  html={},
-  bibtex_show = {false},
-  selected={false},
-  recenthighlights={false},
-  topic = {ai4sustainability},
-  timerange = {21to25},
-  show = {false}
-}
 
 
 @article{Learning_AnytimeConstrainedRL_NeurIPS_2023,