Twelve papers at ECML-PKDD 2023

Three fields revolving around the question of how to cope with limited amounts of labeled data are Deep Active Learning (DAL), deep Constrained Clustering (CC), and Weakly Supervised Learning (WSL). DAL tackles the problem by adaptively posing the question of which data samples to annotate next in order to achieve the best incremental learning improvement, although it suffers from several limitations that hinder its deployment in practical settings. We point out how CC algorithms and WSL could be employed to overcome these limitations and increase the practical applicability of DAL research. Specifically, we discuss the opportunities to use the class discovery capabilities of CC and the possibility of further reducing human annotation efforts by utilizing WSL. We argue that the practical applicability of DAL algorithms will benefit from employing CC and WSL methods for the learning and labeling process. We inspect the overlaps between the three research areas and identify relevant and exciting research questions at the intersection of these areas.

This work introduces interpretable regional descriptors, or IRDs, for local, model-agnostic interpretations. IRDs are hyperboxes that describe how an observation’s feature values can be changed without affecting its prediction. They justify a prediction by providing a set of “even if” arguments (semi-factual explanations), and they indicate which features affect a prediction and whether pointwise biases or implausibilities exist. A concrete use case shows that this is valuable for both machine learning modelers and persons subject to a decision. We formalize the search for IRDs as an optimization problem and introduce a unifying framework for computing IRDs that covers desiderata, initialization techniques, and a post-processing method. We show how existing hyperbox methods can be adapted to fit into this unified framework. A benchmark study compares the methods based on several quality measures and identifies two strategies to improve IRDs.

Temporal knowledge graph completion (TKGC) aims to predict the missing links among the entities in a temporal knowledge graph (TKG). Most previous TKGC methods only consider predicting the missing links among the entities seen in the training set, while they are unable to achieve great performance in link prediction concerning newly-emerged unseen entities. Recently, a new task, i.e., TKG few-shot out-of-graph (OOG) link prediction, is proposed, where TKGC models are required to achieve great link prediction performance concerning newly-emerged entities that only have few-shot observed examples. In this work, we propose a TKGC method FITCARL that combines few-shot learning with reinforcement learning to solve this task. In FITCARL, an agent traverses through the whole TKG to search for the prediction answer. A policy network is designed to guide the search process based on the traversed path. To better address the data scarcity problem in the few-shot setting, we introduce a module that computes the confidence of each candidate action and integrate it into the policy for action selection. We also exploit the entity concept information with a novel concept regularizer to boost model performance. Experimental results show that FITCARL achieves stat-of-the-art performance on TKG few-shot OOG link prediction.

Node classification is one of the core tasks on attributed graphs, but successful graph learning solutions require sufficiently labeled data. To keep annotation costs low, active graph learning focuses on selecting the most qualitative subset of nodes that maximizes label efficiency. However, deciding which heuristic is best suited for an unlabeled graph to increase label efficiency is a persistent challenge. Existing solutions either neglect aligning the learned model and the sampling method or focus only on limited selection aspects. They are thus sometimes worse or only equally good as random sampling. In this work, we introduce a novel active graph learning approach called DiffusAL, showing significant robustness in diverse settings. Toward better transferability between different graph structures, we combine three independent scoring functions to identify the most informative node samples for labeling in a parameter-free way: i) Model Uncertainty, ii) Diversity Component, and iii) Node Importance computed via graph diffusion heuristics. Most of our calculations for acquisition and training can be pre-processed, making DiffusAL more efficient compared to approaches combining diverse selection criteria and similarly fast as simpler heuristics. Our experiments on various benchmark datasets show that, unlike previous methods, our approach significantly outperforms random selection in 100% of all datasets and labeling budgets tested.

Do we need active learning? The rise of strong deep semi-supervised methods raises doubt about the usability of active learning in limited labeled data settings. This is caused by results showing that combining semi-supervised learning (SSL) methods with a random selection for labeling can outperform existing active learning (AL) techniques. However, these results are obtained from experiments on well-established benchmark datasets that can overestimate the external validity. However, the literature lacks sufficient research on the performance of active semi-supervised learning methods in realistic data scenarios, leaving a notable gap in our understanding. Therefore we present three data challenges common in real-world applications: between-class imbalance, within-class imbalance, and between-class similarity. These challenges can hurt SSL performance due to confirmation bias. We conduct experiments with SSL and AL on simulated data challenges and find that random sampling does not mitigate confirmation bias and, in some cases, leads to worse performance than supervised learning. In contrast, we demonstrate that AL can overcome confirmation bias in SSL in these realistic settings. Our results provide insights into the potential of combining active and semi-supervised learning in the presence of common real-world challenges, which is a promising direction for robust methods when learning with limited labeled data in real-world applications.

This paper proposes a novel approach for modeling observational data in the form of expert ratings, which are commonly given on an ordered (numerical or ordinal) scale. In practice, such ratings are often biased, due to the expert’s preferences, psychological effects, etc. Our approach aims to rectify these biases, thereby preventing machine learning methods from transferring them to models trained on the data. To this end, we make use of so-called label smoothing, which allows for redistributing probability mass from the originally observed rating to other ratings, which are considered as possible corrections. This enables the incorporation of domain knowledge into the standard cross-entropy loss and leads to flexibly configurable models. Concretely, our method is realized for ordinal ratings and allows for arbitrary unimodal smoothings using a binary smoothing relation. Additionally, the paper suggests two practically motivated smoothing heuristics to address common biases in observational data, a time-based smoothing to handle concept drift and a class-wise smoothing based on class priors to mitigate data imbalance. The effectiveness of the proposed methods is demonstrated on four real-world goodwill assessment data sets of a car manufacturer with the aim of automating goodwill decisions. Overall, this paper presents a promising approach for modeling ordinal observational data that can improve decision-making processes and reduce reliance on human expertise.

