Zobrazeno 1 - 10
of 94
pro vyhledávání: '"Yang, Hanfang"'
Asynchronous event sequence clustering aims to group similar event sequences in an unsupervised manner. Mixture models of temporal point processes have been proposed to solve this problem, but they often suffer from overfitting, leading to excessive
Externí odkaz:
http://arxiv.org/abs/2411.04397
Effective models for analysing and predicting pedestrian flow are important to ensure the safety of both pedestrians and other road users. These tools also play a key role in optimising infrastructure design and geometry and supporting the economic u
Externí odkaz:
http://arxiv.org/abs/2411.03360
Publikováno v:
ICML2024 Proceedings
Previous studies yielded discouraging results for item-level locally differentially private linear regression with $s^*$-sparsity assumption, where the minimax rate for $nm$ samples is $\mathcal{O}(s^{*}d / nm\varepsilon^2)$. This can be challenging
Externí odkaz:
http://arxiv.org/abs/2408.04313
While extensive research has explored the use of large language models (LLMs) for table-based reasoning, most approaches struggle with scalability when applied to large tables. To maintain the superior comprehension abilities of LLMs in these scenari
Externí odkaz:
http://arxiv.org/abs/2407.03061
We initiate the study of locally differentially private (LDP) learning with public features. We define semi-feature LDP, where some features are publicly available while the remaining ones, along with the label, require protection under local differe
Externí odkaz:
http://arxiv.org/abs/2405.13481
We consider the paradigm of unsupervised anomaly detection, which involves the identification of anomalies within a dataset in the absence of labeled examples. Though distance-based methods are top-performing for unsupervised anomaly detection, they
Externí odkaz:
http://arxiv.org/abs/2312.01046
Autor:
Ma, Yuheng, Yang, Hanfang
In this work, we investigate the problem of public data assisted non-interactive Local Differentially Private (LDP) learning with a focus on non-parametric classification. Under the posterior drift assumption, we for the first time derive the mini-ma
Externí odkaz:
http://arxiv.org/abs/2311.11369
Previous abstractive methods apply sequence-to-sequence structures to generate summary without a module to assist the system to detect vital mentions and relationships within a document. To address this problem, we utilize semantic graph to boost the
Externí odkaz:
http://arxiv.org/abs/2109.06046
In this paper, we propose an ensemble learning algorithm called \textit{under-bagging $k$-nearest neighbors} (\textit{under-bagging $k$-NN}) for imbalanced classification problems. On the theoretical side, by developing a new learning theory analysis
Externí odkaz:
http://arxiv.org/abs/2109.00531
In this paper, we propose a gradient boosting algorithm for large-scale regression problems called \textit{Gradient Boosted Binary Histogram Ensemble} (GBBHE) based on binary histogram partition and ensemble learning. From the theoretical perspective
Externí odkaz:
http://arxiv.org/abs/2106.01986