<div dir="ltr"><div style="font-size:12.8px"><div class="gmail_default" style="font-size:12.8px"><font color="#000000" face="arial, helvetica, sans-serif">When: Wednesday, February 15th at 11:00 am </font></div><div class="gmail_default" style="font-size:12.8px"><font color="#000000" face="arial, helvetica, sans-serif"><br></font></div><div class="gmail_default" style="font-size:12.8px"><font color="#000000" face="arial, helvetica, sans-serif">Where: <span class="gmail-m_-8050178055875231471gmail-m_4588640196162890717m_3136881027827703783gmail-m_-5845083723734738781gmail-m_8936094986121047744gmail-m_-3164973227124251497gmail-m_2953668934074478317gmail-m_-3155518689668024534m_9067904842688472155gmail-m_3071693547520408192gmail-il">TTIC</span>, 6045 S Kenwood Avenue, 5th Floor, Room 526</font></div><div class="gmail_default" style="font-size:12.8px"><font color="#000000" face="arial, helvetica, sans-serif"><br></font></div><div><font face="arial, helvetica, sans-serif"><span style="font-size:12.8px"><font color="#000000">Who: </font></span><font color="#000000"><span style="font-size:12.8px">Zhaoran Wang, Princeton</span></font></font></div></div><div style="font-size:12.8px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="font-size:12.8px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif">Title: Taming Nonconvexity with Data</font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif">Abstract: Nonconvex optimization is becoming one of the most powerful workhorses of data science and artificial intelligence. Compared with convex optimization, it enjoys superior statistical accuracy, computational efficiency, and modeling flexibility in modern scenarios. However, the empirical success of nonconvex optimization largely eludes the reach of classical statistical and optimization theory, which prohibits us from designing more efficient algorithms in a principled manner.</font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif">In this talk, I will illustrate how statistical thinking enables us to harness the power of nonconvex optimization. In particular, I will present an algorithmic framework for exploiting the latent geometry induced by the randomness of data. By integrating three new global exploration meta-algorithms — namely, homotopy continuation, tightening after relaxation, and noise regularization — with local search heuristics such as gradient descent, this unified framework leads to new nonconvex optimization algorithms for a wide variety of challenging learning problems, which enjoy provably optimal statistical accuracy and computational efficiency. Time permitting, I will also discuss the corresponding statistical-computational tradeoffs.</font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif">Bio: Zhaoran is a graduate student at Princeton University, working at the interface of machine learning, statistics, and optimization. He is the recipient of the AISTATS notable paper award, ASA best student paper in statistical learning and data mining, INFORMS best student paper finalist in data mining, and the Microsoft fellowship.</font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif">Host: <a href="mailto:yury@ttic.edu" target="_blank">Yury Makarychev</a></font></div><div style="font-size:12.8px"><br></div></div><div class="gmail_extra"><br clear="all"><div><div class="gmail_signature" data-smartmail="gmail_signature"><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 504</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>
<br><div class="gmail_quote">On Tue, Feb 14, 2017 at 12:43 PM, Mary Marre <span dir="ltr"><<a href="mailto:mmarre@ttic.edu" target="_blank">mmarre@ttic.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div style="font-size:12.8px"><div class="gmail_default" style="font-size:12.8px"><font color="#000000" face="arial, helvetica, sans-serif">When: Wednesday, February 15th at 11:00 am </font></div><div class="gmail_default" style="font-size:12.8px"><font color="#000000" face="arial, helvetica, sans-serif"><br></font></div><div class="gmail_default" style="font-size:12.8px"><font color="#000000" face="arial, helvetica, sans-serif">Where: <span class="m_-8050178055875231471gmail-m_4588640196162890717m_3136881027827703783gmail-m_-5845083723734738781gmail-m_8936094986121047744gmail-m_-3164973227124251497gmail-m_2953668934074478317gmail-m_-3155518689668024534m_9067904842688472155gmail-m_3071693547520408192gmail-il">TTIC</span>, 6045 S Kenwood Avenue, 5th Floor, Room 526</font></div><div class="gmail_default" style="font-size:12.8px"><font color="#000000" face="arial, helvetica, sans-serif"><br></font></div><div><font face="arial, helvetica, sans-serif"><span style="font-size:12.8px"><font color="#000000">Who: </font></span><font color="#000000"><span style="font-size:12.8px">Zhaoran Wang, Princeton</span></font></font></div></div><div style="font-size:12.8px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="font-size:12.8px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif">Title: Taming Nonconvexity with Data</font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif">Abstract: Nonconvex optimization is becoming one of the most powerful workhorses of data science and artificial intelligence. Compared with convex optimization, it enjoys superior statistical accuracy, computational efficiency, and modeling flexibility in modern scenarios. However, the empirical success of nonconvex optimization largely eludes the reach of classical statistical and optimization theory, which prohibits us from designing more efficient algorithms in a principled manner.