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Author Information
John Duchi (Stanford)
Danny Tarlow (Google Research, Brain team)
Gal Elidan (Hebrew University)
Daphne Koller (insitro)
Daphne Koller is the Rajeev Motwani Professor of Computer Science at Stanford University and the co-founder and co-CEO of Coursera, a social entrepreneurship company that works with the best universities to connect anyone around the world with the best education, for free. Coursera is the leading MOOC (Massive Open Online Course) platform, and has partnered with dozens of the world’s top universities to offer hundreds of courses in a broad range of disciplines to millions of students, spanning every country in the world. In her research life, she works in the area of machine learning and probabilistic modeling, with applications to systems biology and personalized medicine. She is the author of over 200 refereed publications in venues that span a range of disciplines, and has given over 15 keynote talks at major conferences. She is the recipient of many awards, which include the Presidential Early Career Award for Scientists and Engineers (PECASE), the MacArthur Foundation Fellowship, the ACM/Infosys award, and membership in the US National Academy of Engineering. She is also an award winning teacher, who pioneered in her Stanford class many of the ideas that underlie the Coursera user experience. She received her BSc and MSc from the Hebrew University of Jerusalem, and her PhD from Stanford in 1994.
More from the Same Authors
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2021 Spotlight: PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair »
Zimin Chen · Vincent J Hellendoorn · Pascal Lamblin · Petros Maniatis · Pierre-Antoine Manzagol · Daniel Tarlow · Subhodeep Moitra -
2021 Spotlight: Learning Generalized Gumbel-max Causal Mechanisms »
Guy Lorberbom · Daniel D. Johnson · Chris Maddison · Daniel Tarlow · Tamir Hazan -
2021 : Regression modeling on DNA encoded libraries »
Ralph Ma · Gabriel Dreiman · Fiorella Ruggiu · Adam Riesselman · Bowen Liu · Mohammad M Sultan · Daphne Koller -
2021 Workshop: Advances in Programming Languages and Neurosymbolic Systems (AIPLANS) »
Breandan Considine · Disha Shrivastava · David Yu-Tung Hui · Chin-Wei Huang · Shawn Tan · Xujie Si · Prakash Panangaden · Guy Van den Broeck · Daniel Tarlow -
2021 Poster: Structured Denoising Diffusion Models in Discrete State-Spaces »
Jacob Austin · Daniel D. Johnson · Jonathan Ho · Daniel Tarlow · Rianne van den Berg -
2021 Poster: Learning to Combine Per-Example Solutions for Neural Program Synthesis »
Disha Shrivastava · Hugo Larochelle · Daniel Tarlow -
2021 Poster: PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair »
Zimin Chen · Vincent J Hellendoorn · Pascal Lamblin · Petros Maniatis · Pierre-Antoine Manzagol · Daniel Tarlow · Subhodeep Moitra -
2021 Poster: Learning Generalized Gumbel-max Causal Mechanisms »
Guy Lorberbom · Daniel D. Johnson · Chris Maddison · Daniel Tarlow · Tamir Hazan -
2019 : In conversations: Daphne Koller and Barbara Englehardt »
Daphne Koller · Barbara Engelhardt -
2014 Workshop: Perturbations, Optimization, and Statistics »
Tamir Hazan · George Papandreou · Danny Tarlow -
2014 Poster: Just-In-Time Learning for Fast and Flexible Inference »
S. M. Ali Eslami · Danny Tarlow · Pushmeet Kohli · John Winn -
2014 Poster: A* Sampling »
Chris Maddison · Danny Tarlow · Tom Minka -
2014 Oral: A* Sampling »
Chris Maddison · Danny Tarlow · Tom Minka -
2013 Workshop: Perturbations, Optimization, and Statistics »
Tamir Hazan · George Papandreou · Sasha Rakhlin · Danny Tarlow -
2013 Poster: Information-theoretic lower bounds for distributed statistical estimation with communication constraints »
Yuchen Zhang · John Duchi · Michael Jordan · Martin J Wainwright -
2013 Oral: Information-theoretic lower bounds for distributed statistical estimation with communication constraints »
Yuchen Zhang · John Duchi · Michael Jordan · Martin J Wainwright -
2013 Invited Talk: The Online Revolution: Learning without Limits »
Daphne Koller -
2013 Poster: Learning to Pass Expectation Propagation Messages »
Nicolas Heess · Danny Tarlow · John Winn -
2013 Poster: Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation »
John Duchi · Martin J Wainwright · Michael Jordan -
