<div dir="ltr"><div dir="ltr"><div class="gmail_default" style="font-size:small"><div dir="ltr"><div class="gmail_default"><font style="font-family:arial,sans-serif;vertical-align:inherit"><font style="vertical-align:inherit"><b>When:</b> </font></font><font style="font-family:arial,sans-serif;vertical-align:inherit"><font style="vertical-align:inherit"> Tuesday, February 16th at<b> 11:10 am CT</b></font></font><br></div></div><div dir="ltr"><div><div><div><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><font color="#000000" style="font-family:arial,sans-serif">Zoom Virtual Talk (</font><b style="font-family:arial,sans-serif"><font color="#0000ff"><a href="https://uchicagogroup.zoom.us/webinar/register/WN_6LU_3OPgR4yFdkQ2omffXg" target="_blank">register in advance here</a></font></b><font color="#000000" style="font-family: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></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>Jonathan Warrell, Yale University</p></div><div><br style="color:rgb(80,0,80)"></div></div><div><br></div><div><font face="arial, sans-serif"><b>Title:</b> Interpretability and Higher-order Generalization in Deep Learning: Integrated models of Genomics, Evolution and the Brain<br></font></div><div><font face="arial, sans-serif"><br></font></div><div><font face="arial, sans-serif"><span style="color:rgb(80,0,80)"><b>Abstract:<span class="gmail_default"> </span></b></span>A gap has emerged in many domains between the performance of the most predictive models, which are typically deep neural networks, and models whose parameters are readily interpretable. This gap raises questions concerning which assumptions embedded in deep learning models / training algorithms allow them to generalize so well, what such assumptions correspond to semantically in particular domains, and how we might use such implicit semantics to gain new knowledge about a domain. I will discuss these issues from a PAC-Bayes viewpoint, particularly focusing on how model architectures, incorporation of prior knowledge, and compressibility / complexity control can be motivated by these considerations in the context of genomics and neuroscience. In the process, I will introduce a type-theoretic framework based on probabilistic programming for deriving higher-order generalization bounds. These learn a hierarchy of model complexity measures for individual tasks or groups of tasks, which combine compressibility and domain specific constraints (e.g. from physical models) in order to provide optimal biases during training. I will then outline case studies for how the issues discussed have led me to explore both hand-designed architectures / algorithms for extracting semantics out of interpretable models in particular domains, and <i>de novo</i> variational-based approaches using the higher-order generalization bounds I propose. These case studies include developing integrated models of genetic risk for psychiatric disorders and cognition as part of the NIH PsychENCODE consortium (including genetic, epigenetic, cellular and brain imaging data), detecting positive and negative selection in cancer, and identifying latent evolutionary structure in genomics and cultural domains.</font></div><div><br><font face="arial, sans-serif"><b>Bio: </b>Jonathan Warrell is a postdoctoral associate research scientist in the Computational Biology and Bioinformatics program at Yale University, working with Mark Gerstein. He has published extensively in computational biology, machine learning, computer vision, and theoretical biology and evolution. He is currently a member of several large-scale genomics consortia, including ENCODE, PsychENCODE, and PCAWG (Pan-Cancer Analysis of Whole Genomes), and his work has been featured in the journals Science and Cell, as well as conferences such as CVPR, ECCV and ISMB. Jonathan has held postdoctoral positions in computer vision and machine learning at University College London and Oxford / Oxford Brookes Universities, and computational biology and genomics at University of Cape Town and Yale University. He began his academic career in music theory, and holds a BA in music from Cambridge, an MA and PhD from King's College London in music theory and analysis, and an MSc in computer science from University College London. His current research areas include integrated models of genetic risk in psychiatric genomics, neuroscience and cancer, interpretable machine learning, statistical learning theory, and generalized evolutionary models of gene networks, cancer, and cultural processes.