Clustering heterogeneous data is an ongoing challenge in the data mining community. The most prevalent clustering methods are designed to process datasets with numerical features only, but often datasets consist of mixed numerical and categorical features. This requires new approaches capable of handling both kinds of data types. Further, the most relevant cluster structures are often hidden in only a few features. Thus, another key challenge is to detect those specific features automatically and abandon features not relevant for clustering. This paper proposes the subspace mixed-type clustering algorithm k-SubMix, which tackles both challenges. Its cost function can handle both numerical and categorical features while simultaneously identifying those with the biggest impact for a high-quality clustering result. Unlike other subspace mixed-type clustering methods, k-SubMix preserves inter-cluster comparability, as it is the first mixed-type approach that defines a common subspace for all clusters. Extensive experiments show that k-SubMix outperforms competitive methods and reduces the data’s complexity by a simultaneous dimensionality reduction.

Existing methods for explainable artificial intelligence (XAI), including popular feature importance measures such as SAGE, are mostly restricted to the batch learning scenario. However, machine learning is often applied in dynamic environments, where data arrives continuously and learning must be done in an online manner. Therefore, we propose iSAGE, a time- and memory-efficient incrementalization of SAGE, which is able to react to changes in the model as well as to drift in the data-generating process. We further provide efficient feature removal methods that break (interventional) and retain (observational) feature dependencies. Moreover, we formally analyze our explanation method to show that iSAGE adheres to similar theoretical properties as SAGE. Finally, we evaluate our approach in a thorough experimental analysis based on well-established data sets and data streams with concept drift.

Recent studies have demonstrated how to assess the stereotypical bias in pre-trained English language models. In this work, we extend this branch of research in multiple different dimensions by systematically investigating (a) mono- and multilingual models of (b) different underlying architectures with respect to their bias in (c) multiple different languages. To that end, we make use of the English StereoSet data set (Nadeem et al., 2021), which we semi-automatically translate into German, French, Spanish, and Turkish. We find that it is of major importance to conduct this type of analysis in a multilingual setting, as our experiments show a much more nuanced picture as well as notable differences from the English-only analysis. The main takeaways from our analysis are that mGPT-2 (partly) shows surprising anti-stereotypical behavior across languages, English (monolingual) models exhibit the strongest bias, and the stereotypes reflected in the data set are least present in Turkish models. Finally, we release our codebase alongside the translated data sets and practical guidelines for the semi-automatic translation to encourage a further extension of our work to other languages.

Deep active learning (DAL) seeks to reduce annotation costs by enabling the model to actively query instance annotations from which it expects to learn the most. Despite extensive research, there is currently no standardized evaluation protocol for transformer-based language models in the field of DAL. Diverse experimental settings lead to difficulties in comparing research and deriving recommendations for practitioners. To tackle this challenge, we propose the ACTIVEGLAE benchmark, a comprehensive collection of data sets and evaluation guidelines for assessing DAL. Our benchmark aims to facilitate and streamline the evaluation process of novel DAL strategies. Additionally, we provide an extensive overview of current practice in DAL with transformer-based language models. We identify three key challenges - data set selection, model training, and DAL settings - that pose difficulties in comparing query strategies. We establish baseline results through an extensive set of experiments as a reference point for evaluating future work. Based on our findings, we provide guidelines for researchers and practitioners.

With the introduction of ChatGPT, OpenAI made large language models (LLM) accessible to users with limited IT expertise. However, users with no background in natural language processing (NLP) might lack a proper understanding of LLMs. Thus the awareness of their inherent limitations, and therefore will take the systems’ output at face value. In this paper, we systematically analyse prompts and the generated responses to identify possible problematic issues with a special focus on gender biases, which users need to be aware of when processing the system’s output. We explore how ChatGPT reacts in English and German if prompted to answer from a female, male, or neutral perspective. In an in-depth investigation, we examine selected prompts and analyse to what extent responses differ if the system is prompted several times in an identical way. On this basis, we show that ChatGPT is indeed useful for helping non-IT users draft texts for their daily work. However, it is absolutely crucial to thoroughly check the system’s responses for biases as well as for syntactic and grammatical mistakes.

Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape. Markov chain Monte Carlo approaches asymptotically recover the true posterior but are considered prohibitively expensive for large modern architectures. Local methods, which have emerged as a popular alternative, focus on specific parameter regions that can be approximated by functions with tractable integrals. While these often yield satisfactory empirical results, they fail, by definition, to account for the multi-modality of the parameter posterior. Such coarse approximations can be detrimental in practical applications, notably safety-critical ones. In this work, we argue that the dilemma between exact-but-unaffordable and cheap-but-inexact approaches can be mitigated by exploiting symmetries in the posterior landscape. These symmetries, induced by neuron interchangeability and certain activation functions, manifest in different parameter values leading to the same functional output value. We show theoretically that the posterior predictive density in Bayesian neural networks can be restricted to a symmetry-free parameter reference set. By further deriving an upper bound on the number of Monte Carlo chains required to capture the functional diversity, we propose a straightforward approach for feasible Bayesian inference. Our experiments suggest that efficient sampling is indeed possible, opening up a promising path to accurate uncertainty quantification in deep learning.