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 dissertation uses ILP to learn Bayesian network structures efficiently.
We describe algorithms for learning Bayesian networks from a combination of user knowledge and statistical data. The algorithms have two components: a scoring metric and a search procedure. The scoring metric takes a network structure, statistical data, and a user's prior knowledge, and returns a score proportional to …
Bayesian approach improves AdaLoRA's performance and efficiency.
Recent reports have described that the equivalent sample size (ESS) in a Dirichlet prior plays an important role in learning Bayesian networks. This paper provides an asymptotic analysis of the marginal likelihood score for a Bayesian network. Results show that the ratio of the ESS and sample size determine the penalty…
CCE improves anomaly detection metrics by measuring both confidence and consistency.
This work introduces a new data-driven estimator for the Bayesian Cramér-Rao bound using score matching.
The paper improves experimental design by weighting diversity metrics with quality, leading to more diverse and effective discoveries.
It is often stated in papers tackling the task of inferring Bayesian network structures from data that there are these two distinct approaches: (i) Apply conditional independence tests when testing for the presence or otherwise of edges; (ii) Search the model space using a scoring metric. Here I argue that for complete…
Score based learning (SBL) is a promising approach for learning Bayesian networks in the discrete domain. However, when employing SBL in the continuous domain, one is either forced to move the problem to the discrete domain or use metrics such as BIC/AIC, and these approaches are often lacking. Discretization can have …
Bayesian approach improves image captioning quality metrics.
Several structure learning algorithms have been proposed towards discovering causal or Bayesian Network (BN) graphs. The validity of these algorithms tends to be evaluated by assessing the relationship between the learnt and the ground truth graph. However, there is no agreed scoring metric to determine this relationsh…
Bayesian classifier improves robustness with optimistic score ratio.
Recently several researchers have investigated techniques for using data to learn Bayesian networks containing compact representations for the conditional probability distributions (CPDs) stored at each node. The majority of this work has concentrated on using decision-tree representations for the CPDs. In addition, re…
Framework assesses autograders' reliability and biases.
We develop a scoring and classification procedure based on the PAC-Bayesian approach and the AUC (Area Under Curve) criterion. We focus initially on the class of linear score functions. We derive PAC-Bayesian non-asymptotic bounds for two types of prior for the score parameters: a Gaussian prior, and a spike-and-slab p…
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…
Bayesian optimization improves DRL for ESG portfolio management.
Bayesian framework uses unlabeled data to improve fairness assessment.
New method for Bayesian inference in infinite dimensions using SDMs.
Proposes a new Bayesian score for learning network structure from related datasets.
Bayesian scores improve structure learning in probabilistic circuits.
A new method for Bayesian inference using diffusion models.
Bayesian network modelling is a well adapted approach to study messy and highly correlated datasets which are very common in, e.g., systems epidemiology. A popular approach to learn a Bayesian network from an observational datasets is to identify the maximum a posteriori network in a search-and-score approach. Many sco…
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…
Many algorithms for score-based Bayesian network structure learning (BNSL), in particular exact ones, take as input a collection of potentially optimal parent sets for each variable in the data. Constructing such collections naively is computationally intensive since the number of parent sets grows exponentially with t…
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…
DrBO uses Bayesian optimization to learn DAGs more efficiently.
A new method for signal recovery in high dimensions using projections and diffusion models.
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…
MIRA scores assess conditional distribution accuracy using joint samples.
A classic approach for learning Bayesian networks from data is to identify a maximum a posteriori (MAP) network structure. In the case of discrete Bayesian networks, MAP networks are selected by maximising one of several possible Bayesian Dirichlet (BD) scores; the most famous is the Bayesian Dirichlet equivalent unifo…
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.…
Method solves Bayesian inverse problems in function space without assuming log-concavity.
Proposes a new method to measure epistemic uncertainty in Bayesian neural networks.
Study uses BNs to predict cryptocurrency prices, improving accuracy with discretisation.
EPICSCORE improves conformal scores by explicitly accounting for epistemic uncertainty.
ASVGD accelerates SVGD for efficient sampling.
A novel diffusion method for Bayesian posterior sampling with theoretical guarantees.
Proposes an approach to ensure acyclic graphs in Bayesian structure learning.
New method improves SBI efficiency and scalability.
Paper introduces a fast, robust, scalable method for detecting changes in data streams.
In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a scoring criterion that favors the simplest model able to represent the generative distribution exactly. Our results therefore hold whenever t…
We give a new consistent scoring function for structure learning of Bayesian networks. In contrast to traditional approaches to score-based 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 sho…
Improving Bayesian filtering with strictly proper scoring rules
Bayesian deep learning improves out-of-distribution detection but not always.
Robust Bayes-Assisted Conformal Prediction improves prediction set sizes.
ASVGD accelerates SVGD for efficient sampling from Gaussian targets.
A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as an optimization problem using the well-known score-and-search approach. However, selecting a single model (i.e., the best scoring BN) can b…