Deploying to gh-pages from @ d679b04 🚀

feed.xml

@@ -1,4 +1,4 @@
-<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en"><generator uri="https://jekyllrb.com/" version="4.3.3">Jekyll</generator><link href="https://shaoleiren.github.io/feed.xml" rel="self" type="application/atom+xml"/><link href="https://shaoleiren.github.io/" rel="alternate" type="text/html" hreflang="en"/><updated>2024-03-12T04:31:15+00:00</updated><id>https://shaoleiren.github.io/feed.xml</id><title type="html">blank</title><subtitle></subtitle><entry><title type="html">a post with bibliography</title><link href="https://shaoleiren.github.io/blog/2023/post-bibliography/" rel="alternate" type="text/html" title="a post with bibliography"/><published>2023-07-12T13:56:00+00:00</published><updated>2023-07-12T13:56:00+00:00</updated><id>https://shaoleiren.github.io/blog/2023/post-bibliography</id><content type="html" xml:base="https://shaoleiren.github.io/blog/2023/post-bibliography/"><![CDATA[<p>This post shows how to add bibliography to simple blog posts. If you would like something more academic, check the <a href="/blog/2021/distill/">distill style post</a>.</p>]]></content><author><name></name></author><category term="sample-posts"/><category term="formatting"/><category term="bib"/><summary type="html"><![CDATA[an example of a blog post with bibliography]]></summary></entry><entry><title type="html">a post with jupyter notebook</title><link href="https://shaoleiren.github.io/blog/2023/jupyter-notebook/" rel="alternate" type="text/html" title="a post with jupyter notebook"/><published>2023-07-04T12:57:00+00:00</published><updated>2023-07-04T12:57:00+00:00</updated><id>https://shaoleiren.github.io/blog/2023/jupyter-notebook</id><content type="html" xml:base="https://shaoleiren.github.io/blog/2023/jupyter-notebook/"><![CDATA[<p>To include a jupyter notebook in a post, you can use the following code:</p> <div class="language-html highlighter-rouge"><div class="highlight"><pre class="highlight"><code>{::nomarkdown}
+<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en"><generator uri="https://jekyllrb.com/" version="4.3.3">Jekyll</generator><link href="https://shaoleiren.github.io/feed.xml" rel="self" type="application/atom+xml"/><link href="https://shaoleiren.github.io/" rel="alternate" type="text/html" hreflang="en"/><updated>2024-03-13T06:48:10+00:00</updated><id>https://shaoleiren.github.io/feed.xml</id><title type="html">blank</title><subtitle></subtitle><entry><title type="html">a post with bibliography</title><link href="https://shaoleiren.github.io/blog/2023/post-bibliography/" rel="alternate" type="text/html" title="a post with bibliography"/><published>2023-07-12T13:56:00+00:00</published><updated>2023-07-12T13:56:00+00:00</updated><id>https://shaoleiren.github.io/blog/2023/post-bibliography</id><content type="html" xml:base="https://shaoleiren.github.io/blog/2023/post-bibliography/"><![CDATA[<p>This post shows how to add bibliography to simple blog posts. If you would like something more academic, check the <a href="/blog/2021/distill/">distill style post</a>.</p>]]></content><author><name></name></author><category term="sample-posts"/><category term="formatting"/><category term="bib"/><summary type="html"><![CDATA[an example of a blog post with bibliography]]></summary></entry><entry><title type="html">a post with jupyter notebook</title><link href="https://shaoleiren.github.io/blog/2023/jupyter-notebook/" rel="alternate" type="text/html" title="a post with jupyter notebook"/><published>2023-07-04T12:57:00+00:00</published><updated>2023-07-04T12:57:00+00:00</updated><id>https://shaoleiren.github.io/blog/2023/jupyter-notebook</id><content type="html" xml:base="https://shaoleiren.github.io/blog/2023/jupyter-notebook/"><![CDATA[<p>To include a jupyter notebook in a post, you can use the following code:</p> <div class="language-html highlighter-rouge"><div class="highlight"><pre class="highlight"><code>{::nomarkdown}
 {% assign jupyter_path = "assets/jupyter/blog.ipynb" | relative_url %}
 {% capture notebook_exists %}{% file_exists assets/jupyter/blog.ipynb %}{% endcapture %}
 {% if notebook_exists == "true" %}

