Search Results for author: Fabian Hinder

Found 13 papers, 6 papers with code

Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)

1 code implementation ICML 2020 Fabian Hinder, André Artelt, CITEC Barbara Hammer

The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time; as a consequence machine learning models may become inaccurate and need adjustment.

On the Change of Decision Boundaries and Loss in Learning with Concept Drift

no code implementations2 Dec 2022 Fabian Hinder, Valerie Vaquet, Johannes Brinkrolf, Barbara Hammer

More precisely, we relate a change of the ITTE to the presence of real drift, i. e., a changed posterior, and to a change of the training result under the assumption of optimality.

Precise Change Point Detection using Spectral Drift Detection

no code implementations13 May 2022 Fabian Hinder, André Artelt, Valerie Vaquet, Barbara Hammer

The notion of concept drift refers to the phenomenon that the data generating distribution changes over time; as a consequence machine learning models may become inaccurate and need adjustment.

Change Point Detection

The SAME score: Improved cosine based bias score for word embeddings

no code implementations28 Mar 2022 Sarah Schröder, Alexander Schulz, Philip Kenneweg, Robert Feldhans, Fabian Hinder, Barbara Hammer

Furthermore, we thoroughly investigate the existing cosine-based scores and their limitations in order to show why these scores fail to report biases in some situations.

Sentence Embeddings Word Embeddings

Suitability of Different Metric Choices for Concept Drift Detection

no code implementations19 Feb 2022 Fabian Hinder, Valerie Vaquet, Barbara Hammer

In this paper, we analyze structural properties of the drift induced signals in the context of different metrics.

Evaluating Metrics for Bias in Word Embeddings

no code implementations15 Nov 2021 Sarah Schröder, Alexander Schulz, Philip Kenneweg, Robert Feldhans, Fabian Hinder, Barbara Hammer

However, lately some works have raised doubts about these metrics showing that even though such metrics report low biases, other tests still show biases.

Sentence Embeddings Word Embeddings

Contrastive Explanations for Explaining Model Adaptations

1 code implementation6 Apr 2021 André Artelt, Fabian Hinder, Valerie Vaquet, Robert Feldhans, Barbara Hammer

We also propose a method for automatically finding regions in data space that are affected by a given model adaptation and thus should be explained.

Decision Making

Evaluating Robustness of Counterfactual Explanations

1 code implementation3 Mar 2021 André Artelt, Valerie Vaquet, Riza Velioglu, Fabian Hinder, Johannes Brinkrolf, Malte Schilling, Barbara Hammer

Counterfactual explanations explain a behavior to the user by proposing actions -- as changes to the input -- that would cause a different (specified) behavior of the system.

Decision Making Fairness

Analysis of Drifting Features

1 code implementation1 Dec 2020 Fabian Hinder, Jonathan Jakob, Barbara Hammer

The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time.

Counterfactual Explanations of Concept Drift

no code implementations23 Jun 2020 Fabian Hinder, Barbara Hammer

The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time; as a consequence machine learning models may become inaccurate and need adjustment.

Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information

no code implementations10 Dec 2019 Lukas Pfannschmidt, Jonathan Jakob, Fabian Hinder, Michael Biehl, Peter Tino, Barbara Hammer

In this contribution, we focus on feature selection paradigms, which enable us to uncover relevant factors of a given regularity based on a sparse model.

regression

A probability theoretic approach to drifting data in continuous time domains

1 code implementation4 Dec 2019 Fabian Hinder, André Artelt, Barbara Hammer

The notion of drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time.

Change Point Detection

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