<div dir="ltr"><div dir="ltr"><div class="gmail_default" style="font-size:small"><div class="gmail_default"><div class="gmail_default"><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;margin:0px"><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"> Thursday, January 9th at 11:00 am</font></font><br></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><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 style="vertical-align:inherit"><font style="vertical-align:inherit">TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526</font></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><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>Who: </b> </font></font></font>Sumegha Garg, Princeton University</p></div><div class="gmail_default"><br></div><div class="gmail_default"><b><br></b></div><div class="gmail_default"><b>Title</b>: Understanding the Limits of Space-Bounded and Fair Learning Algorithms<br></div></div><div class="gmail_default"><div><br></div><div><b>Abstract</b>: Machine learning has been a growing field since the 1960s with a wide variety of applications. With the increasing scale of problems, it has become both practically and philosophically important to study the feasibility of learning under memory constraints. Secondarily, as machine learning algorithms are increasingly being deployed for making decisions on humans, for example, in healthcare, hiring, loan decisions or policing, fears that these systems may inadvertently discriminate against members of underrepresented populations have grown. This necessitates the development of a theory of algorithmic fairness that studies the feasibility of fair learning and gives theoretical guarantees for limiting the harm caused by automated decision making on humans.</div><div><br></div><div>A recent line of works has focused on the following question: Can one prove strong lower bounds on the number of samples needed for learning under memory constraints? In the first part of the talk, we develop an extractor-based approach to prove memory-sample tradeoffs for a large class of learning problems and extend it to even when the learner is allowed a second pass over the stream of samples.</div><div><br></div><div>In the second part of the talk, we will investigate the role of information in fair prediction algorithms. We prove that at times, the cost associated with requiring fairness should be blamed on a lack of information about important subpopulations, not on the fairness desideratum itself.</div><div><br></div><div><br></div><div>Host: <a href="mailto:yury@ttic.edu" target="_blank">Yury Makarychev</a></div><div><br></div><div><br></div></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"><font face="arial, helvetica, sans-serif">Mary C. Marre</font><div><font face="arial, helvetica, sans-serif">Administrative Assistant</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><br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Wed, Jan 8, 2020 at 3:38 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 dir="ltr"><div style="font-size:small"><div><div><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;margin:0px"><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"> Thursday, January 9th at 11:00 am</font></font><br></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><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 style="vertical-align:inherit"><font style="vertical-align:inherit">TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526</font></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><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>Who: </b> </font></font></font>Sumegha Garg, Princeton University</p></div><div><br></div><div><b><br></b></div><div><b>Title</b>: Understanding the Limits of Space-Bounded and Fair Learning Algorithms<br></div></div><div><div><br></div><div><b>Abstract</b>: Machine learning has been a growing field since the 1960s with a wide variety of applications. With the increasing scale of problems, it has become both practically and philosophically important to study the feasibility of learning under memory constraints. Secondarily, as machine learning algorithms are increasingly being deployed for making decisions on humans, for example, in healthcare, hiring, loan decisions or policing, fears that these systems may inadvertently discriminate against members of underrepresented populations have grown. This necessitates the development of a theory of algorithmic fairness that studies the feasibility of fair learning and gives theoretical guarantees for limiting the harm caused by automated decision making on humans.</div><div><br></div><div>A recent line of works has focused on the following question: Can one prove strong lower bounds on the number of samples needed for learning under memory constraints? In the first part of the talk, we develop an extractor-based approach to prove memory-sample tradeoffs for a large class of learning problems and extend it to even when the learner is allowed a second pass over the stream of samples.</div><div><br></div><div>In the second part of the talk, we will investigate the role of information in fair prediction algorithms. We prove that at times, the cost associated with requiring fairness should be blamed on a lack of information about important subpopulations, not on the fairness desideratum itself.</div><div><br></div><div><br></div><div>Host: <a href="mailto:yury@ttic.edu" target="_blank">Yury Makarychev</a></div><div><br></div></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"><font face="arial, helvetica, sans-serif">Mary C. Marre</font><div><font face="arial, helvetica, sans-serif">Administrative Assistant</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><br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Thu, Jan 2, 2020 at 6:12 PM Mary Marre <<a href="mailto:mmarre@ttic.edu" target="_blank">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><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;margin:0px"><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"> Thursday, January 9th at 11:00 am</font></font><br></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><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 style="vertical-align:inherit"><font style="vertical-align:inherit">TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526</font></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><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>Who: </b> </font></font></font>Sumegha Garg, Princeton University</p></div><div><br></div><div><b><br></b></div><div><b>Title</b>: Understanding the Limits of Space-Bounded and Fair Learning Algorithms<br></div></div><div style="font-size:small"><div><br></div><div><b>Abstract</b>: Machine learning has been a growing field since the 1960s with a wide variety of applications. With the increasing scale of problems, it has become both practically and philosophically important to study the feasibility of learning under memory constraints. Secondarily, as machine learning algorithms are increasingly being deployed for making decisions on humans, for example, in healthcare, hiring, loan decisions or policing, fears that these systems may inadvertently discriminate against members of underrepresented populations have grown. This necessitates the development of a theory of algorithmic fairness that studies the feasibility of fair learning and gives theoretical guarantees for limiting the harm caused by automated decision making on humans.</div><div><br></div><div>A recent line of works has focused on the following question: Can one prove strong lower bounds on the number of samples needed for learning under memory constraints? In the first part of the talk, we develop an extractor-based approach to prove memory-sample tradeoffs for a large class of learning problems and extend it to even when the learner is allowed a second pass over the stream of samples.</div><div><br></div><div>In the second part of the talk, we will investigate the role of information in fair prediction algorithms. We prove that at times, the cost associated with requiring fairness should be blamed on a lack of information about important subpopulations, not on the fairness desideratum itself.</div><div><br></div><div><br></div><div>Host: <a href="mailto:yury@ttic.edu" target="_blank">Yury Makarychev</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"><font face="arial, helvetica, sans-serif">Mary C. Marre</font><div><font face="arial, helvetica, sans-serif">Administrative Assistant</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>
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