Zobrazeno 1 - 10
of 193 736
pro vyhledávání: '"Kuang, A A"'
Conformal prediction is a valuable tool for quantifying predictive uncertainty of machine learning models. However, its applicability relies on the assumption of data exchangeability, a condition which is often not met in real-world scenarios. In thi
Externí odkaz:
http://arxiv.org/abs/2412.19318
We consider game-theoretically secure distributed protocols for coalition games that approximate the Shapley value with small multiplicative error. Since all known existing approximation algorithms for the Shapley value are randomized, it is a challe
Externí odkaz:
http://arxiv.org/abs/2412.19192
People who are blind or have low vision (BLV) encounter numerous challenges in their daily lives and work. To support them, various haptic assistive tools have been developed. Despite these advancements, the effective utilization of these tools -- in
Externí odkaz:
http://arxiv.org/abs/2412.19105
Autor:
Zhang, Hao, Yu, Dongjun, Zhang, Lei, Rong, Guoping, Yu, Yongda, Shen, Haifeng, Zhang, He, Shao, Dong, Kuang, Hongyu
Log statements have become an integral part of modern software systems. Prior research efforts have focused on supporting the decisions of placing log statements, such as where/what to log, while automated generation or completion of log statements h
Externí odkaz:
http://arxiv.org/abs/2412.18835
Autor:
Jiang, Zhonghua, Xu, Jimin, Zhang, Shengyu, Shen, Tao, Li, Jiwei, Kuang, Kun, Cai, Haibin, Wu, Fei
Federated learning (FL) is a promising technology for data privacy and distributed optimization, but it suffers from data imbalance and heterogeneity among clients. Existing FL methods try to solve the problems by aligning client with server model or
Externí odkaz:
http://arxiv.org/abs/2412.18904
Autor:
Wang, Yan, Wu, Jin-Lei, Jiao, Ya-Feng, Lu, Tian-Xiang, Zhang, Hui-Lai, Jiang, Li-Ying, Kuang, Le-Man, Jing, Hui
Tripartite entanglement as a remarkable resource in quantum information science has been extensively investigated in hybrid quantum systems, whereas it is generally weak and prone to be suppressed by noise, restricting its practical application in qu
Externí odkaz:
http://arxiv.org/abs/2412.18732
Autor:
Demarne, Mathieu, Cilimdzic, Miso, Falkowski, Tom, Johnson, Timothy, Gramling, Jim, Kuang, Wei, Hou, Hoobie, Aryan, Amjad, Subramaniam, Gayatri, Lee, Kenny, Mejia, Manuel, Liu, Lisa, Vermareddy, Divya
We present ARCAS (Automated Root Cause Analysis System), a diagnostic platform based on a Domain Specific Language (DSL) built for fast diagnostic implementation and low learning curve. Arcas is composed of a constellation of automated troubleshootin
Externí odkaz:
http://arxiv.org/abs/2412.15374
Dealing with missing data poses significant challenges in predictive analysis, often leading to biased conclusions when oversimplified assumptions about the missing data process are made. In cases where the data are missing not at random (MNAR), join
Externí odkaz:
http://arxiv.org/abs/2412.14946
Ads demand forecasting for Walmart's ad products plays a critical role in enabling effective resource planning, allocation, and management of ads performance. In this paper, we introduce a comprehensive demand forecasting system that tackles hierarch
Externí odkaz:
http://arxiv.org/abs/2412.14718
Noisy labels are both inevitable and problematic in machine learning methods, as they negatively impact models' generalization ability by causing overfitting. In the context of learning with noise, the transition matrix plays a crucial role in the de
Externí odkaz:
http://arxiv.org/abs/2412.13516