</font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif">In this talk, I will illustrate how statistical thinking enables us to harness the power of nonconvex optimization. In particular, I will present an algorithmic framework for exploiting the latent geometry induced by the randomness of data. By integrating three new global exploration meta-algorithms — namely, homotopy continuation, tightening after relaxation, and noise regularization — with local search heuristics such as gradient descent, this unified framework leads to new nonconvex optimization algorithms for a wide variety of challenging learning problems, which enjoy provably optimal statistical accuracy and computational efficiency. Time permitting, I will also discuss the corresponding statistical-computational tradeoffs.</font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif">Bio: Zhaoran is a graduate student at Princeton University, working at the interface of machine learning, statistics, and optimization. He is the recipient of the AISTATS notable paper award, ASA best student paper in statistical learning and data mining, INFORMS best student paper finalist in data mining, and the Microsoft fellowship.</font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif">Host: <a href="mailto:yury@ttic.edu" target="_blank">Yury Makarychev</a></font></div><div><br></div><div><br></div><div><br></div><div class="gmail_extra"><br clear="all"><div><div class="m_-8050178055875231471gmail_signature" data-smartmail="gmail_signature"><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 504</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:<a href="tel:(773)%20834-1757" value="+17738341757" target="_blank">(773) 834-1757</a></font></i></div><div><i><font face="arial, helvetica, sans-serif">f: <a href="tel:(773)%20357-6970" value="+17733576970" target="_blank">(773) 357-6970</a></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>
<br><div class="gmail_quote">On Fri, Feb 10, 2017 at 1:15 PM, Mary Marre <span dir="ltr"><<a href="mailto:mmarre@ttic.edu" target="_blank">mmarre@ttic.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div><div style="font-size:12.8px"><font color="#000000" face="arial, helvetica, sans-serif">When: Wednesday, February 15th at 11:00 am </font></div><div style="font-size:12.8px"><font color="#000000" face="arial, helvetica, sans-serif"><br></font></div><div style="font-size:12.8px"><font color="#000000" face="arial, helvetica, sans-serif">Where: <span class="m_-8050178055875231471m_4588640196162890717m_3136881027827703783gmail-m_-5845083723734738781gmail-m_8936094986121047744gmail-m_-3164973227124251497gmail-m_2953668934074478317gmail-m_-3155518689668024534m_9067904842688472155gmail-m_3071693547520408192gmail-il">TTIC</span>, 6045 S Kenwood Avenue, 5th Floor, Room 526</font></div><div style="font-size:12.8px"><font color="#000000" face="arial, helvetica, sans-serif"><br></font></div><div><font face="arial, helvetica, sans-serif"><span style="font-size:12.8px"><font color="#000000">Who: </font></span><font color="#000000"><span style="font-size:12.8px">Zhaoran Wang, Princeton</span></font></font></div></div><div><font face="arial, helvetica, sans-serif"><br></font></div><div><font face="arial, helvetica, sans-serif"><br></font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif">Title: Taming Nonconvexity with Data</font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif">Abstract: Nonconvex optimization is becoming one of the most powerful workhorses of data science and artificial intelligence. Compared with convex optimization, it enjoys superior statistical accuracy, computational efficiency, and modeling flexibility in modern scenarios. However, the empirical success of nonconvex optimization largely eludes the reach of classical statistical and optimization theory, which prohibits us from designing more efficient algorithms in a principled manner.</font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif">In this talk, I will illustrate how statistical thinking enables us to harness the power of nonconvex optimization. In particular, I will present an algorithmic framework for exploiting the latent geometry induced by the randomness of data. By integrating three new global exploration meta-algorithms — namely, homotopy continuation, tightening after relaxation, and noise regularization — with local search heuristics such as gradient descent, this unified framework leads to new nonconvex optimization algorithms for a wide variety of challenging learning problems, which enjoy provably optimal statistical accuracy and computational efficiency. Time permitting, I will also discuss the corresponding statistical-computational tradeoffs.</font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif">Bio: Zhaoran is a graduate student at Princeton University, working at the interface of machine learning, statistics, and optimization. He is the recipient of the AISTATS notable paper award, ASA best student paper in statistical learning and data mining, INFORMS best student paper finalist in data mining, and the Microsoft fellowship.</font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif">Host: <a href="mailto:yury@ttic.edu" target="_blank">Yury Makarychev</a></font></div><div style="color:rgb(0,0,0);font-size:13px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="color:rgb(0,0,0);font-family:helvetica,arial;font-size:13px"><br></div><div style="color:rgb(0,0,0);font-family:helvetica,arial;font-size:13px"><br></div><div><div class="m_-8050178055875231471m_4588640196162890717m_3136881027827703783gmail_signature"><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 504</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:<a href="tel:(773)%20834-1757" value="+17738341757" target="_blank">(773) 834-1757</a></font></i></div><div><i><font face="arial, helvetica, sans-serif">f: <a href="tel:(773)%20357-6970" value="+17733576970" target="_blank">(773) 357-6970</a></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>
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