index.html

@@ -12,12 +12,12 @@
   <span class="na">month</span> <span class="p">=</span> <span class="s">{}</span><span class="p">,</span>
   <span class="na">year</span> <span class="p">=</span> <span class="s">{2023}</span><span class="p">,</span>
   <span class="na">url</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2304.03271}</span><span class="p">,</span>
-<span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <div class="col-sm-2 abbr"><abbr class="badge">arXiv</abbr></div> <div id="Environmentally_Equitable_AI_arXiv_2023" class="col-sm-10"> <div class="title"><a href="https://arxiv.org/abs/2307.05494" rel="external nofollow noopener" target="_blank">Towards Environmentally Equitable AI via Geographical Load Balancing</a></div> <div class="author"> Pengfei Li, Jianyi Yang, Adam Wierman, and <em>Shaolei Ren</em> </div> <div class="periodical"> <em>arXiv</em>, 2023 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abstract</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/abs/2307.05494" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">HTML</a> </div> <div class="badges"> </div> <div class="abstract hidden"> <p>Fueled by the soaring popularity of large language and foundation models, the accelerated growth of artificial intelligence (AI) models’ enormous environmental footprint has come under increased scrutiny. While many approaches have been proposed to make AI more energy-efficient and environmentally friendly, environmental inequity – the fact that AI’s environmental footprint can be disproportionately higher in certain regions than in others – has emerged, raising social-ecological justice concerns. This paper takes a first step toward addressing AI’s environmental inequity by balancing its regional negative environmental impact. Concretely, we focus on the carbon and water footprints of AI model inference and propose equity-aware geographical load balancing (GLB) to explicitly address AI’s environmental impacts on the most disadvantaged regions. We run trace-based simulations by considering a set of 10 geographically-distributed data centers that serve inference requests for a large language AI model. The results demonstrate that existing GLB approaches may amplify environmental inequity while our proposed equity-aware GLB can significantly reduce the regional disparity in terms of carbon and water footprints.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">Environmentally_Equitable_AI_arXiv_2023</span><span class="p">,</span>
+<span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <div class="col-sm-2 abbr"><abbr class="badge">e-Energy</abbr></div> <div id="Environmentally_Equitable_AI_eEnergy_2024" class="col-sm-10"> <div class="title"><a href="https://arxiv.org/abs/2307.05494" rel="external nofollow noopener" target="_blank">Towards Environmentally Equitable AI via Geographical Load Balancing</a></div> <div class="author"> Pengfei Li, Jianyi Yang, Adam Wierman, and <em>Shaolei Ren</em> </div> <div class="periodical"> <em>e-Energy</em>, 2024 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abstract</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/abs/2307.05494" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">HTML</a> </div> <div class="badges"> </div> <div class="abstract hidden"> <p>Fueled by the soaring popularity of large language and foundation models, the accelerated growth of artificial intelligence (AI) models’ enormous environmental footprint has come under increased scrutiny. While many approaches have been proposed to make AI more energy-efficient and environmentally friendly, environmental inequity – the fact that AI’s environmental footprint can be disproportionately higher in certain regions than in others – has emerged, raising social-ecological justice concerns. This paper takes a first step toward addressing AI’s environmental inequity by balancing its regional negative environmental impact. Concretely, we focus on the carbon and water footprints of AI model inference and propose equity-aware geographical load balancing (GLB) to explicitly address AI’s environmental impacts on the most disadvantaged regions. We run trace-based simulations by considering a set of 10 geographically-distributed data centers that serve inference requests for a large language AI model. The results demonstrate that existing GLB approaches may amplify environmental inequity while our proposed equity-aware GLB can significantly reduce the regional disparity in terms of carbon and water footprints.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">Environmentally_Equitable_AI_eEnergy_2024</span><span class="p">,</span>
   <span class="na">title</span> <span class="p">=</span> <span class="s">{Towards Environmentally Equitable AI via Geographical Load Balancing}</span><span class="p">,</span>
   <span class="na">author</span> <span class="p">=</span> <span class="s">{Li, Pengfei and Yang, Jianyi and Wierman, Adam and Ren, Shaolei}</span><span class="p">,</span>
-  <span class="na">journal</span> <span class="p">=</span> <span class="s">{arXiv}</span><span class="p">,</span>
+  <span class="na">journal</span> <span class="p">=</span> <span class="s">{e-Energy}</span><span class="p">,</span>
   <span class="na">month</span> <span class="p">=</span> <span class="s">{}</span><span class="p">,</span>
-  <span class="na">year</span> <span class="p">=</span> <span class="s">{2023}</span><span class="p">,</span>
+  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span>
   <span class="na">url</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2307.05494}</span><span class="p">,</span>
 <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <div class="col-sm-2 abbr"><abbr class="badge">NeurIPS</abbr></div> <div id="Learning_AnytimeConstrainedRL_NeurIPS_2023" class="col-sm-10"> <div class="title"><a href="https://arxiv.org/abs/2311.01568" rel="external nofollow noopener" target="_blank">Anytime-Competitive Reinforcement Learning with Policy Prior</a></div> <div class="author"> Jianyi Yang, Pengfei Li, Tongxin Li, Adam Wierman, and <em>Shaolei Ren</em> </div> <div class="periodical"> <em>NeurIPS</em>, 2023 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abstract</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/abs/2311.01568" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">HTML</a> </div> <div class="badges"> </div> <div class="abstract hidden"> <p>This paper studies the problem of Anytime-Competitive Markov Decision Process (A-CMDP). Existing works on Constrained Markov Decision Processes (CMDPs) aim to optimize the expected reward while constraining the expected cost over random dynamics, but the cost in a specific episode can still be unsatisfactorily high. In contrast, the goal of A-CMDP is to optimize the expected reward while guaranteeing a bounded cost in each round of any episode against a policy prior. We propose a new algorithm, called Anytime-Competitive Reinforcement Learning (ACRL), which provably guarantees the anytime cost constraints. The regret analysis shows the policy asymptotically matches the optimal reward achievable under anytime constraints. Experiments on the application of carbon-intelligent computing verify the reward performance and cost constraint guarantee of ACRL.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">Learning_AnytimeConstrainedRL_NeurIPS_2023</span><span class="p">,</span>
   <span class="na">title</span> <span class="p">=</span> <span class="s">{Anytime-Competitive Reinforcement Learning with Policy Prior}</span><span class="p">,</span>