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Learning Bounded Treewidth Bayesian Networks
2008
Neural Information Processing Systems
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With the increased availability of data for complex domains, it is desirable to learn Bayesian network structures that are sufficiently expressive for generalization while also allowing for tractable inference ...
In this work we present a novel method for learning Bayesian networks of bounded treewidth that employs global structure modifications and that is polynomial in the size of the graph and the treewidth ...
Finally, we learn a bounded treewidth Bayesian network by iteratively augmenting the model with such chains. ...
dblp:conf/nips/ElidanG08
fatcat:tdll4l645vf5pdyeb4hneblhtm
FINDING MOST LIKELY HAPLOTYPES IN GENERAL PEDIGREES THROUGH PARALLEL SEARCH WITH DYNAMIC LOAD BALANCING
[chapter]
2010
Biocomputing 2011
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and information learned from past subproblems which is facilitated through the use of depth-first branch and bound to explore the master search space. ...
General pedigrees can be encoded as Bayesian networks, where the common MPE query corresponds to finding the most likely haplotype configuration. ...
The structure of the AND/OR search tree is based on the underlying pseudo tree T : the root of the AND/OR search tree is an OR node labeled with the root of T . ...
doi:10.1142/9789814335058_0004
fatcat:cyekyq7e7ndudh25puuub3xsdy
Auto-Keras: An Efficient Neural Architecture Search System
[article]
2019
arXiv
pre-print
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In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search. ...
The framework develops a neural network kernel and a tree-structured acquisition function optimization algorithm to efficiently explores the search space. ...
NETWORK MORPHISM GUIDED BY BAYESIAN OPTIMIZATION The key idea of the proposed method is to explore the search space via morphing the neural architectures guided by Bayesian optimization (BO) algorithm. ...
arXiv:1806.10282v3
fatcat:u4ieabmp7zgt7g2lpfvwv5gbzu
Learning Bounded Treewidth Bayesian Networks with Thousands of Variables
[article]
2016
arXiv
pre-print
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We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. ...
Bounding the treewidth of a Bayesian greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. ...
As a second contribution, we propose two approaches for learning Bayesian networks with bounded treewidth. ...
arXiv:1605.03392v1
fatcat:66cp6fq3fzcn3kemwzxmcuhd7i
A Dynamic Approach for MPE and Weighted MAX-SAT
2007
International Joint Conference on Artificial Intelligence
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We consider the more general CNF-based MPE problem, where each literal in a CNF-formula is associated with a weight. ...
The MPE-SAT algorithm is quite competitive with the state-of-the-art MAX-SAT, WCSP, and MPE solvers on a variety of problems. ...
The induced width of a Bayesian network is a measure of its density. ...
dblp:conf/ijcai/SangBK07
fatcat:huqbcmrzwncqvnfmdhjjsjqq2e
Parameterized Complexity Results for Exact Bayesian Network Structure Learning
2013
The Journal of Artificial Intelligence Research
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Bayesian network structure learning is the notoriously difficult problem of discovering a Bayesian network that optimally represents a given set of training data. ...
Results: We show that exact Bayesian network structure learning can be carried out in non-uniform polynomial time if the super-structure has bounded treewidth, and in linear time if in addition the super-structure ...
In the following we will assume that we are given an instance I = (D, f, k) of k-O-Local Search Bayesian Network Structure Learning together with a nice tree decomposition (T, χ, r) for S f of width at ...
doi:10.1613/jair.3744
fatcat:4vxjfnzavnapxkujvl6eqqorhe
Best-First AND/OR Search for Most Probable Explanations
[article]
2012
arXiv
pre-print
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The paper evaluates the power of best-first search over AND/OR search spaces for solving the Most Probable Explanation (MPE) task in Bayesian networks. ...
The main contribution of this paper is in showing that a recent extension of AND/OR search algorithms from depth-first Branch-and-Bound to best-first is indeed very effective for computing the MPE in Bayesian ...
Furthermore, we showed [3] that AOBB can improve its guiding heuristic function dynamically, by learning from portions of the search space that were already explored. ...
arXiv:1206.5268v1
fatcat:z4qd76htpfbpfiliatj7kuoge4
Toward parallel search for optimization in graphical models
2010
International Symposium on Artificial Intelligence and Mathematics
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Subproblem generation is itself embedded into an AND/OR Branch and Bound algorithm and dynamically takes previous subproblem solutions into account. ...
