@@ -752,29 +752,75 @@ <h3> Competitive performance</h3>
752752 < h3 > Usage Examples </ h3 >
753753
754754 < div class ="cards-container ">
755- < a href =" https://arxiv.org/pdf/2406.07658 " class ="example-card ">
756- <!-- < img src="imgs/img_avatar .png" alt="Avatar"> -- >
755+ <!-- <a class="example-card">
756+ <img src="imgs/img_sinusoidal .png" alt="Bimodal, heteroskedastic response" >
757757 <div class="example-container">
758- < h4 > < b > Optimal Inventory Allocation</ b > </ h4 >
758+ <h4>
759+ <b> A simple response with two sinusoidal components </b>
760+ <a href="https://colab.research.google.com/github/blei-lab/treeffuser/blob/tutorials/examples/README_example.ipynb"
761+ style="color: #145c15; font-weight: bold;">[open in Colab]</a>
762+ <a href="https://github.com/blei-lab/treeffuser/blob/tutorials/examples/README_example.ipynb"
763+ style="color: #145c15; font-weight: bold;">[source]</a>
764+ </h4>
759765 <p>
760- We use Treeffuser to model the distribution of demand for products
761- in a retail store. This allows us to compute the optimal inventory
762- allocation that minimizes the risk of stockouts and waste while maximizing
763- profits.
766+ We demonstrate how to train and generate samples with Treeffuser
767+ using a simple example with synthethic data, featuring a multimodal
768+ response with two sinusoidal components.
764769 </p>
770+ </div>
771+ </a> -->
765772
773+ < a class ="example-card ">
774+ < img src ="imgs/img_sinusoidal.png " alt ="Bimodal, heteroskedastic response ">
775+ < div class ="example-container ">
776+ < div class ="title-with-links ">
777+ < h4 >
778+ < b > A simple response with two sinusoidal components </ b >
779+ </ h4 >
780+ < div class ="links ">
781+ < a href ="https://colab.research.google.com/github/blei-lab/treeffuser/blob/tutorials/examples/README_example.ipynb "
782+ style ="color: #145c15; font-weight: bold; "> [open in Colab]</ a >
783+ < a href ="https://github.com/blei-lab/treeffuser/blob/tutorials/examples/README_example.ipynb "
784+ style ="color: #145c15; font-weight: bold; "> [source]</ a >
785+ </ div >
786+ </ div >
787+ < p >
788+ We demonstrate how to train and generate samples with Treeffuser
789+ using a simple example with synthetic data, featuring a multimodal
790+ response with two sinusoidal components.
791+ </ p >
766792 </ div >
767793 </ a >
768- < a href ="https://arxiv.org/pdf/2406.07658 " class ="example-card ">
769- <!-- <img src="imgs/img_avatar.png" alt="Avatar"> -->
794+
795+ <!-- <a href="https://arxiv.org/pdf/2406.07658" class="example-card">
796+ <img src="imgs/img_avatar.png" alt="Avatar">
770797 <div class="example-container">
771798 <h4><b> Synthetic Data </b></h4>
772799 <p>
773800 We demonstrate the effectiveness and flexibility of Treeffuser
774- of treeffuser on Synthetic data with multivariate output,
801+ on Synthetic data with multivariate output,
775802 complex varying multimodality, heavy tails and skewness.
776803 </p>
777804 </div>
805+ </a> -->
806+
807+ < a href ="https://arxiv.org/pdf/2406.07658 " class ="example-card ">
808+ <!-- <img src="imgs/img_avatar.png" alt="Avatar"> -->
809+ < div class ="example-container ">
810+ < h4 >
811+ < b > Optimal Inventory Allocation </ b >
812+ <!-- <a href="https://colab.research.google.com/github/blei-lab/treeffuser/blob/tutorials/examples/README_example.ipynb"
813+ style="color: #145c15; font-weight: bold;">[open in Colab]</a>
814+ <a href="https://github.com/blei-lab/treeffuser/blob/tutorials/examples/README_example.ipynb"
815+ style="color: #145c15; font-weight: bold;">[source]</a> -->
816+ </ h4 >
817+ We use Treeffuser to model the distribution of demand for products
818+ in a retail store. This allows us to compute the optimal inventory
819+ allocation that minimizes the risk of stockouts and waste while maximizing
820+ profits.
821+ </ p >
822+
823+ </ div >
778824 </ a >
779825
780826 <!--
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