MAPL 2017 Contributors -

Sun 18 - Fri 23 June 2017 Barcelona, Spain

co-located with PLDI 2017

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Results (32)

A Martin AbadiAbadi, Martin Google A Computational Model for TensorFlow (An Introduction)
B Raj BarikBarik, Raj Intel Labs Committee Member in Organizing Committee within the MAPL-track Committee Member in Program Committee within the MAPL-track
E Jason EisnerEisner, Jason The Johns Hopkins University Dyna: Toward a Self-Optimizing Declarative Language for Machine Learning Applications
Stefano ErmonErmon, Stefano Stanford Committee Member in Organizing Committee within the MAPL-track Committee Member in Program Committee within the MAPL-track
F Nathaniel Wesley FilardoFilardo, Nathaniel Wesley Dyna: Toward a Self-Optimizing Declarative Language for Machine Learning Applications
Jeffrey S. FosterFoster, Jeffrey S. University of Maryland, College ParkUnited States Learning a Classifier for False Positive Error Reports Emitted by Static Code Analysis Tools
Matthew Francis-LandauFrancis-Landau, Matthew The Johns Hopkins University Dyna: Toward a Self-Optimizing Declarative Language for Machine Learning Applications
G Justin GottschlichGottschlich, Justin Intel Labs Introduction and Welcome Concluding Remarks Committee Member in Organizing Committee within the MAPL-track Program Chair in Program Committee within the MAPL-track
Dan GrossmanGrossman, Dan University of WashingtonUnited States Debugging Probabilistic Programs
Sumit GulwaniGulwani, Sumit Microsoft Research Programming by Examples: PL Meets ML
H Mary HallHall, Mary University of Utah Committee Member in Program Committee within the MAPL-track
Peter HawkinsHawkins, Peter Google Committee Member in Program Committee within the MAPL-track
I Costin IancuIancu, Costin Lawrence Berkeley National Laboratory Committee Member in Organizing Committee within the MAPL-track Committee Member in Program Committee within the MAPL-track
Michael IsardIsard, Michael Google A Computational Model for TensorFlow (An Introduction)
K Farzad KhorasaniKhorasani, Farzad Rice University Dyna: Toward a Self-Optimizing Declarative Language for Machine Learning Applications
Ugur KocKoc, Ugur University of Maryland, College ParkUnited States Learning a Classifier for False Positive Error Reports Emitted by Static Code Analysis Tools
M Kathryn S McKinleyMcKinley, Kathryn S Microsoft Research Debugging Probabilistic Programs
Charlie MurphyMurphy, Charlie Princeton University Verified Perceptron Convergence Theorem
Derek MurrayMurray, Derek Google A Computational Model for TensorFlow (An Introduction)
Todd MytkowiczMytkowicz, Todd Debugging Probabilistic Programs
N Mayur NaikNaik, Mayur Georgia Tech Combining the Logical and the Probabilistic in Program Analysis
Chandrakana NandiNandi, Chandrakana University of Washington, USAIndia Debugging Probabilistic Programs
O Michael F. P. O'BoyleO'Boyle, Michael F. P. University of Edinburgh Committee Member in Program Committee within the MAPL-track
Kunle OlukotunOlukotun, Kunle Stanford UniversityUnited States Committee Member in Organizing Committee within the MAPL-track Committee Member in Program Committee within the MAPL-track
P Adam PorterPorter, Adam University of Maryland Learning a Classifier for False Positive Error Reports Emitted by Static Code Analysis Tools
S Parsa SaadatpanahSaadatpanah, Parsa University of Maryland Learning a Classifier for False Positive Error Reports Emitted by Static Code Analysis Tools
Adrian SampsonSampson, Adrian Cornell UniversityUnited States Debugging Probabilistic Programs
Tatiana ShpeismanShpeisman, Tatiana Intel LabsUnited States Introduction and Welcome Concluding Remarks General Chair in Organizing Committee within the MAPL-track Committee Member in Program Committee within the MAPL-track
Xujie SiSi, Xujie Combining the Logical and the Probabilistic in Program Analysis
Gordon StewartStewart, Gordon Ohio University Verified Perceptron Convergence Theorem
V Tim VieiraVieira, Tim Johns Hopkins University Dyna: Toward a Self-Optimizing Declarative Language for Machine Learning Applications
Z Xin ZhangZhang, Xin Georgia TechChina Combining the Logical and the Probabilistic in Program Analysis