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.
LP algorithm optimizes neural networks with architectural constraints.
Proposes a new algorithm for learning continuous-time Bayesian network structures.
Survey of integrating domain knowledge into DL models.
Proposes methods to add constraints to neural networks to improve stability and generalization.
Three classes of algorithms to learn the structure of Bayesian networks from data are common in the literature: constraint-based algorithms, which use conditional independence tests to learn the dependence structure of the data; score-based algorithms, which use goodness-of-fit scores as objective functions to maximise…
New method combines gradient optimization with constraint-based techniques for causal discovery.
Bayesian networks are probabilistic graphical models widely employed to understand dependencies in high dimensional data, and even to facilitate causal discovery. Learning the underlying network structure, which is encoded as a directed acyclic graph (DAG) is highly challenging mainly due to the vast number of possible…
The paper defines conditions for learning causal graphs from data with unobserved variables.
A new algorithm infers causal networks from data using topological thresholds.
bnlearn is an R package which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the snow package to improve their performance via parallel c…
Improved graph neural networks by separating feature aggregation and depth.
Constraint-based (CB) learning is a formalism for learning a causal network with a database D by performing a series of conditional-independence tests to infer structural information. This paper considers a new test of independence that combines ideas from Bayesian learning, Bayesian network inference, and classical hy…
Proposes a new method for GNNs that avoids iterative node state convergence.
New concept of illiquidity linked to credit risk, using Jarrow & Turnbull's analogy.
New algorithm reduces conditional independence tests needed for causal discovery.
A new method reduces CI tests for causal structure learning.
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…
We explore a new approach for training neural networks where all loss functions are replaced by hard constraints. The same approach is very successful in phase retrieval, where signals are reconstructed from magnitude constraints and general characteristics (sparsity, support, etc.). Instead of taking gradient steps, t…
Causal processes in nature may contain cycles, and real datasets may violate causal sufficiency as well as contain selection bias. No constraint-based causal discovery algorithm can currently handle cycles, latent variables and selection bias (CLS) simultaneously. I therefore introduce an algorithm called Cyclic Causal…
We propose a method for learning Markov network structures for continuous data without invoking any assumptions about the distribution of the variables. The method makes use of previous work on a non-parametric estimator for mutual information which is used to create a non-parametric test for multivariate conditional i…
New method for estimating local structure around target nodes in DAGs.
The study learns causal graphs from time series data using entropy measures.
Paper presents new algorithms for causal discovery with latent variables and overlapping datasets.
This work integrates domain knowledge into A*-based causal discovery methods.
Bayesian method estimates dynamics from near-optimal trajectories.
A new algorithm for robust causal discovery in small sample sizes.
Cluster-DAGs improve causal discovery with prior knowledge.
Two ML approaches learn local volatility surfaces from option prices, with GP being arbitrage-free.
Learning a Bayesian network structure from data is an NP-hard problem and thus exact algorithms are feasible only for small data sets. Therefore, network structures for larger networks are usually learned with various heuristics. Another approach to scaling up the structure learning is local learning. In local learning…
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…
New method for identifying causal relationships in financial time series data.
Discovering causal relationships from data is the ultimate goal of many research areas. Constraint based causal exploration algorithms, such as PC, FCI, RFCI, PC-simple, IDA and Joint-IDA have achieved significant progress and have many applications. A common problem with these methods is the high computational complex…
CCHM algorithm learns BN structure with latent variables, improving causal effect measurement.
New PCstar algorithm discovers causal structure of max-linear Bayesian networks.
DiCoLa recursively decomposes causal structure learning for latent variables.
We address the problem of Bayesian structure learning for domains with hundreds of variables by employing non-parametric bootstrap, recursively. We propose a method that covers both model averaging and model selection in the same framework. The proposed method deals with the main weakness of constraint-based learning--…
We consider constraint-based methods for causal structure learning, such as the PC-, FCI-, RFCI- and CCD- algorithms (Spirtes et al. (2000, 1993), Richardson (1996), Colombo et al. (2012), Claassen et al. (2013)). The first step of all these algorithms consists of the PC-algorithm. This algorithm is known to be order-d…
Novel hybrid method for Bayesian network structure learning reduces computational time without sacrificing accuracy.
This paper deals with chain graphs under the alternative Andersson-Madigan-Perlman (AMP) interpretation. In particular, we present a constraint based algorithm for learning an AMP chain graph a given probability distribution is faithful to. We also show that the extension of Meek's conjecture to AMP chain graphs does n…
Markov networks are widely studied and used throughout multivariate statistics and computer science. In particular, the problem of learning the structure of Markov networks from data without invoking chordality assumptions in order to retain expressiveness of the model class has been given a considerable attention in t…
We study the problem of discovering the simplest latent variable that can make two observed discrete variables conditionally independent. The minimum entropy required for such a latent is known as common entropy in information theory. We extend this notion to Renyi common entropy by minimizing the Renyi entropy of the …
New algorithm uncovers causal relations in non-stationary time series.
This paper deals with chain graphs under the Andersson-Madigan-Perlman (AMP) interpretation. In particular, we present a constraint based algorithm for learning an AMP chain graph a given probability distribution is faithful to. Moreover, we show that the extension of Meek's conjecture to AMP chain graphs does not hold…
New method identifies causal structure in exchangeable data.
New score-based methods identify causal structures with latent variables.
Scalable method learns context-specific models for hundreds of variables.
Paper tackles BNSL with IP, improving quality of solutions.
Fast nonparametric conditional independence testing via two-stage regression