Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Albert Bifet,Jesse Davis,Indr? ?liobait? Confer

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FairFlow: An Automated Approach to?Model-Based Counterfactual Data Augmentation for NLPhese inherent biases often result in detrimental effects in various applications. Counterfactual Data Augmentation (CDA), which seeks to balance demographic attributes in training data, has been a widely adopted approach to mitigate bias in natural language processing. However, many existing CDA app

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GrINd: Grid Interpolation Network for?Scattered Observationsntific domains. Traditional methods rely on dense grid-structured data, limiting their applicability in scenarios with sparse observations. To address this challenge, we introduce GrINd (Grid Interpolation Network for Scattered Observations), a novel network architecture that leverages the high-perf

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MEGA: Multi-encoder GNN Architecture for?Stronger Task Collaboration and?Generalizationtive node representations. However, the reliance on a single pretext task often constrains generalization across various downstream tasks and datasets. Recent advancements in multi-task learning on graphs aim to tackle this limitation by integrating multiple pretext tasks, framing the problem as a m

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MetaQuRe: Meta-learning from?Model Quality and?Resource Consumptiona pivotal role in neural architecture search, it is less pronounced by classical AutoML approaches. In fact, they generally focus on only maximizing predictive quality and disregard the importance of finding resource-efficient solutions. To push resource awareness further, our work explicitly explor

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Propagation Structure-Semantic Transfer Learning for?Robust Fake News Detections detection methods primarily learn the semantic features from news content or integrate structural features from propagation. However, in practical scenarios, due to the semantic ambiguity of informal language and unreliable user interactive behaviors on social media, there are inherent semantic an

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Exploring Contrastive Learning for?Long-Tailed Multi-label Text Classificationge arises from the inherent complexity of the task, which is shaped by two key factors: the intricate connections between labels and the widespread long-tailed distribution of the data. To overcome this issue, one potential approach involves integrating supervised contrastive learning with classical

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Simultaneous Linear Connectivity of?Neural Networks Modulo Permutatione symmetries contribute to the non-convexity of the networks’ loss landscapes, since linearly interpolating between two permuted versions of a trained network tends to encounter a high loss barrier. Recent work has argued that permutation symmetries are the . sources of non-convexity, meaning there

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Fast Fishing: Approximating , for?Efficient and?Scalable Deep Active Image Classificationsher Information, has demonstrated impressive performance across various datasets. However, .’s high computational and memory requirements hinder its applicability on large-scale classification tasks, resulting in current research neglecting . in their evaluation. This paper introduces two methods t

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