<div dir="ltr"><div dir="ltr"><div class="gmail_default" style="font-size:small"><div><div dir="ltr"><div class="gmail_default"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>When:</b> </font></font><font style="vertical-align:inherit"><font style="vertical-align:inherit"> Monday, March 1st at<b> 11:10 am CT</b></font></font><br></font></div></div><div dir="ltr"><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"> </font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>Where:</b> </font></font><font color="#000000">Zoom Virtual Talk (</font><b><font color="#0000ff"><a href="https://uchicagogroup.zoom.us/webinar/register/WN_xtwk0PfYR963X1A2ewhnQw" target="_blank">register in advance here</a></font></b><font color="#000000">)</font></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"> </font></p></div><div class="gmail_default"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>Who: </b> </font></font></font>Dylan Foster, MIT</div></div><div><br></div><div><b><br></b></div><div><b>Title:</b> Bridging Learning and Decision Making<br><br><b>Abstract: </b>Machine learning is becoming widely used in decision making, in domains ranging from personalized medicine and mobile health to online education and recommendation systems. While (supervised) machine learning traditionally excels at prediction problems, decision making requires answering questions that are counterfactual in nature, and ignoring this mismatch leads to unreliable decisions. As a consequence, our understanding of the algorithmic foundations for data-driven decision making is limited, and efficient algorithms are typically developed on an ad hoc basis. Can we bridge this gap and make decision making as easy as machine learning?<br><br>Focusing on the contextual bandit, a core problem in data-driven decision making, we bridge the gap by providing the first optimal and efficient reduction to supervised machine learning. The algorithm allows users to seamlessly apply off-the-shelf supervised learning models and methods to make decisions on the fly, and has been implemented in widely-used, industry-standard tools for decision making.<br><br>Our results advance a broader program to develop a universal algorithm design paradigm for data-driven decision making. I will close the talk by discussing challenges and opportunities in building such a framework, including efforts to extend our developments to difficult reinforcement learning problems in large state spaces.<br><br><b>Bio: </b>Dylan Foster is a postdoctoral fellow at the MIT Institute for Foundations of Data Science. He holds a PhD in computer science from Cornell University, where he was advised by Karthik Sridharan. He has received several awards, including the best paper award at COLT (2019), best student paper award at COLT (2018, 2019), Facebook PhD fellowship, and NDSEG PhD fellowship.<br><br>His research focuses on problems at the intersection of learning and decision making.<br></div><div><br></div><div><br></div><div><b>Host:</b> <a href="mailto:avrim@ttic.edu" target="_blank">Avrim Blum</a></div><div><br></div><div><br></div></div><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><font face="arial, helvetica, sans-serif">Mary C. Marre</font><div><font face="arial, helvetica, sans-serif">Faculty Administrative Support</font></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6"><b>Toyota Technological Institute</b></font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">6045 S. Kenwood Avenue</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Room 517</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL 60637</font></i></div><div><i><font face="arial, helvetica, sans-serif">p:(773) 834-1757</font></i></div><div><i><font face="arial, helvetica, sans-serif">f: (773) 357-6970</font></i></div><div><b><i><a href="mailto:mmarre@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif">mmarre@ttic.edu</font></a></i></b></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Mon, Feb 22, 2021 at 8:03 PM Mary Marre <<a href="mailto:mmarre@ttic.edu">mmarre@ttic.edu</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div style="font-size:small"><div><div dir="ltr"><div><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>When:</b> </font></font><font style="vertical-align:inherit"><font style="vertical-align:inherit"> Monday, March 1st at<b> 11:10 am CT</b></font></font><br></font></div></div><div dir="ltr"><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"> </font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>Where:</b> </font></font><font color="#000000">Zoom Virtual Talk (</font><b><font color="#0000ff"><a href="https://uchicagogroup.zoom.us/webinar/register/WN_xtwk0PfYR963X1A2ewhnQw" target="_blank">register in advance here</a></font></b><font color="#000000">)</font></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"> </font></p></div><div><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>Who: </b> </font></font></font>Dylan Foster, MIT</div></div><div><br></div><div><b><br></b></div><div><b>Title:</b> Bridging Learning and Decision Making<br><br><b>Abstract: </b>Machine learning is becoming widely used in decision making, in domains ranging from personalized medicine and mobile health to online education and recommendation systems. While (supervised) machine learning traditionally excels at prediction problems, decision making requires answering questions that are counterfactual in nature, and ignoring this mismatch leads to unreliable decisions. As a consequence, our understanding of the algorithmic foundations for data-driven decision making is limited, and efficient algorithms are typically developed on an ad hoc basis. Can we bridge this gap and make decision making as easy as machine learning?<br><br>Focusing on the contextual bandit, a core problem in data-driven decision making, we bridge the gap by providing the first optimal and efficient reduction to supervised machine learning. The algorithm allows users to seamlessly apply off-the-shelf supervised learning models and methods to make decisions on the fly, and has been implemented in widely-used, industry-standard tools for decision making.<br><br>Our results advance a broader program to develop a universal algorithm design paradigm for data-driven decision making. I will close the talk by discussing challenges and opportunities in building such a framework, including efforts to extend our developments to difficult reinforcement learning problems in large state spaces.<br><br><b>Bio: </b>Dylan Foster is a postdoctoral fellow at the MIT Institute for Foundations of Data Science. He holds a PhD in computer science from Cornell University, where he was advised by Karthik Sridharan. He has received several awards, including the best paper award at COLT (2019), best student paper award at COLT (2018, 2019), Facebook PhD fellowship, and NDSEG PhD fellowship.<br><br>His research focuses on problems at the intersection of learning and decision making.<br></div><div><br></div><div><br></div><div><b>Host:</b> <a href="mailto:avrim@ttic.edu" target="_blank">Avrim Blum</a></div><div><br></div><div><br></div><div><br></div></div><div><div dir="ltr"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><font face="arial, helvetica, sans-serif">Mary C. Marre</font><div><font face="arial, helvetica, sans-serif">Faculty Administrative Support</font></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6"><b>Toyota Technological Institute</b></font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">6045 S. Kenwood Avenue</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Room 517</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL 60637</font></i></div><div><i><font face="arial, helvetica, sans-serif">p:(773) 834-1757</font></i></div><div><i><font face="arial, helvetica, sans-serif">f: (773) 357-6970</font></i></div><div><b><i><a href="mailto:mmarre@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif">mmarre@ttic.edu</font></a></i></b></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>
</blockquote></div></div>