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Recent research in machine learning and statistics has seen the proliferation of computational methods for analyzing networks and learning with graphs. These methods support progress in many application areas, including the social sciences, biology, medicine, neuroscience, physics, finance, and economics.
The primary goal of the workshop is to actively promote a concerted effort to address statistical, methodological and computational issues that arise when modeling and analyzing large collection of data that are largely represented as static and/or dynamic graphs. To this end, we aim at bringing together researchers from applied disciplines such as sociology, economics, medicine and biology, together with researchers from more theoretical disciplines such as mathematics and physics, within our community of statisticians and computer scientists. Different communities use diverse ideas and mathematical tools; our goal is to to foster cross-disciplinary collaborations and intellectual exchange.
Presentations will include novel graph models, the application of established models to new domains, theoretical and computational issues, limitations of current graph methods and directions for future research.
Author Information
Edo M Airoldi (Harvard University)
Jure Leskovec (Stanford University/Pinterest)
Jon Kleinberg (Cornell University)
Josh Tenenbaum (MIT)
Josh Tenenbaum is an Associate Professor of Computational Cognitive Science at MIT in the Department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 1999, and was an Assistant Professor at Stanford University from 1999 to 2002. He studies learning and inference in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing computers closer to human capacities. He focuses on problems of inductive generalization from limited data -- learning concepts and word meanings, inferring causal relations or goals -- and learning abstract knowledge that supports these inductive leaps in the form of probabilistic generative models or 'intuitive theories'. He has also developed several novel machine learning methods inspired by human learning and perception, most notably Isomap, an approach to unsupervised learning of nonlinear manifolds in high-dimensional data. He has been Associate Editor for the journal Cognitive Science, has been active on program committees for the CogSci and NIPS conferences, and has co-organized a number of workshops, tutorials and summer schools in human and machine learning. Several of his papers have received outstanding paper awards or best student paper awards at the IEEE Computer Vision and Pattern Recognition (CVPR), NIPS, and Cognitive Science conferences. He is the recipient of the New Investigator Award from the Society for Mathematical Psychology (2005), the Early Investigator Award from the Society of Experimental Psychologists (2007), and the Distinguished Scientific Award for Early Career Contribution to Psychology (in the area of cognition and human learning) from the American Psychological Association (2008).
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2016 : Datasets, Methodology, and Challenges in Intuitive Physics »
Emmanuel Dupoux · Josh Tenenbaum -
2016 : Josh Tenenbaum »
Josh Tenenbaum -
2016 : Reverse engineering human cooperation (or, How to build machines that treat people like people) »
Josh Tenenbaum · Max Kleiman-Weiner -
2016 : Naive Physics 101: A Tutorial »
Emmanuel Dupoux · Josh Tenenbaum -
2016 : Opening Remarks »
Josh Tenenbaum -
2016 Workshop: Intuitive Physics »
Adam Lerer · Jiajun Wu · Josh Tenenbaum · Emmanuel Dupoux · Rob Fergus -
2016 Poster: Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation »
Tejas Kulkarni · Karthik Narasimhan · Ardavan Saeedi · Josh Tenenbaum -
2016 Poster: Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making »
Himabindu Lakkaraju · Jure Leskovec -
2016 Poster: Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling »
Jiajun Wu · Chengkai Zhang · Tianfan Xue · Bill Freeman · Josh Tenenbaum -
2016 Poster: Sampling for Bayesian Program Learning »