</font></div><div><font face="arial, sans-serif"><br></font></div><div><font face="arial, sans-serif"><b>Host:</b> <a href="mailto:j3xu@ttic.edu" target="_blank">Jinbo Xu</a></font></div><div><br></div><div><br></div><div><br></div></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"><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 15, 2021 at 3:30 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"><font style="font-family:arial,sans-serif;vertical-align:inherit"><font style="vertical-align:inherit"><b>When:</b> </font></font><font style="font-family:arial,sans-serif;vertical-align:inherit"><font style="vertical-align:inherit"> Tuesday, February 16th at<b> 11:10 am CT</b></font></font><br></div></div><div dir="ltr"><div><div style="font-size:small"><div><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><font color="#000000" style="font-family:arial,sans-serif">Zoom Virtual Talk (</font><b style="font-family:arial,sans-serif"><font color="#0000ff"><a href="https://uchicagogroup.zoom.us/webinar/register/WN_6LU_3OPgR4yFdkQ2omffXg" target="_blank">register in advance here</a></font></b><font color="#000000" style="font-family: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></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>Jonathan Warrell, Yale University</p></div><div><br style="color:rgb(80,0,80)"></div></div><div style="font-size:small"><br></div><div style="font-size:small"><font face="arial, sans-serif"><b>Title:</b> Interpretability and Higher-order Generalization in Deep Learning: Integrated models of Genomics, Evolution and the Brain<br></font></div><div style="font-size:small"><font face="arial, sans-serif"><br></font></div><div><font face="arial, sans-serif"><span style="color:rgb(80,0,80)"><b>Abstract:<span class="gmail_default"> </span></b></span>A gap has emerged in many domains between the performance of the most predictive models, which are typically deep neural networks, and models whose parameters are readily interpretable. This gap raises questions concerning which assumptions embedded in deep learning models / training algorithms allow them to generalize so well, what such assumptions correspond to semantically in particular domains, and how we might use such implicit semantics to gain new knowledge about a domain. I will discuss these issues from a PAC-Bayes viewpoint, particularly focusing on how model architectures, incorporation of prior knowledge, and compressibility / complexity control can be motivated by these considerations in the context of genomics and neuroscience. In the process, I will introduce a type-theoretic framework based on probabilistic programming for deriving higher-order generalization bounds. These learn a hierarchy of model complexity measures for individual tasks or groups of tasks, which combine compressibility and domain specific constraints (e.g. from physical models) in order to provide optimal biases during training. I will then outline case studies for how the issues discussed have led me to explore both hand-designed architectures / algorithms for extracting semantics out of interpretable models in particular domains, and <i>de novo</i> variational-based approaches using the higher-order generalization bounds I propose. These case studies include developing integrated models of genetic risk for psychiatric disorders and cognition as part of the NIH PsychENCODE consortium (including genetic, epigenetic, cellular and brain imaging data), detecting positive and negative selection in cancer, and identifying latent evolutionary structure in genomics and cultural domains.</font></div><div style="font-size:small"><span><br></span><font face="arial, sans-serif"><b>Bio: </b>Jonathan Warrell is a postdoctoral associate research scientist in the Computational Biology and Bioinformatics program at Yale University, working with Mark Gerstein. He has published extensively in computational biology, machine learning, computer vision, and theoretical biology and evolution. He is currently a member of several large-scale genomics consortia, including ENCODE, PsychENCODE, and PCAWG (Pan-Cancer Analysis of Whole Genomes), and his work has been featured in the journals Science and Cell, as well as conferences such as CVPR, ECCV and ISMB. Jonathan has held postdoctoral positions in computer vision and machine learning at University College London and Oxford / Oxford Brookes Universities, and computational biology and genomics at University of Cape Town and Yale University. He began his academic career in music theory, and holds a BA in music from Cambridge, an MA and PhD from King's College London in music theory and analysis, and an MSc in computer science from University College London. His current research areas include integrated models of genetic risk in psychiatric genomics, neuroscience and cancer, interpretable machine learning, statistical learning theory, and generalized evolutionary models of gene networks, cancer, and cultural processes.