<html><head><meta http-equiv="content-type" content="text/html; charset=utf-8"></head><body dir="auto"><div dir="ltr"></div><div dir="ltr">Reminder this is happening today!</div><div dir="ltr"><br><blockquote type="cite">On Apr 10, 2023, at 7:57 AM, Asher Trockman <ashert@cs.cmu.edu> wrote:<br><br></blockquote></div><blockquote type="cite"><div dir="ltr"><div dir="ltr">Dear all,<div><br></div><div><div>We look forward to seeing you<b> </b><b>this Tuesday (4/11)</b> from <b><font color="#ff0000">1</font></b><font color="#ff0000"><b>2:00-1:00 PM (U.S. Eastern time)</b></font> for the next talk of this semester's <b>CMU AI Seminar</b>, sponsored by <a href="https://sambanova.ai/" target="_blank">SambaNova Systems</a>. The seminar will be held in GHC 6115 <b>with pizza provided </b>and will<b> </b>be streamed on Zoom.</div><div><br></div><div>To learn more about the seminar series or to see the future schedule, please visit the <a href="http://www.cs.cmu.edu/~aiseminar/" target="_blank">seminar website</a>.</div><div><br></div><font color="#0b5394"><span style="background-color:rgb(255,255,0)">This Tuesday (4/11), <u>Lucio Dery</u></span><b style="background-color:rgb(255,255,0)"> </b><span style="background-color:rgb(255,255,0)">(CMU) will be giving a talk titled </span><b style="background-color:rgb(255,255,0)">"</b><b style="background-color:rgb(255,255,0)">An automated transfer learning approach to tackling learning under limited data</b><b style="background-color:rgb(255,255,0)">".</b></font></div><div><font color="#0b5394"><span style="background-color:rgb(255,255,0)"><br></span><b>Title</b>: An automated transfer learning approach to tackling learning under limited data<br><br></font><div><font color="#0b5394"><b>Talk Abstract</b>: Transfer learning is arguably the engine of the current deep learning revolution in machine learning. A common branch of transfer learning is learning with auxiliary objectives — supplementary learning signals that are introduced to help aid learning on data-starved or highly complex end-tasks. Whilst much work has been done to formulate useful auxiliary objectives, their construction is still an art which proceeds by slow and tedious hand-design. Intuition for how and when these objectives improve end-task performance has also had limited theoretical backing. <br><br>In this talk, I will present a task agnostic approach for automatically generating a suite of auxiliary objectives and maximally utilizing them to benefit a specified end-task. We achieve this by deconstructing existing objectives within a novel unified taxonomy, identifying connections between them, and generating new ones based on the uncovered structure. We theoretically formalize widely-held intuitions about how auxiliary learning improves generalization on the end-task which leads us to a principled and efficient algorithm for searching the space of generated objectives to find those most useful to a specified end-task.</font><div><div><font color="#0b5394"><br></font></div><div><font color="#0b5394"><b style="caret-color: rgb(11, 83, 148);">Speaker Bio:</b><span style="caret-color: rgb(11, 83, 148);"> </span>Lucio Dery is a PhD student in the Computer Science Department at Carnegie Mellon University co-advised by Ameet Talwalkar and Graham Neubig. Before starting his PhD, he was a Research Engineer at Facebook AI Research (FAIR) in Seattle. His current research interests broadly cover all things related to learning from multiple tasks: transfer learning, meta-learning, multi-tasking and auxiliary learning. He primarily explores these fields in the context of Natural Language Processing but the tools he develops are domain agnostic.</font><br></div><div><span style="color:rgb(11,83,148)"><br></span></div><div><font color="#0b5394"><b>In person: </b>GHC 6115</font></div><div><font color="#0b5394"><b>Zoom Link</b>: <a href="https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09" target="_blank">https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09</a></font></div></div></div></div><div><br></div><div>Thanks,</div><div>Asher Trockman</div></div>
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