A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This paper improves deep learning by integrating Bayesian inference into network structure learning.
A gradient-based method learns the structure of TAN for Bayesian network classifiers.
DiBS learns Bayesian network structure and parameters efficiently.
Bayesian network structure learning algorithms with limited data are being used in domains such as systems biology and neuroscience to gain insight into the underlying processes that produce observed data. Learning reliable networks from limited data is difficult, therefore transfer learning can improve the robustness …
EDAs with matrix transpose improve Bayesian structure learning performance.
BiDAG R package learns and samples Bayesian network structures efficiently.
Sum-product networks (SPNs) are flexible density estimators and have received significant attention due to their attractive inference properties. While parameter learning in SPNs is well developed, structure learning leaves something to be desired: Even though there is a plethora of SPN structure learners, most of them…
Meta-learning improves Bayesian causal discovery by sampling from the posterior.
A novel method for learning Bayesian network structures from decentralized data, balancing privacy and efficiency.
Paper introduces VBG for Bayesian causal structure and mechanism learning.
We study the problem of learning Bayesian network structures from data. We develop an algorithm for finding the k-best Bayesian network structures. We propose to compute the posterior probabilities of hypotheses of interest by Bayesian model averaging over the k-best Bayesian networks. We present empirical results on s…
Library learns Bayesian networks from mixed data without discretization.
Paper improves Bayesian network learning from related data sets.
Bayesian scores improve structure learning in probabilistic circuits.
Federated learning for Bayesian network structure learning across distributed data.
In recent years there has been a flurry of works on learning Bayesian networks from data. One of the hard problems in this area is how to effectively learn the structure of a belief network from incomplete data- that is, in the presence of missing values or hidden variables. In a recent paper, I introduced an algorithm…
Bayesian approach adapts deep network structure for continual learning.
Bayesian structure learning is the NP-hard problem of discovering a Bayesian network that optimally represents a given set of training data. In this paper we study the computational worst-case complexity of exact Bayesian structure learning under graph theoretic restrictions on the super-structure. The super-structure …
Bayesian structure learning improved using GFlowNets.
Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is…
This dissertation uses ILP to learn Bayesian network structures efficiently.
Proposes a new Bayesian score for learning network structure from related datasets.
Structure learning of Bayesian networks is an important problem that arises in numerous machine learning applications. In this work, we present a novel approach for learning the structure of Bayesian networks using the solution of an appropriately constructed traveling salesman problem. In our approach, one computes an…
Approaches to learning Bayesian networks from data typically combine a scoring function with a heuristic search procedure. Given a Bayesian network structure, many of the scoring functions derived in the literature return a score for the entire equivalence class to which the structure belongs. When using such a scoring…
New federated learning method for structured models.
sBayFDNN bridges deep learning and functional data analysis for complex, structured data.
Paper proposes an efficient algorithm for learning sparse Bayesian networks from discrete high-dimensional data.
We present a novel hybrid algorithm for Bayesian network structure learning, called Hybrid HPC (H2PC). It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. It is based on a subroutine called HPC, that combines ideas from increment…
Bayesian network structure learning is often performed in a Bayesian setting, evaluating candidate structures using their posterior probabilities for a given data set. Score-based algorithms then use those posterior probabilities as an objective function and return the maximum a posteriori network as the learned model.…
New algorithm learns any part of a Bayesian network structure efficiently.
Novel hybrid method for Bayesian network structure learning reduces computational time without sacrificing accuracy.
In literature there are several studies on the performance of Bayesian network structure learning algorithms. The focus of these studies is almost always the heuristics the learning algorithms are based on, i.e. the maximisation algorithms (in score-based algorithms) or the techniques for learning the dependencies of e…
New algorithm learns causal structures by intersecting Markov blankets.
New algorithm optimizes MCMC sampling for structural dynamic models.
Bayesian method learns network structure from Gaussian process priors.
Finding an optimal parameter of a black-box function is important for searching stable material structures and finding optimal neural network structures, and Bayesian optimization algorithms are widely used for the purpose. However, most of existing Bayesian optimization algorithms can only handle vector data and canno…
Unified Bayesian framework for efficient off-policy evaluation and learning in large action spaces.
We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the …
We study the Bayesian model averaging approach to learning Bayesian network structures (DAGs) from data. We develop new algorithms including the first algorithm that is able to efficiently sample DAGs according to the exact structure posterior. The DAG samples can then be used to construct estimators for the posterior …
Proposes a new algorithm for learning continuous-time Bayesian network structures.
We give a new consistent scoring function for structure learning of Bayesian networks. In contrast to traditional approaches to scorebased structure learning, such as BDeu or MDL, the complexity penalty that we propose is data-dependent and is given by the probability that a conditional independence test correctly show…
Bayesian network structure learning is often performed in a Bayesian setting, by evaluating candidate structures using their posterior probabilities for a given data set. Score-based algorithms then use those posterior probabilities as an objective function and return the maximum a posteriori network as the learned mod…
Unified PAC-Bayesian framework for deep learning generalization.
Proposes an approach to ensure acyclic graphs in Bayesian structure learning.
Bayesian approach generalizes ADMM for federated learning.
Bayesian deep learning improves accuracy and calibration without sacrificing scalability.
In this paper, we provide a method to learn the directed structure of a Bayesian network using data. The data is accessed by making conditional probability queries to a black-box model. We introduce a notion of simplicity of representation of conditional probability tables for the nodes in the Bayesian network, that we…
Bayesian networks learn sub-population differences from data.