Kevin Ellis · Armando Solar-Lezama · Josh Tenenbaum -
2016 Poster: Probing the Compositionality of Intuitive Functions »
Eric Schulz · Josh Tenenbaum · David Duvenaud · Maarten Speekenbrink · Samuel J Gershman -
2015 Workshop: Black box learning and inference »
Josh Tenenbaum · Jan-Willem van de Meent · Tejas Kulkarni · S. M. Ali Eslami · Brooks Paige · Frank Wood · Zoubin Ghahramani -
2015 Workshop: Networks in the Social and Information Sciences »
Edo M Airoldi · David S Choi · Aaron Clauset · Johan Ugander · Panagiotis Toulis -
2015 : Discussion Panel with Morning Speakers (Day 1) »
Pedro Domingos · Stephen H Muggleton · Rina Dechter · Josh Tenenbaum -
2015 : Cognitive Foundations for Common-Sense Knowledge Representation and Reasoning »
Josh Tenenbaum -
2015 Poster: Softstar: Heuristic-Guided Probabilistic Inference »
Mathew Monfort · Brenden M Lake · Brenden Lake · Brian Ziebart · Patrick Lucey · Josh Tenenbaum -
2015 Poster: Deep Convolutional Inverse Graphics Network »
Tejas Kulkarni · William Whitney · Pushmeet Kohli · Josh Tenenbaum -
2015 Spotlight: Deep Convolutional Inverse Graphics Network »
Tejas Kulkarni · William Whitney · Pushmeet Kohli · Josh Tenenbaum -
2015 Poster: Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning »
Jiajun Wu · Ilker Yildirim · Joseph Lim · Bill Freeman · Josh Tenenbaum -
2015 Poster: Unsupervised Learning by Program Synthesis »
Kevin Ellis · Armando Solar-Lezama · Josh Tenenbaum -
2015 Poster: Copula variational inference »
Dustin Tran · David Blei · Edo M Airoldi -
2014 Workshop: Networks: From Graphs to Rich Data »
Edo M Airoldi · Aaron Clauset · Johan Ugander · David S Choi · Leto Peel -
2014 Workshop: 3rd NIPS Workshop on Probabilistic Programming »
Daniel Roy · Josh Tenenbaum · Thomas Dietterich · Stuart J Russell · YI WU · Ulrik R Beierholm · Alp Kucukelbir · Zenna Tavares · Yura Perov · Daniel Lee · Brian Ruttenberg · Sameer Singh · Michael Hughes · Marco Gaboardi · Alexey Radul · Vikash Mansinghka · Frank Wood · Sebastian Riedel · Prakash Panangaden -
2013 Workshop: Deep Learning »
Yoshua Bengio · Hugo Larochelle · Russ Salakhutdinov · Tomas Mikolov · Matthew D Zeiler · David Mcallester · Nando de Freitas · Josh Tenenbaum · Jian Zhou · Volodymyr Mnih -
2013 Workshop: Frontiers of Network Analysis: Methods, Models, and Applications »
Edo M Airoldi · David S Choi · Aaron Clauset · Khalid El-Arini · Jure Leskovec -
2013 Poster: One-shot learning by inverting a compositional causal process »
Brenden M Lake · Russ Salakhutdinov · Josh Tenenbaum -
2013 Poster: Stochastic blockmodel approximation of a graphon: Theory and consistent estimation »
Edo M Airoldi · Thiago B Costa · Stanley H Chan -
2013 Poster: Nonparametric Multi-group Membership Model for Dynamic Networks »
Myunghwan Kim · Jure Leskovec -
2013 Poster: Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs »
Vikash Mansinghka · Tejas D Kulkarni · Yura N Perov · Josh Tenenbaum -
2013 Oral: Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs »
Vikash Mansinghka · Tejas D Kulkarni · Yura N Perov · Josh Tenenbaum -
2012 Workshop: Social network and social media analysis: Methods, models and applications »
Edo M Airoldi · David S Choi · Khalid El-Arini · Jure Leskovec -
2012 Poster: Learning to Discover Social Circles in Ego Networks »
Julian J McAuley · Jure Leskovec -
2011 Workshop: Challenges in Learning Hierarchical Models: Transfer Learning and Optimization »
Quoc V. Le · Marc'Aurelio Ranzato · Russ Salakhutdinov · Josh Tenenbaum · Andrew Y Ng -
2011 Oral: Reconstructing Patterns of Information Diffusion from Incomplete Observations »
Flavio Chierichetti · Jon Kleinberg · David Liben-Nowell -
2011 Poster: Learning to Learn with Compound HD Models »
Russ Salakhutdinov · Josh Tenenbaum · Antonio Torralba -
2011 Poster: Reconstructing Patterns of Information Diffusion from Incomplete Observations »
Flavio Chierichetti · Jon Kleinberg · David Liben-Nowell -
2011 Spotlight: Learning to Learn with Compound HD Models »
Russ Salakhutdinov · Josh Tenenbaum · Antonio Torralba -
2011 Poster: Confidence Sets for Network Structure »