</font></div><div style="font-size:small"><font face="arial, sans-serif"><br></font></div><div style="font-size:small"><font face="arial, sans-serif"><b>Host:</b> <a href="mailto:j3xu@ttic.edu" target="_blank">Jinbo Xu</a></font></div><div style="font-size:small"><br></div><div style="font-size:small"><br></div><div style="font-size:small"><br></div><div style="font-size:small"><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><br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Wed, Feb 10, 2021 at 11:52 AM 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"> Tuesday, February 16th at<b> 11:10 am CT</b></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><font color="#000000" style="font-family:arial,sans-serif">Zoom Virtual Talk (</font><b style="font-family:arial,sans-serif"><font color="#0000ff"><a href="https://uchicagogroup.zoom.us/webinar/register/WN_6LU_3OPgR4yFdkQ2omffXg" target="_blank">register in advance here</a></font></b><font color="#000000" style="font-family: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></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>Jonathan Warrell, Yale University</p></div><div><br style="color:rgb(80,0,80)"></div></div><div style="font-size:small"><br></div><div><font face="arial, sans-serif"><b>Title:</b> Interpretability and Higher-order Generalization in Deep Learning: Integrated models of Genomics, Evolution and the Brain<br></font></div><div><font face="arial, sans-serif"><br></font></div><div><font face="arial, sans-serif"><b>Abstract:</b> A gap has emerged in many domains between the performance of the most predictive models, which are typically deep neural networks, and models whose parameters are readily interpretable. This gap raises questions concerning which assumptions embedded in deep learning models / training algorithms allow them to generalize so well, what such assumptions correspond to semantically in particular domains, and how we might use such implicit semantics to gain new knowledge about a domain. I will discuss these issues from a PAC-Bayes viewpoint, particularly focusing on how model architectures, incorporation of prior knowledge, and compressibility / complexity control can be motivated by these considerations in the context of genomics and neuroscience. In the process, I will introduce a type-theoretic framework based on probabilistic programming for deriving higher-order generalization bounds. These learn a hierarchy of model complexity measures for individual tasks or groups of tasks, which combine compressibility and domain specific constraints (e.g. from physical models) in order to provide optimal biases during training. I will then outline case studies for how the issues discussed have led me to explore both hand-designed architectures / algorithms for extracting semantics out of interpretable models in particular domains, and <i>de novo</i> approaches using the higher-order generalization bounds I propose. These case studies include developing integrated models of genetic risk for psychiatric disorders and cognition as part of the NIH PsychENCODE consortium (including genetic, epigenetic, cellular and brain imaging data), detecting positive and negative selection in cancer, and identifying latent evolutionary structure in genomics and cultural domains.<br><br><b>Bio: </b>Jonathan Warrell is a postdoctoral associate research scientist in the Computational Biology and Bioinformatics program at Yale University, working with Mark Gerstein. He has published extensively in computational biology, machine learning, computer vision, and theoretical biology and evolution. He is currently a member of several large-scale genomics consortia, including ENCODE, PsychENCODE, and PCAWG (Pan-Cancer Analysis of Whole Genomes), and his work has been featured in the journals Science and Cell, as well as conferences such as CVPR, ECCV and ISMB. Jonathan has held postdoctoral positions in computer vision and machine learning at University College London and Oxford / Oxford Brookes Universities, and computational biology and genomics at University of Cape Town and Yale University. He began his academic career in music theory, and holds a BA in music from Cambridge, an MA and PhD from King's College London in music theory and analysis, and an MSc in computer science from University College London. His current research areas include integrated models of genetic risk in psychiatric genomics, neuroscience and cancer, interpretable machine learning, statistical learning theory, and generalized evolutionary models of gene networks, cancer, and cultural processes.</font></div><div><font face="arial, sans-serif"><br></font></div><div><font face="arial, sans-serif"><b>Host:</b> <a href="mailto:j3xu@ttic.edu" target="_blank">Jinbo Xu</a></font></div><div><br></div><div><br></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>
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