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Found 20 papers, 2 papers with code
LoCoMotif: Discovering time-warped motifs in time series
All existing methods for TSMD have one or more of the following limitations: they only look for the two most similar occurrences of a pattern; they only look for patterns of a pre-specified, fixed length; they cannot handle variability along the time axis; and they only handle univariate time series.
Combining Predictions under Uncertainty: The Case of Random Decision Trees
A common approach to aggregate classification estimates in an ensemble of decision trees is to either use voting or to average the probabilities for each class.
COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series
This paper studies semi-supervised clustering in the context of time series.
COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints
This background knowledge is often obtained by allowing the clustering system to pose pairwise queries to the user: should these two elements be in the same cluster or not?
COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints
Clustering is inherently ill-posed: there often exist multiple valid clusterings of a single dataset, and without any additional information a clustering system has no way of knowing which clustering it should produce.
Demystifying Relational Latent Representations
This work addresses these issues and shows that (1) latent features created by clustering are interpretable and capture interesting properties of data; (2) they identify local regions of instances that match well with the label, which partially explains their benefit; and (3) although the number of latent features generated by this approach is large, often many of them are highly redundant and can be removed without hurting performance much.
Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation
The goal of unsupervised representation learning is to extract a new representation of data, such that solving many different tasks becomes easier.
An expressive dissimilarity measure for relational clustering using neighbourhood trees
It is the first measure to incorporate a wide variety of types of similarity, including similarity of attributes, similarity of relational context, and proximity in a hypergraph.
Constraint-Based Clustering Selection
Semi-supervised clustering methods incorporate a limited amount of supervision into the clustering process.
Theory reconstruction: a representation learning view on predicate invention
With this positional paper we present a representation learning view on predicate invention.
Predicate Logic as a Modeling Language: Modeling and Solving some Machine Learning and Data Mining Problems with IDP3
This paper provides a gentle introduction to problem solving with the IDP3 system.
Lifted Variable Elimination: Decoupling the Operators from the Constraint Language
The groups are defined by means of constraints, so the flexibility of the grouping is determined by the expressivity of the constraint language.
First-Order Decomposition Trees
Lifting attempts to speed up probabilistic inference by exploiting symmetries in the model.
Learning Relational Representations with Auto-encoding Logic Programs
This framework, inspired by the auto-encoding principle, uses first-order logic as a data representation language, and the mapping between the original and latent representation is done by means of logic programs instead of neural networks.
LazyBum: Decision tree learning using lazy propositionalization
Propositionalization is the process of summarizing relational data into a tabular (attribute-value) format.
Feature Interactions in XGBoost
Further, the improvement in the model structure can also lead to better interpretability.
SaDe: Learning Models that Provably Satisfy Domain Constraints
In many real world applications of machine learning, models have to meet certain domain-based requirements that can be expressed as constraints (e. g., safety-critical constraints in autonomous driving systems).
Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming
In this work, we represent product concepts using database queries and tackle two learning problems.
DeepSaDe: Learning Neural Networks that Guarantee Domain Constraint Satisfaction
In this paper, we present an approach to train neural networks which can enforce a wide variety of constraints and guarantee that the constraint is satisfied by all possible predictions.
AD-MERCS: Modeling Normality and Abnormality in Unsupervised Anomaly Detection
Moreover, by identifying patterns and conditions in (low-dimensional) subspaces, the anomaly detector can provide simple explanations of why something is considered an anomaly.