2013 Poster: Estimation, Optimization, and Parallelism when Data is Sparse »
John Duchi · Michael Jordan · Brendan McMahan -
2012 Workshop: Perturbations, Optimization, and Statistics »
Tamir Hazan · George Papandreou · Danny Tarlow -
2012 Workshop: Big Learning : Algorithms, Systems, and Tools »
Sameer Singh · John Duchi · Yucheng Low · Joseph E Gonzalez -
2012 Poster: Shifting Weights: Adapting Object Detectors from Image to Video »
Kevin Tang · Vignesh Ramanathan · Li Fei-Fei · Daphne Koller -
2012 Poster: Nonparanormal Belief Propagation (NPBP) »
Gal Elidan · Cobi Cario -
2012 Poster: Bayesian n-Choose-k Models for Classification and Ranking »
Kevin Swersky · Danny Tarlow · Richard Zemel · Ryan Adams · Brendan J Frey -
2012 Poster: Privacy Aware Learning »
John Duchi · Michael Jordan · Martin J Wainwright -
2012 Poster: Communication-Efficient Algorithms for Statistical Optimization »
Yuchen Zhang · John Duchi · Martin J Wainwright -
2012 Oral: Privacy Aware Learning »
John Duchi · Michael Jordan · Martin J Wainwright -
2012 Poster: Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods »
John Duchi · Michael Jordan · Martin J Wainwright · Andre Wibisono -
2012 Poster: Cardinality Restricted Boltzmann Machines »
Kevin Swersky · Danny Tarlow · Ilya Sutskever · Richard Zemel · Russ Salakhutdinov · Ryan Adams -
2011 Workshop: Copulas in Machine Learning »
Gal Elidan · Zoubin Ghahramani · John Lafferty -
2011 Poster: Active Classification based on Value of Classifier »
Tianshi Gao · Daphne Koller -
2011 Poster: Distributed Delayed Stochastic Optimization »
Alekh Agarwal · John Duchi -
2011 Spotlight: Active Classification based on Value of Classifier »
Tianshi Gao · Daphne Koller -
2010 Workshop: Learning on Cores, Clusters, and Clouds »
Alekh Agarwal · Lawrence Cayton · Ofer Dekel · John Duchi · John Langford -
2010 Spotlight: Distributed Dual Averaging In Networks »
John Duchi · Alekh Agarwal · Martin J Wainwright -
2010 Poster: Copula Bayesian Networks »
Gal Elidan -
2010 Poster: Distributed Dual Averaging In Networks »
John Duchi · Alekh Agarwal · Martin J Wainwright -
2010 Poster: Self-Paced Learning for Latent Variable Models »
M. Pawan Kumar · Benjamin D Packer · Daphne Koller -
2009 Poster: Efficient Learning using Forward-Backward Splitting »
John Duchi · Yoram Singer -
2009 Poster: Region-based Segmentation and Object Detection »
Stephen Gould · Tianshi Gao · Daphne Koller -
2009 Spotlight: Region-based Segmentation and Object Detection »
Stephen Gould · Tianshi Gao · Daphne Koller -
2009 Oral: Efficient Learning using Forward-Backward Splitting »
John Duchi · Yoram Singer -
2009 Poster: Learning a Small Mixture of Trees »
M. Pawan Kumar · Daphne Koller -
2008 Oral: Cascaded Classification Models: Combining Models for Holistic Scene Understanding »
Geremy Heitz · Stephen Gould · Ashutosh Saxena · Daphne Koller -
2008 Poster: Cascaded Classification Models: Combining Models for Holistic Scene Understanding »
Geremy Heitz · Stephen Gould · Ashutosh Saxena · Daphne Koller -
2008 Poster: Learning Bounded Treewidth Bayesian Networks »
Gal Elidan · Stephen Gould -
2008 Spotlight: Learning Bounded Treewidth Bayesian Networks »
Gal Elidan · Stephen Gould -
2008 Poster: LOOPS: Localizing Object Outlines using Probabilistic Shape »
Geremy Heitz · Gal Elidan · Benjamin D Packer · Daphne Koller -
2007 Demonstration: Holistic Scene Understanding from Visual and Range Data »
Stephen Gould · Morgan Quigley · Andrew Y Ng · Daphne Koller -
2006 Poster: Max-margin classification of incomplete data »
Gal Chechik · Geremy Heitz · Gal Elidan · Pieter Abbeel · Daphne Koller -
2006 Poster: Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Task »
Alexis Battle · Gal Chechik · Daphne Koller -
2006 Spotlight: Max-margin classification of incomplete data »
Gal Chechik · Geremy Heitz · Gal Elidan · Pieter Abbeel · Daphne Koller -
2006 Talk: Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Task »
Alexis Battle · Gal Chechik · Daphne Koller -
2006 Spotlight: Using Combinatorial Optimization within Max-Product Belief Propagation »
John Duchi · Danny Tarlow · Gal Elidan · Daphne Koller -
2006 Poster: Efficient Structure Learning of Markov Networks using L1-Regularization »
Su-In Lee · Varun Ganapathi · Daphne Koller