Search Results for author: Paul Beame

Found 5 papers, 1 papers with code

Smoothing Structured Decomposable Circuits

1 code implementation NeurIPS 2019 Andy Shih, Guy Van Den Broeck, Paul Beame, Antoine Amarilli

Further, for the important case of All-Marginals, we show a more efficient linear-time algorithm.

Density Estimation

Time-Space Tradeoffs for Learning from Small Test Spaces: Learning Low Degree Polynomial Functions

no code implementations8 Aug 2017 Paul Beame, Shayan Oveis Gharan, Xin Yang

We develop an extension of recently developed methods for obtaining time-space tradeoff lower bounds for problems of learning from random test samples to handle the situation where the space of tests is signficantly smaller than the space of inputs, a class of learning problems that is not handled by prior work.

New Limits for Knowledge Compilation and Applications to Exact Model Counting

no code implementations8 Jun 2015 Paul Beame, Vincent Liew

We use this relationship to prove exponential lower bounds on the SDD size for representing a large class of problems that occur naturally as queries over probabilistic databases.

Symmetric Weighted First-Order Model Counting

no code implementations3 Dec 2014 Paul Beame, Guy Van Den Broeck, Eric Gribkoff, Dan Suciu

For the combined complexity, we prove that, for every fragment FO$^{k}$, $k\geq 2$, the combined complexity of FOMC (or WFOMC) is #P-complete.

Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases

no code implementations26 Sep 2013 Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu

The best current methods for exactly computing the number of satisfying assignments, or the satisfying probability, of Boolean formulas can be seen, either directly or indirectly, as building 'decision-DNNF' (decision decomposable negation normal form) representations of the input Boolean formulas.

Cannot find the paper you are looking for? You can Submit a new open access paper.