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Modeling the impact of out-of-schema questions in task-oriented dialog systems
Existing work on task-oriented dialog systems generally assumes that the interaction of users with the system is restricted to the information stored...
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Intention enhanced mixed attentive model for session-based recommendation
Session-based recommendation aims to generate recommendations for the next item of users’ interest based on a given session. In this manuscript, we...
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Improving graph-based recommendation with unraveled graph learning
Graph Collaborative Filtering (GraphCF) has emerged as a promising approach in recommendation systems, leveraging the inferential power of Graph...
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A practical approach to novel class discovery in tabular data
The problem of novel class discovery (NCD) consists in extracting knowledge from a labeled set of known classes to accurately partition an unlabeled...
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Bias-aware ranking from pairwise comparisons
Human feedback is often used, either directly or indirectly, as input to algorithmic decision making. However, humans are biased: if the algorithm...
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LoCoMotif: discovering time-warped motifs in time series
Time series motif discovery (TSMD) refers to the task of identifying patterns that occur multiple times (possibly with minor variations) in a time...
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On the impact of multi-dimensional local differential privacy on fairness
Automated decision systems are increasingly used to make consequential decisions in people’s lives. Due to the sensitivity of the manipulated data...
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Effective interpretable learning for large-scale categorical data
Large scale categorical datasets are ubiquitous in machine learning and the success of most deployed machine learning models rely on how effectively...
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WaveLSea: hel** experts interactively explore pattern mining search spaces
This article presents the method Wave Top-k Random-d Lineage Search (WaveLSea) which guides an expert through data mining results according to her...
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Active learning with biased non-response to label requests
Active learning can improve the efficiency of training prediction models by identifying the most informative new labels to acquire. However,...
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quant: a minimalist interval method for time series classification
We show that it is possible to achieve the same accuracy, on average, as the most accurate existing interval methods for time series classification...
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MSGNN: Multi-scale Spatio-temporal Graph Neural Network for epidemic forecasting
Infectious disease forecasting has been a key focus and proved to be crucial in controlling epidemic. A recent trend is to develop forecasting models...
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Intersectional fair ranking via subgroup divergence
Societal biases encoded in real-world data can contaminate algorithmic decisions, perpetuating preexisting inequalities in domains such as employment...
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The Hadamard decomposition problem
We introduce the Hadamard decomposition problem in the context of data analysis. The problem is to represent exactly or approximately a given matrix...
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Unsupervised feature based algorithms for time series extrinsic regression
Time Series Extrinsic Regression (TSER) involves using a set of training time series to form a predictive model of a continuous response variable...
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Decoupling Anomaly Discrimination and Representation Learning: Self-supervised Learning for Anomaly Detection on Attributed Graph
Anomaly detection on attributed graphs is a crucial topic for practical applications. Existing methods suffer from semantic mixture and imbalance...
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Towards more sustainable and trustworthy reporting in machine learning
With machine learning (ML) becoming a popular tool across all domains, practitioners are in dire need of comprehensive reporting on the...
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Interpretable representations in explainable AI: from theory to practice
Interpretable representations are the backbone of many explainers that target black-box predictive systems based on artificial intelligence and...
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Multiple hypergraph convolutional network social recommendation using dual contrastive learning
Due to the strong representation capabilities of graph structures in social networks, social relationships are often used to improve recommendation...
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Bake off redux: a review and experimental evaluation of recent time series classification algorithms
In 2017, a research paper (Bagnall et al. Data Mining and Knowledge Discovery 31(3):606-660.
2017 ) compared 18 Time Series Classification (TSC)...