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- research-articleMay 2024
Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI Recommendation
WWW '24: Proceedings of the ACM Web Conference 2024Pages 3930–3939https://doi.org/10.1145/3589334.3645696In Location-based Social Networks (LBSNs), Point-of-Interest (POI) recommendation helps users discover interesting places. There is a trend to move from the conventional cloud-based model to on-device recommendations for privacy protection and reduced ...
- research-articleNovember 2023
Optimizing model-agnostic random subspace ensembles
Machine Language (MALE), Volume 113, Issue 2Pages 993–1042https://doi.org/10.1007/s10994-023-06427-5AbstractThis paper presents a model-agnostic ensemble approach for supervised learning. The proposed approach is based on a parametric version of Random Subspace, in which each base model is learned from a feature subset sampled according to a Bernoulli ...
- research-articleJuly 2023
Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 423–432https://doi.org/10.1145/3539618.3591733As an indispensable personalized service in Location-based Social Networks (LBSNs), the next Point-of-Interest (POI) recommendation aims to help people discover attractive and interesting places. Currently, most POI recommenders are based on the ...
Make Data Reliable: An Explanation-powered Cleaning on Malware Dataset Against Backdoor Poisoning Attacks
ACSAC '22: Proceedings of the 38th Annual Computer Security Applications ConferencePages 267–278https://doi.org/10.1145/3564625.3564661Machine learning (ML) based Malware classification provides excellent performance and has been deployed in various real-world applications. Training for malware classification often relies on crowdsourced threat feeds, which exposes a natural attack ...
- short-paperOctober 2022
MASR: A Model-Agnostic Sparse Routing Architecture for Arbitrary Order Feature Sharing in Multi-Task Learning
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementPages 3923–3927https://doi.org/10.1145/3511808.3557635Multi-task learning (MTL) has experienced rapid growth in recent years. A typical way of conducting MTL with deep neural networks (DNNs) is either establishing a sort of global feature sharing mechanism across all tasks or assigning each task an ...
- research-articleOctober 2022
MIC: Model-agnostic Integrated Cross-channel Recommender
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementPages 3400–3409https://doi.org/10.1145/3511808.3557081Semantically connecting users and items is a fundamental problem for the matching stage of an industrial recommender system. Recent advances in this topic are based on multi-channel retrieval to efficiently measure users' interest on items from the ...
- research-articleAugust 2022
A Model-Agnostic Approach to Differentially Private Topic Mining
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 1835–1845https://doi.org/10.1145/3534678.3539417Topic mining extracts patterns and insights from text data (e.g., documents, emails and product reviews), which can be used in various applications such as intent detection. However, topic mining can result in severe privacy threats to the users who have ...
- short-paperJuly 2022
Re-weighting Negative Samples for Model-Agnostic Matching
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 1823–1827https://doi.org/10.1145/3477495.3532053Recommender Systems (RS), as an efficient tool to discover users' interested items from a very large corpus, has attracted more and more attention from academia and industry. As the initial stage of RS, large-scale matching is fundamental yet ...
- research-articleNovember 2021
Spatial-Net: A Self-Adaptive and Model-Agnostic Deep Learning Framework for Spatially Heterogeneous Datasets
SIGSPATIAL '21: Proceedings of the 29th International Conference on Advances in Geographic Information SystemsPages 313–323https://doi.org/10.1145/3474717.3483970Knowledge discovery from spatial data is essential for many important societal applications including crop monitoring, solar energy estimation, traffic prediction and public health. This paper aims to tackle a key challenge posed by spatial data - the ...
- research-articleOctober 2021
ASTERYX: A model-Agnostic SaT-basEd appRoach for sYmbolic and score-based eXplanations
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge ManagementPages 120–129https://doi.org/10.1145/3459637.3482321The ever increasing complexity of machine learning techniques used more and more in practice, gives rise to the need to explain the outcomes of these models, often used as black-boxes. Explainable AI approaches are either numerical feature-based aiming ...
- research-articleJuly 2021
RelEx: A Model-Agnostic Relational Model Explainer
AIES '21: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and SocietyPages 1042–1049https://doi.org/10.1145/3461702.3462562In recent years, considerable progress has been made on improving the interpretability of machine learning models. This is essential, as complex deep learning models with millions of parameters produce state of the art performance, but it can be nearly ...