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Found 15 papers, 3 papers with code
Clarity: an improved gradient method for producing quality visual counterfactual explanations
Visual counterfactual explanations identify modifications to an image that would change the prediction of a classifier.
Delta-Closure Structure for Studying Data Distribution
In this paper, we revisit pattern mining and study the distribution underlying a binary dataset thanks to the closure structure which is based on passkeys, i. e., minimum generators in equivalence classes robust to noise.
Reducing Unintended Bias of ML Models on Tabular and Textual Data
Unintended biases in machine learning (ML) models are among the major concerns that must be addressed to maintain public trust in ML.
A Bayesian Convolutional Neural Network for Robust Galaxy Ellipticity Regression
We show that while a convolutional network can be trained to correctly estimate well calibrated aleatoric uncertainty, -- the uncertainty due to the presence of noise in the images -- it is unable to generate a trustworthy ellipticity distribution when exposed to previously unseen data (i. e. here, blended scenes).
A Bayesian Neural Network based on Dropout Regulation
Bayesian Neural Networks (BNN) have recently emerged in the Deep Learning world for dealing with uncertainty estimation in classification tasks, and are used in many application domains such as astrophysics, autonomous driving... BNN assume a prior over the weights of a neural network instead of point estimates, enabling in this way the estimation of both aleatoric and epistemic uncertainty of the model prediction. Moreover, a particular type of BNN, namely MC Dropout, assumes a Bernoulli distribution on the weights by using Dropout. Several attempts to optimize the dropout rate exist, e. g. using a variational approach. In this paper, we present a new method called "Dropout Regulation" (DR), which consists of automatically adjusting the dropout rate during training using a controller as used in automation. DR allows for a precise estimation of the uncertainty which is comparable to the state-of-the-art while remaining simple to implement.
Mint: MDL-based approach for Mining INTeresting Numerical Pattern Sets
Pattern mining is well established in data mining research, especially for mining binary datasets.
Discovering alignment relations with Graph Convolutional Networks: a biomedical case study
In this article, we propose to match nodes within a knowledge graph by (i) learning node embeddings with Graph Convolutional Networks such that similar nodes have low distances in the embedding space, and (ii) clustering nodes based on their embeddings, in order to suggest alignment relations between nodes of a same cluster.
Making ML models fairer through explanations: the case of LimeOut
To illustrate, we will revisit the case of "LimeOut" that was proposed to tackle "process fairness", which measures a model's reliance on sensitive or discriminatory features.
Discovery data topology with the closure structure. Theoretical and practical aspects
The closure structure allows one to understand the topology of the dataset in the whole and the inherent complexity of the data.
Tackling scalability issues in mining path patterns from knowledge graphs: a preliminary study
Features mined from knowledge graphs are widely used within multiple knowledge discovery tasks such as classification or fact-checking.
LimeOut: An Ensemble Approach To Improve Process Fairness
To achieve both, we draw inspiration from "dropout" techniques in neural based approaches, and propose a framework that relies on "feature drop-out" to tackle process fairness.
Knowledge-Based Matching of $n$-ary Tuples
In particular, units should be matched within and across sources, and their level of relatedness should be classified into equivalent, more specific, or similar.
Mining Best Closed Itemsets for Projection-antimonotonic Constraints in Polynomial Time
One of the first constraints proposed in pattern mining is support (frequency) of a pattern in a dataset.
Fast Generation of Best Interval Patterns for Nonmonotonic Constraints
In this paper we consider stability and $\Delta$-measure, which are nonmonotonic constraints, and apply them to interval tuple datasets.
On mining complex sequential data by means of FCA and pattern structures
Pattern structures are used for mining complex data (such as sequences or graphs) and are based on a subsumption operation, which in our case is defined with respect to the partial order on sequences.
