Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Ke, Shuqi"'
Differentially private (DP) machine learning pipelines typically involve a two-phase process: non-private pre-training on a public dataset, followed by fine-tuning on private data using DP optimization techniques. In the DP setting, it has been obser
Externí odkaz:
http://arxiv.org/abs/2402.18905
Autor:
Zhang, Shenao, Zheng, Sirui, Ke, Shuqi, Liu, Zhihan, Jin, Wanxin, Yuan, Jianbo, Yang, Yingxiang, Yang, Hongxia, Wang, Zhaoran
Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback. However, RL algorithms may require extensive trial-and-error interactions to collect usef
Externí odkaz:
http://arxiv.org/abs/2402.16181
Large language models (LLMs) demonstrate impressive reasoning abilities, but translating reasoning into actions in the real world remains challenging. In particular, it remains unclear how to complete a given task provably within a minimum number of
Externí odkaz:
http://arxiv.org/abs/2309.17382
Federated Learning (FL) is a distributed machine learning paradigm where clients collaboratively train a model using their local (human-generated) datasets. While existing studies focus on FL algorithm development to tackle data heterogeneity across
Externí odkaz:
http://arxiv.org/abs/2211.07816
In cross-silo federated learning, clients (e.g., organizations) train a shared global model using local data. However, due to privacy concerns, the clients may not contribute enough data points during training. To address this issue, we propose a gen
Externí odkaz:
http://arxiv.org/abs/2203.03885
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.