Daniel Selsam

Publication Activity (10 Years)

Years Active: 2014-2021
Publications (10 Years): 17

Top Topics

Equivalence Relation

Propositional Satisfiability

Top Venues

Proc. ACM Program. Lang.

Publications

Daniel Selsam, Jesse Michael Han
k-Equivalence Relations and Associated Algorithms. CoRR (2021)
Ryan Krueger, Jesse Michael Han, Daniel Selsam
Automatically Building Diagrams for Olympiad Geometry Problems. CADE (2021)
Daniel Selsam, Simon Hudon, Leonardo de Moura
Sealing pointer-based optimizations behind pure functions. Proc. ACM Program. Lang. 4 (ICFP) (2020)
Daniel Selsam, Jesse Michael Han, Leonardo de Moura, Patrice Godefroid
Universal Policies for Software-Defined MDPs. CoRR (2020)
Daniel Selsam, Sebastian Ullrich, Leonardo de Moura
Tabled Typeclass Resolution. CoRR (2020)
Daniel Selsam, Simon Hudon, Leonardo de Moura
Sealing Pointer-Based Optimizations Behind Pure Functions. CoRR (2020)
Ryan Krueger, Jesse Michael Han, Daniel Selsam
Automatically Building Diagrams for Olympiad Geometry Problems. CoRR (2020)
Daniel Selsam, Nikolaj Bjørner
NeuroCore: Guiding High-Performance SAT Solvers with Unsat-Core Predictions. CoRR (2019)
Daniel Selsam, Nikolaj Bjørner
Guiding High-Performance SAT Solvers with Unsat-Core Predictions. SAT (2019)
Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, David L. Dill
Learning a SAT Solver from Single-Bit Supervision. ICLR (Poster) (2019)
Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, David L. Dill
Learning a SAT Solver from Single-Bit Supervision. CoRR (2018)
Daniel Selsam, Percy Liang, David L. Dill
Developing Bug-Free Machine Learning Systems With Formal Mathematics. ICML (2017)
Daniel Selsam, Leonardo de Moura
Congruence Closure in Intensional Type Theory. CoRR (2017)
Daniel Selsam, Percy Liang, David L. Dill
Developing Bug-Free Machine Learning Systems With Formal Mathematics. CoRR (2017)
Daniel Selsam, Leonardo de Moura
Congruence Closure in Intensional Type Theory. IJCAR (2016)
Alexander J. Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré
Data Programming: Creating Large Training Sets, Quickly. NIPS (2016)
Alexander Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré
Data Programming: Creating Large Training Sets, Quickly. CoRR (2016)
Vikash K. Mansinghka, Daniel Selsam, Yura N. Perov
Venture: a higher-order probabilistic programming platform with programmable inference. CoRR (2014)