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Found 40 papers, 7 papers with code
Learning Exceptional Subgroups by End-to-End Maximizing KL-divergence
Finding and describing sub-populations that are exceptional regarding a target property has important applications in many scientific disciplines, from identifying disadvantaged demographic groups in census data to finding conductive molecules within gold nanoparticles.
Succinct Interaction-Aware Explanations
SHAP is a popular approach to explain black-box models by revealing the importance of individual features.
Data is Moody: Discovering Data Modification Rules from Process Event Logs
Although event logs are a powerful source to gain insight about the behavior of the underlying business process, existing work primarily focuses on finding patterns in the activity sequences of an event log, while ignoring event attribute data.
Finding Interpretable Class-Specific Patterns through Efficient Neural Search
Discovering patterns in data that best describe the differences between classes allows to hypothesize and reason about class-specific mechanisms.
Understanding and Mitigating Classification Errors Through Interpretable Token Patterns
Characterizing these errors in easily interpretable terms gives insight into whether a classifier is prone to making systematic errors, but also gives a way to act and improve the classifier.
Towards Concept-Aware Large Language Models
Concepts play a pivotal role in various human cognitive functions, including learning, reasoning and communication.
Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent
Addressing the interpretability problem of NMF on Boolean data, Boolean Matrix Factorization (BMF) uses Boolean algebra to decompose the input into low-rank Boolean factor matrices.
Preserving local densities in low-dimensional embeddings
We show, however, that these methods fail to reconstruct local properties, such as relative differences in densities (Fig.
All the World's a (Hyper)Graph: A Data Drama
We introduce Hyperbard, a dataset of diverse relational data representations derived from Shakespeare's plays.
Differentially Describing Groups of Graphs
How does neural connectivity in autistic children differ from neural connectivity in healthy children or autistic youths?
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors
Characterizing these errors in easily interpretable terms gives insight into whether a classifier is prone to making systematic errors, but also gives a way to act and improve the classifier.
Federated Learning from Small Datasets
Federated learning allows multiple parties to collaboratively train a joint model without sharing local data.
Picking Daisies in Private: Federated Learning from Small Datasets
Federated learning allows multiple parties to collaboratively train a joint model without sharing local data.
Graph Similarity Description: How Are These Graphs Similar?
We formalize this problem as a model selection task using the Minimum Description Length principle, capturing the similarity of the input graphs in a common model and the differences between them in transformations to individual models.
Factoring out prior knowledge from low-dimensional embeddings
Low-dimensional embedding techniques such as tSNE and UMAP allow visualizing high-dimensional data and therewith facilitate the discovery of interesting structure.
What's in the Box? Exploring the Inner Life of Neural Networks with Robust Rules
In particular, we consider activation values of a network for given data, and propose to mine noise-robust rules of the form $X \rightarrow Y$ , where $X$ and $Y$ are sets of neurons in different layers.
Data-driven equation for drug-membrane permeability across drugs and membranes
Beyond the widely recognized correlation with hydrophobicity, we additionally consider the functional relationship between passive permeation and acidity.
Chemical Physics Soft Condensed Matter
Discovering Reliable Causal Rules
Moreover, to discover reliable causal rules from a sample, we propose a conservative and consistent estimator of the causal effect, and derive an efficient and exact algorithm that maximises the estimator.
What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization
We apply our rules to three large KGs (NELL, DBpedia, and Yago), and tasks such as compression, various types of error detection, and identification of incomplete information.
Discovering Reliable Correlations in Categorical Data
This raises many questions, such as how to reliably and interpretably measure correlation between a multivariate set of attributes, how to do so without having to make assumptions on distribution of the data or the type of correlation, and, how to efficiently discover the top-most reliably correlated attribute sets from data.
Summarizing Data Succinctly with the Most Informative Itemsets
As we use the Maximum Entropy principle to obtain unbiased probabilistic models, and only include those itemsets that are most informative with regard to the current model, the summaries we construct are guaranteed to be both descriptive and non-redundant.
Testing Conditional Independence on Discrete Data using Stochastic Complexity
Testing for conditional independence is a core aspect of constraint-based causal discovery.
Comparing Apples and Oranges: Measuring Differences between Exploratory Data Mining Results
Our approach provides a means to study and tell differences between results of different exploratory data mining methods.
The Long and the Short of It: Summarising Event Sequences with Serial Episodes
An ideal outcome of pattern mining is a small set of informative patterns, containing no redundancy or noise, that identifies the key structure of the data at hand.
We Are Not Your Real Parents: Telling Causal from Confounded using MDL
We propose to do so using the Minimum Description Length (MDL) principle.
Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms
The reliable fraction of information is an attractive score for quantifying (functional) dependencies in high-dimensional data.
Causal Discovery by Telling Apart Parents and Children
We consider the problem of inferring the directed, causal graph from observational data, assuming no hidden confounders.
Telling Cause from Effect using MDL-based Local and Global Regression
We infer $X$ causes $Y$ in case it is shorter to describe $Y$ as a function of $X$ than the inverse direction.
Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups
That is, these sub-populations are exceptional with regard to the global distribution.
Discovering Reliable Approximate Functional Dependencies
As we want to be agnostic on the form of the dependence, we adopt an information-theoretic approach, and construct a reliable, bias correcting score that can be efficiently computed.
Causal Inference by Stochastic Complexity
The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as that direction with the lowest Kolmogorov complexity.
Causal Inference on Multivariate and Mixed-Type Data
Given data over the joint distribution of two random variables $X$ and $Y$, we consider the problem of inferring the most likely causal direction between $X$ and $Y$.
Efficiently Summarising Event Sequences with Rich Interleaving Patterns
Discovering the key structure of a database is one of the main goals of data mining.
Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery
Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find.
Beauty and Brains: Detecting Anomalous Pattern Co-Occurrences
Our world is filled with both beautiful and brainy people, but how often does a Nobel Prize winner also wins a beauty pageant?
Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns
We study how to obtain concise descriptions of discrete multivariate sequential data.
Universal Dependency Analysis
For practical use, such a measure should be universal in the sense that it captures correlation in subspaces of any dimensionality and allows to meaningfully compare correlation scores across different subspaces, regardless how many dimensions they have and what specific statistical properties their dimensions possess.
Canonical Divergence Analysis
We aim to analyze the relation between two random vectors that may potentially have both different number of attributes as well as realizations, and which may even not have a joint distribution.
Linear-time Detection of Non-linear Changes in Massively High Dimensional Time Series
Change detection in multivariate time series has applications in many domains, including health care and network monitoring.
Flexibly Mining Better Subgroups
For nominal attributes, this is relatively straightforward, as we can consider individual attribute values as binary features.