Based on the AND/OR graph search framework, the procedure exploits the structure of the underlying problem graph. ...
its master search space through AND/OR graph search guided by the start pseudo tree T c . ...
dblp:conf/isaim/OttenD10
fatcat:irdvlyn2vffx5dbh2eifoyflsu
SAT-Based Approaches to Treewidth Computation: An Evaluation
2014
2014 IEEE 26th International Conference on Tools with Artificial Intelligence
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Malone, “Learning optimal bounded
modifications to the encoding. Compared to earlier work, we treewidth bayesian networks via maximum satisfiability,” in Proc. ...
treewidth Bayesian network structures [17]. ...
doi:10.1109/ictai.2014.57
dblp:conf/ictai/BergJ14
fatcat:pcjlnsh4cvcqbnbb4zaelcadx4
Bayesian Inference in Monte-Carlo Tree Search
[article]
2012
arXiv
pre-print
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Monte-Carlo Tree Search (MCTS) methods are drawing great interest after yielding breakthrough results in computer Go. ...
We further propose propagating inference in the tree via fast analytic Gaussian approximation methods: this can make the overhead of Bayesian inference manageable in domains such as Go, while preserving ...
RELATED WORK Several extensions of UCT are known in the literature; most use the core UCT search strategy but provide alternative upper-bound functions to guide node selection. ...
arXiv:1203.3519v1
fatcat:egcrymgdxbe6hlhmm6c77hweni
Planning for Novelty: Width-Based Algorithms for Common Problems in Control, Planning and Reinforcement Learning
[article]
2021
arXiv
pre-print
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Width-based algorithms search for solutions through a general definition of state novelty. ...
Width-based algorithms performance is understood theoretically through the notion of planning width, providing polynomial guarantees on their runtime and memory consumption. ...
Similar treewidth notions have been proposed to bound algorithms over intractable problems in constraint satisfaction and bayesian networks [Dechter, 2003; Pearl, 1988] . Definition 2. ...
arXiv:2106.04866v1
fatcat:yhsvlej5j5av3bf5iw7bwadvji
Learning Multivariate Distributions by Competitive Assembly of Marginals
2013
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The approach is motivated by compositional models and Bayesian networks, and designed to adapt to small sample sizes. ...
We start with a large, overlapping set of elementary statistical building blocks, or "primitives", which are low-dimensional marginal distributions learned from data. ...
gives a subset of Bayesian networks of tree-width w, while dropping the global constraint gives the superset CPCP w [46] . ...
doi:10.1109/tpami.2012.96
pmid:22529323
fatcat:annyoql4prhhnmlzc2n7utjz74
AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Graphical Models
2008
The Journal of Artificial Intelligence Research
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Inspired by the recently introduced framework of AND/OR search spaces for graphical models, we propose to augment Multi-Valued Decision Diagrams (MDD) with AND nodes, in order to capture function decomposition ...
The first is search-based, and works by applying reduction rules to the trace of the memory intensive AND/OR search algorithm. ...
This work was supported by the NSF grants IIS-0412854 and IIS-0713118, and the initial part by the Radcliffe fellowship 2005-2006 (through the partner program), with Harvard undergraduate student John ...
doi:10.1613/jair.2605
fatcat:pfoyf633kvaabkcsppwqm4b4sm
Stochastic Anytime Search for Bounding Marginal MAP
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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In this paper, we develop new search-based bounding schemes for Marginal MAP that produce anytime upper and lower bounds without performing exact likelihood computations. ...
The empirical evaluation demonstrates the effectiveness of our new methods against the current best-performing search-based bounds. ...
AND/OR Search Spaces AND/OR search is guided by a spanning pseudo tree of the graphical model's primal graph (in which any arc of the model not in the tree is a back-arc in the pseudo-tree) and the search ...
doi:10.24963/ijcai.2018/704
dblp:conf/ijcai/0002DI18
fatcat:xnx47pkymrg37fqbktpqx34yie
Approximate Inference by Compilation to Arithmetic Circuits
2010
Neural Information Processing Systems
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Arithmetic circuits (ACs) exploit context-specific independence and determinism to allow exact inference even in networks with high treewidth. ...
We propose and evaluate a variety of techniques based on exact compilation, forward sampling, AC structure learning, Markov network parameter learning, variational inference, and Gibbs sampling. ...
The WinMine Toolkit learns BNs with tree-structured CPDs, leading to complex models with high tree-width. ...
dblp:conf/nips/LowdD10
fatcat:3vugsjcgrnbndi6l7rftmnidwe
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