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of 2 843
pro vyhledávání: '"Liu, Xuefeng"'
To address data heterogeneity, the key strategy of Personalized Federated Learning (PFL) is to decouple general knowledge (shared among clients) and client-specific knowledge, as the latter can have a negative impact on collaboration if not removed.
Externí odkaz:
http://arxiv.org/abs/2406.19931
The objective of drug discovery is to identify chemical compounds that possess specific pharmaceutical properties toward a binding target. Existing large language models (LLMS) can achieve high token matching scores in terms of likelihood for molecul
Externí odkaz:
http://arxiv.org/abs/2406.07025
Federated Semi-Supervised Learning (FSSL) leverages both labeled and unlabeled data on clients to collaboratively train a model.In FSSL, the heterogeneous data can introduce prediction bias into the model, causing the model's prediction to skew towar
Externí odkaz:
http://arxiv.org/abs/2405.19789
This paper studies Learning from Imperfect Human Feedback (LIHF), motivated by humans' potential irrationality or imperfect perception of true preference. We revisit the classic dueling bandit problem as a model of learning from comparative human fee
Externí odkaz:
http://arxiv.org/abs/2405.11204
While reinforcement learning (RL) has shown promising performance, its sample complexity continues to be a substantial hurdle, restricting its broader application across a variety of domains. Imitation learning (IL) utilizes oracles to improve sample
Externí odkaz:
http://arxiv.org/abs/2310.01737
Personalized federated learning (PFL) reduces the impact of non-independent and identically distributed (non-IID) data among clients by allowing each client to train a personalized model when collaborating with others. A key question in PFL is to dec
Externí odkaz:
http://arxiv.org/abs/2309.11103
Personalized federated learning (PFL) is a popular framework that allows clients to have different models to address application scenarios where clients' data are in different domains. The typical model of a client in PFL features a global encoder tr
Externí odkaz:
http://arxiv.org/abs/2307.13995
We propose 3Deformer, a general-purpose framework for interactive 3D shape editing. Given a source 3D mesh with semantic materials, and a user-specified semantic image, 3Deformer can accurately edit the source mesh following the shape guidance of the
Externí odkaz:
http://arxiv.org/abs/2307.09892
Reinforcement learning (RL) has made significant strides in various complex domains. However, identifying an effective policy via RL often necessitates extensive exploration. Imitation learning aims to mitigate this issue by using expert demonstratio
Externí odkaz:
http://arxiv.org/abs/2306.10259