1,852 research outputs found
Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting
Weighted model counting (WMC) has emerged as a prevalent approach for
probabilistic inference. In its most general form, WMC is #P-hard. Weighted DNF
counting (weighted #DNF) is a special case, where approximations with
probabilistic guarantees are obtained in O(nm), where n denotes the number of
variables, and m the number of clauses of the input DNF, but this is not
scalable in practice. In this paper, we propose a neural model counting
approach for weighted #DNF that combines approximate model counting with deep
learning, and accurately approximates model counts in linear time when width is
bounded. We conduct experiments to validate our method, and show that our model
learns and generalizes very well to large-scale #DNF instances.Comment: To appear in Proceedings of the Thirty-Fourth AAAI Conference on
Artificial Intelligence (AAAI-20). Code and data available at:
https://github.com/ralphabb/NeuralDNF
Flexible constrained sampling with guarantees for pattern mining
Pattern sampling has been proposed as a potential solution to the infamous
pattern explosion. Instead of enumerating all patterns that satisfy the
constraints, individual patterns are sampled proportional to a given quality
measure. Several sampling algorithms have been proposed, but each of them has
its limitations when it comes to 1) flexibility in terms of quality measures
and constraints that can be used, and/or 2) guarantees with respect to sampling
accuracy. We therefore present Flexics, the first flexible pattern sampler that
supports a broad class of quality measures and constraints, while providing
strong guarantees regarding sampling accuracy. To achieve this, we leverage the
perspective on pattern mining as a constraint satisfaction problem and build
upon the latest advances in sampling solutions in SAT as well as existing
pattern mining algorithms. Furthermore, the proposed algorithm is applicable to
a variety of pattern languages, which allows us to introduce and tackle the
novel task of sampling sets of patterns. We introduce and empirically evaluate
two variants of Flexics: 1) a generic variant that addresses the well-known
itemset sampling task and the novel pattern set sampling task as well as a wide
range of expressive constraints within these tasks, and 2) a specialized
variant that exploits existing frequent itemset techniques to achieve
substantial speed-ups. Experiments show that Flexics is both accurate and
efficient, making it a useful tool for pattern-based data exploration.Comment: Accepted for publication in Data Mining & Knowledge Discovery journal
(ECML/PKDD 2017 journal track
Distribution-Aware Sampling and Weighted Model Counting for SAT
Given a CNF formula and a weight for each assignment of values to variables,
two natural problems are weighted model counting and distribution-aware
sampling of satisfying assignments. Both problems have a wide variety of
important applications. Due to the inherent complexity of the exact versions of
the problems, interest has focused on solving them approximately. Prior work in
this area scaled only to small problems in practice, or failed to provide
strong theoretical guarantees, or employed a computationally-expensive maximum
a posteriori probability (MAP) oracle that assumes prior knowledge of a
factored representation of the weight distribution. We present a novel approach
that works with a black-box oracle for weights of assignments and requires only
an {\NP}-oracle (in practice, a SAT-solver) to solve both the counting and
sampling problems. Our approach works under mild assumptions on the
distribution of weights of satisfying assignments, provides strong theoretical
guarantees, and scales to problems involving several thousand variables. We
also show that the assumptions can be significantly relaxed while improving
computational efficiency if a factored representation of the weights is known.Comment: This is a full version of AAAI 2014 pape
Simple Load Balancing for Distributed Hash Tables
Distributed hash tables have recently become a useful building block for a variety of distributed applications. However, current schemes based upon consistent hashing require both considerable implementation complexity and substantial storage overhead to achieve desired load balancing goals. We argue in this paper that these goals can b e achieved more simply and more cost-effectively. First, we suggest the direct application of the "power of two choices" paradigm, whereby an item is stored at the less loaded of two (or more) random alternatives. We then consider how associating a small constant number of hash values with a key can naturally b e extended to support other load balancing methods, including load-stealing or load-shedding schemes, as well as providing natural fault-tolerance mechanisms
Sampling Techniques for Boolean Satisfiability
Boolean satisfiability ({\SAT}) has played a key role in diverse areas
spanning testing, formal verification, planning, optimization, inferencing and
the like. Apart from the classical problem of checking boolean satisfiability,
the problems of generating satisfying uniformly at random, and of counting the
total number of satisfying assignments have also attracted significant
theoretical and practical interest over the years. Prior work offered heuristic
approaches with very weak or no guarantee of performance, and theoretical
approaches with proven guarantees, but poor performance in practice.
We propose a novel approach based on limited-independence hashing that allows
us to design algorithms for both problems, with strong theoretical guarantees
and scalability extending to thousands of variables. Based on this approach, we
present two practical algorithms, {\UniformWitness}: a near uniform generator
and {\approxMC}: the first scalable approximate model counter, along with
reference implementations. Our algorithms work by issuing polynomial calls to
{\SAT} solver. We demonstrate scalability of our algorithms over a large set of
benchmarks arising from different application domains.Comment: MS Thesis submitted to Rice Universit
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