Zobrazeno 1 - 10
of 78
pro vyhledávání: '"Shao, Minglai"'
Achieving the generalization of an invariant classifier from training domains to shifted test domains while simultaneously considering model fairness is a substantial and complex challenge in machine learning. Existing methods address the problem of
Externí odkaz:
http://arxiv.org/abs/2408.09312
Product review generation is an important task in recommender systems, which could provide explanation and persuasiveness for the recommendation. Recently, Large Language Models (LLMs, e.g., ChatGPT) have shown superior text modeling and generating a
Externí odkaz:
http://arxiv.org/abs/2407.07487
Fairness-aware domain generalization (FairDG) has emerged as a critical challenge for deploying trustworthy AI systems, particularly in scenarios involving distribution shifts. Traditional methods for addressing fairness have failed in domain general
Externí odkaz:
http://arxiv.org/abs/2406.09495
Influence maximization (IM) is the problem of identifying a limited number of initial influential users within a social network to maximize the number of influenced users. However, previous research has mostly focused on individual information propag
Externí odkaz:
http://arxiv.org/abs/2403.18866
Traditional machine learning methods heavily rely on the independent and identically distribution assumption, which imposes limitations when the test distribution deviates from the training distribution. To address this crucial issue, out-of-distribu
Externí odkaz:
http://arxiv.org/abs/2403.16334
Supervised fairness-aware machine learning under distribution shifts is an emerging field that addresses the challenge of maintaining equitable and unbiased predictions when faced with changes in data distributions from source to target domains. In r
Externí odkaz:
http://arxiv.org/abs/2402.01327
Recently slot filling has witnessed great development thanks to deep learning and the availability of large-scale annotated data. However, it poses a critical challenge to handle a novel domain whose samples are never seen during training. The recogn
Externí odkaz:
http://arxiv.org/abs/2310.15294
Recognizing the prevalence of domain shift as a common challenge in machine learning, various domain generalization (DG) techniques have been developed to enhance the performance of machine learning systems when dealing with out-of-distribution (OOD)
Externí odkaz:
http://arxiv.org/abs/2309.13005
For example, in machine translation tasks, to achieve bidirectional translation between two languages, the source corpus is often used as the target corpus, which involves the training of two models with opposite directions. The question of which one
Externí odkaz:
http://arxiv.org/abs/2308.16879
As the basic element of graph-structured data, node has been recognized as the main object of study in graph representation learning. A single node intuitively has multiple node-centered subgraphs from the whole graph (e.g., one person in a social ne
Externí odkaz:
http://arxiv.org/abs/2308.16441