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Found 8 papers, 2 papers with code
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
In particular, we are interested in discovering instance-level causal structures in an unsupervised manner.
Generative Perturbation Analysis for Probabilistic Black-Box Anomaly Attribution
We then propose a novel framework for probabilistic anomaly attribution that allows us to not only compute attribution scores as the predictive mean but also quantify the uncertainty of those scores.
Black-Box Anomaly Attribution
When the prediction of a black-box machine learning model deviates from the true observation, what can be said about the reason behind that deviation?
Anomaly Attribution with Likelihood Compensation
In either case, one would ideally want to compute a ``responsibility score'' indicative of the extent to which an input variable is responsible for the anomalous output.
Cardinality-Regularized Hawkes-Granger Model
In this paper, we propose a mathematically well-defined sparse causal learning framework based on a cardinality-regularized Hawkes process, which remedies the pathological issues of existing approaches.
Targeted Advertising on Social Networks Using Online Variational Tensor Regression
The proposed framework is designed to accommodate any number of feature vectors in the form of multi-mode tensor, thereby enabling to capture the heterogeneity that may exist over user preferences, products, and campaign strategies in a unified manner.
Directed Graph Auto-Encoders
We introduce a new class of auto-encoders for directed graphs, motivated by a direct extension of the Weisfeiler-Leman algorithm to pairs of node labels.
Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction
We consider the problem of variable group selection for least squares regression, namely, that of selecting groups of variables for best regression performance, leveraging and adhering to a natural grouping structure within the explanatory variables.
