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pro vyhledávání: '"and, Leman"'
Given the GPS coordinates of a large collection of human agents over time, how can we model their mobility behavior toward effective anomaly detection (e.g. for bad-actor or malicious behavior detection) without any labeled data? Human mobility and t
Externí odkaz:
http://arxiv.org/abs/2410.01281
Outlier detection (OD) has a vast literature as it finds numerous applications in environmental monitoring, cybersecurity, finance, and medicine to name a few. Being an inherently unsupervised task, model selection is a key bottleneck for OD (both al
Externí odkaz:
http://arxiv.org/abs/2409.05672
Autor:
Binici, Kuluhan, Aggarwal, Shivam, Acar, Cihan, Pham, Nam Trung, Leman, Karianto, Lee, Gim Hee, Mitra, Tulika
Knowledge distillation (KD) is a key element in neural network compression that allows knowledge transfer from a pre-trained teacher model to a more compact student model. KD relies on access to the training dataset, which may not always be fully ava
Externí odkaz:
http://arxiv.org/abs/2408.13850
The astonishing successes of ML have raised growing concern for the fairness of modern methods when deployed in real world settings. However, studies on fairness have mostly focused on supervised ML, while unsupervised outlier detection (OD), with nu
Externí odkaz:
http://arxiv.org/abs/2408.13667
This paper presents a novel approach to level set estimation for any function/simulation with an arbitrary number of continuous inputs and arbitrary numbers of continuous responses. We present a method that uses existing data from computer model simu
Externí odkaz:
http://arxiv.org/abs/2407.05914
Autor:
Deforce, Boje, Lee, Meng-Chieh, Baesens, Bart, Asensio, Estefanía Serral, Yoo, Jaemin, Akoglu, Leman
Time series anomaly detection (TSAD) finds many applications such as monitoring environmental sensors, industry KPIs, patient biomarkers, etc. A two-fold challenge for TSAD is a versatile and unsupervised model that can detect various different types
Externí odkaz:
http://arxiv.org/abs/2404.02865
A major threat to the peer-review systems of computer science conferences is the existence of "collusion rings" between reviewers. In such collusion rings, reviewers who have also submitted their own papers to the conference work together to manipula
Externí odkaz:
http://arxiv.org/abs/2402.07860
Graph kernels used to be the dominant approach to feature engineering for structured data, which are superseded by modern GNNs as the former lacks learnability. Recently, a suite of Kernel Convolution Networks (KCNs) successfully revitalized graph ke
Externí odkaz:
http://arxiv.org/abs/2402.06087
Discrete diffusion models have seen a surge of attention with applications on naturally discrete data such as language and graphs. Although discrete-time discrete diffusion has been established for a while, only recently Campbell et al. (2022) introd
Externí odkaz:
http://arxiv.org/abs/2402.03701
Graph generation has been dominated by autoregressive models due to their simplicity and effectiveness, despite their sensitivity to ordering. Yet diffusion models have garnered increasing attention, as they offer comparable performance while being p
Externí odkaz:
http://arxiv.org/abs/2402.03687