2,122 research outputs found
Combining Stochastic Constraint Optimization and Probabilistic Programming
Algorithms and the Foundations of Software technolog
ADDMC: Weighted Model Counting with Algebraic Decision Diagrams
We present an algorithm to compute exact literal-weighted model counts of
Boolean formulas in Conjunctive Normal Form. Our algorithm employs dynamic
programming and uses Algebraic Decision Diagrams as the primary data structure.
We implement this technique in ADDMC, a new model counter. We empirically
evaluate various heuristics that can be used with ADDMC. We then compare ADDMC
to state-of-the-art exact weighted model counters (Cachet, c2d, d4, and
miniC2D) on 1914 standard model counting benchmarks and show that ADDMC
significantly improves the virtual best solver.Comment: Presented at AAAI 202
Probabilistic Programming Concepts
A multitude of different probabilistic programming languages exists today,
all extending a traditional programming language with primitives to support
modeling of complex, structured probability distributions. Each of these
languages employs its own probabilistic primitives, and comes with a particular
syntax, semantics and inference procedure. This makes it hard to understand the
underlying programming concepts and appreciate the differences between the
different languages. To obtain a better understanding of probabilistic
programming, we identify a number of core programming concepts underlying the
primitives used by various probabilistic languages, discuss the execution
mechanisms that they require and use these to position state-of-the-art
probabilistic languages and their implementation. While doing so, we focus on
probabilistic extensions of logic programming languages such as Prolog, which
have been developed since more than 20 years
Learning Tuple Probabilities
Learning the parameters of complex probabilistic-relational models from
labeled training data is a standard technique in machine learning, which has
been intensively studied in the subfield of Statistical Relational Learning
(SRL), but---so far---this is still an under-investigated topic in the context
of Probabilistic Databases (PDBs). In this paper, we focus on learning the
probability values of base tuples in a PDB from labeled lineage formulas. The
resulting learning problem can be viewed as the inverse problem to confidence
computations in PDBs: given a set of labeled query answers, learn the
probability values of the base tuples, such that the marginal probabilities of
the query answers again yield in the assigned probability labels. We analyze
the learning problem from a theoretical perspective, cast it into an
optimization problem, and provide an algorithm based on stochastic gradient
descent. Finally, we conclude by an experimental evaluation on three real-world
and one synthetic dataset, thus comparing our approach to various techniques
from SRL, reasoning in information extraction, and optimization
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