David S Choi · Patrick J Wolfe · Edo M Airoldi -
2010 Workshop: Networks Across Disciplines: Theory and Applications »
Edo M Airoldi · Anna Goldenberg · Jure Leskovec · Quaid Morris -
2010 Workshop: Transfer Learning Via Rich Generative Models. »
Russ Salakhutdinov · Ryan Adams · Josh Tenenbaum · Zoubin Ghahramani · Tom Griffiths -
2010 Invited Talk: How to Grow a Mind: Statistics, Structure and Abstraction »
Josh Tenenbaum -
2010 Oral: On the Convexity of Latent Social Network Inference »
Seth A Myers · Jure Leskovec -
2010 Poster: Dynamic Infinite Relational Model for Time-varying Relational Data Analysis »
Katsuhiko Ishiguro · Tomoharu Iwata · Naonori Ueda · Josh Tenenbaum -
2010 Poster: On the Convexity of Latent Social Network Inference »
Seth A Myers · Jure Leskovec -
2010 Poster: Nonparametric Bayesian Policy Priors for Reinforcement Learning »
Finale P Doshi-Velez · David Wingate · Nicholas Roy · Josh Tenenbaum -
2009 Workshop: Bounded-rational analyses of human cognition: Bayesian models, approximate inference, and the brain »
Noah Goodman · Edward Vul · Tom Griffiths · Josh Tenenbaum -
2009 Poster: Perceptual Multistability as Markov Chain Monte Carlo Inference »
Samuel J Gershman · Edward Vul · Josh Tenenbaum -
2009 Poster: Help or Hinder: Bayesian Models of Social Goal Inference »
Tomer D Ullman · Chris L Baker · Owen Macindoe · Owain Evans · Noah Goodman · Josh Tenenbaum -
2009 Spotlight: Perceptual Multistability as Markov Chain Monte Carlo Inference »
Samuel J Gershman · Edward Vul · Josh Tenenbaum -
2009 Poster: Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model »
Edward Vul · Michael C Frank · George Alvarez · Josh Tenenbaum -
2009 Oral: Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model »
Edward Vul · Michael C Frank · George Alvarez · Josh Tenenbaum -
2009 Poster: Modelling Relational Data using Bayesian Clustered Tensor Factorization »
Ilya Sutskever · Russ Salakhutdinov · Josh Tenenbaum -
2008 Workshop: Probabilistic Programming: Universal Languages, Systems and Applications »
Daniel Roy · John Winn · David A McAllester · Vikash Mansinghka · Josh Tenenbaum -
2008 Workshop: Analyzing Graphs: Theory and Applications »
Edo M Airoldi · David Blei · Jake M Hofman · Tony Jebara · Eric Xing -
2008 Workshop: Machine learning meets human learning »
Nathaniel D Daw · Tom Griffiths · Josh Tenenbaum · Jerry Zhu -
2008 Poster: Mixed Membership Stochastic Blockmodels »
Edo M Airoldi · David Blei · Stephen E Fienberg · Eric Xing -
2008 Spotlight: Mixed Membership Stochastic Blockmodels »
Edo M Airoldi · David Blei · Stephen E Fienberg · Eric Xing -
2007 Workshop: The Grammar of Vision: Probabilistic Grammar-Based Models for Visual Scene Understanding and Object Categorization »
Virginia Savova · Josh Tenenbaum · Leslie Kaelbling · Alan Yuille -
2007 Spotlight: A Bayesian Framework for Cross-Situational Word-Learning »
Michael C Frank · Noah Goodman · Josh Tenenbaum -
2007 Poster: A Bayesian Framework for Cross-Situational Word-Learning »
Michael C Frank · Noah Goodman · Josh Tenenbaum -
2007 Poster: A complexity measure for intuitive theories »
Charles Kemp · Noah Goodman · Josh Tenenbaum -
2006 Poster: Combining causal and similarity-based reasoning »
Charles Kemp · Patrick Shafto · Allison Berke · Josh Tenenbaum -
2006 Poster: Multiple timescales and uncertainty in motor adaptation »
Konrad P Kording · Josh Tenenbaum · Reza Shadmehr -
2006 Poster: Learning annotated hierarchies from relational data »
Daniel Roy · Charles Kemp · Vikash Mansinghka · Josh Tenenbaum -
2006 Talk: Learning annotated hierarchies from relational data »
Daniel Roy · Charles Kemp · Vikash Mansinghka · Josh Tenenbaum -
2006 Spotlight: Multiple timescales and uncertainty in motor adaptation »
Konrad P Kording · Josh Tenenbaum · Reza Shadmehr -
2006 Talk: Combining causal and similarity-based reasoning »
Charles Kemp · Patrick Shafto · Allison Berke · Josh Tenenbaum -
2006 Poster: Causal inference in sensorimotor integration »
Konrad P Kording · Josh Tenenbaum -
2006 Tutorial: Bayesian Models of Human Learning and Inference »
Josh Tenenbaum