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.
no code implementations • 8 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.
no code implementations • 8 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.
no code implementations • 3 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.
no code implementations • 26 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.