update

_bibliography/papers.bib

@@ -75,7 +75,7 @@ @article{Learning_AnytimeConstrainedRL_NeurIPS_2023
   bibtex_show = {true},
   recenthighlights={true},
   selected={true},
-  topic = {ai4sustainability},
+  topic = {green},
   timerange = {21to25},
   show = {true}
 }
@@ -97,7 +97,7 @@ @article{SOCO_RCL_NeurIPS_2023
   bibtex_show = {true},
   selected={true},
   recenthighlights={true},
-  topic = {ai4sustainability},
+  topic = {green},
   timerange = {21to25},
   show = {true}
 }
@@ -134,7 +134,7 @@ @article{SOCO_ERL_Infocom_2023
   bibtex_show = {true},
   selected={false},
   recenthighlights={false},
-  topic = {ai4sustainability},
+  topic = {green},
   timerange = {21to25},
   show = {true}
 }
@@ -232,7 +232,7 @@ @article{SOCO_ECL2O_Sigmetrics_2022
   bibtex_show = {true},
   selected={true},
   recenthighlights={true},
-  topic = {ai4sustainability},
+  topic = {green},
   timerange = {21to25},
   show = {true}
 }
@@ -278,7 +278,7 @@ @article{DNN_KnowledgeInformed_ICML_2022
 
 @article{Learning_L2O_Robust_INFOCOM_2022,
   abbr={INFOCOM},
-  title={Learning for robust combinatorial optimization: Algorithm and application},
+  title={Learning for Robust Combinatorial Optimization: Algorithm and Application},
   author={Zhihui Shao and Jianyi Yang and Cong Shen and Shaolei Ren},
   abstract={Learning to optimize (L2O) has recently emerged as a promising approach to solving optimization problems by exploiting the strong prediction power of neural networks and offering lower runtime complexity than conventional solvers. While L2O has been applied to various problems, a crucial yet challenging class of problems -- robust combinatorial optimization in the form of minimax optimization -- have largely remained under-explored. In addition to the exponentially large decision space, a key challenge for robust combinatorial optimization lies in the inner optimization problem, which is typically non-convex and entangled with outer optimization. In this paper, we study robust combinatorial optimization and propose a novel learning-based optimizer, called LRCO (Learning for Robust Combinatorial Optimization), which quickly outputs a robust solution in the presence of uncertain context. LRCO leverages a pair of learning-based optimizers — one for the minimizer and the other for the maximizer — that use their respective objective functions as losses and can be trained without the need of labels for training problem instances. To evaluate the performance of LRCO, we perform simulations for the task offloading problem in vehicular edge computing. Our results highlight that LRCO can greatly reduce the worst-case cost and improve robustness, while having a very low runtime complexity.},
   journal={INFOCOM},
@@ -289,7 +289,7 @@ @article{Learning_L2O_Robust_INFOCOM_2022
   bibtex_show = {true},
   selected={false},
   recenthighlights={false},
-  topic = {ai4sustainability},
+  topic = {green},
   timerange = {21to25},
   show = {true}
 }

_layouts/my_publications.html

@@ -20,15 +20,15 @@ <h3 class="card-title font-weight-medium">Selected papers</h3>
             {% bibliography -f {{ site.scholar.bibliography }} -q @*[selected=true && show=true]* %}
           </div>
 
-          <h3 class="card-title font-weight-medium">AI & computing for sustainability</h3>
+          <h3 class="card-title font-weight-medium">Sustainable AI & computing</h3>
           <div class="publications">
-            {% bibliography -f {{ site.scholar.bibliography }} -q @*[topic=ai4sustainability && show=true]* %}
+            {% bibliography -f {{ site.scholar.bibliography }} -q @*[topic=green && show=true]* %}
           </div>
+
 
-
-          <h3 class="card-title font-weight-medium">Sustainable AI & computing</h3>
+          <h3 class="card-title font-weight-medium">AI & computing for sustainability</h3>
           <div class="publications">
-            {% bibliography -f {{ site.scholar.bibliography }} -q @*[topic=green && show=true]* %}
+            {% bibliography -f {{ site.scholar.bibliography }} -q @*[topic=ai4sustainability && show=true]* %}
           </div>
 
           <h3 class="card-title font-weight-medium">Secure AI & computing</h3>