Search Results for author:
Found 7 papers, 3 papers with code
Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors
Interactive recommender systems have emerged as a promising paradigm to overcome the limitations of the primitive user feedback used by traditional recommender systems (e. g., clicks, item consumption, ratings).
Supervised Learning in the Presence of Concept Drift: A modelling framework
We present a modelling framework for the investigation of supervised learning in non-stationary environments.
How to compare adversarial robustness of classifiers from a global perspective
Adversarial robustness of machine learning models has attracted considerable attention over recent years.
Adversarial Robustness Curves
The existence of adversarial examples has led to considerable uncertainty regarding the trust one can justifiably put in predictions produced by automated systems.
When can unlabeled data improve the learning rate?
In semi-supervised classification, one is given access both to labeled and unlabeled data.
FRI -- Feature Relevance Intervals for Interpretable and Interactive Data Exploration
Most existing feature selection methods are insufficient for analytic purposes as soon as high dimensional data or redundant sensor signals are dealt with since features can be selected due to spurious effects or correlations rather than causal effects.
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces
We propose to phrase time series prediction as a regression problem and apply dissimilarity- or kernel-based regression techniques, such as 1-nearest neighbor, kernel regression and Gaussian process regression, which can be applied to graphs via graph kernels.
