Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Wen, Junfeng"'
In traditional statistical learning, data points are usually assumed to be independently and identically distributed (i.i.d.) following an unknown probability distribution. This paper presents a contrasting viewpoint, perceiving data points as interc
Externí odkaz:
http://arxiv.org/abs/2404.15518
In the traditional federated learning setting, a central server coordinates a network of clients to train one global model. However, the global model may serve many clients poorly due to data heterogeneity. Moreover, there may not exist a trusted cen
Externí odkaz:
http://arxiv.org/abs/2210.06597
Publikováno v:
ICML 2022
Approaches to policy optimization have been motivated from diverse principles, based on how the parametric model is interpreted (e.g. value versus policy representation) or how the learning objective is formulated, yet they share a common goal of max
Externí odkaz:
http://arxiv.org/abs/2206.08499
Publikováno v:
Nature Communications 14, 2899 (2023)
Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework that enables multi-institutional collaborations on decentralized data wit
Externí odkaz:
http://arxiv.org/abs/2111.11343
Actor-critic (AC) methods are ubiquitous in reinforcement learning. Although it is understood that AC methods are closely related to policy gradient (PG), their precise connection has not been fully characterized previously. In this paper, we explain
Externí odkaz:
http://arxiv.org/abs/2106.06932
Publikováno v:
In International Journal of Hydrogen Energy 2 January 2024 51 Part A:212-218
We consider the problem of approximating the stationary distribution of an ergodic Markov chain given a set of sampled transitions. Classical simulation-based approaches assume access to the underlying process so that trajectories of sufficient lengt
Externí odkaz:
http://arxiv.org/abs/2003.00722
Transfer in Reinforcement Learning (RL) refers to the idea of applying knowledge gained from previous tasks to solving related tasks. Learning a universal value function (Schaul et al., 2015), which generalizes over goals and states, has previously b
Externí odkaz:
http://arxiv.org/abs/2001.04025
Autor:
Pei, Qijun, Yu, Jiafeng, Qiu, Guanghao, Tan, Khai Chen, Wen, Junfeng, Yu, Yang, Wang, Jintao, Guo, Jiaquan, Guo, Jianping, Rao, Li, He, Teng, Chen, Ping
Publikováno v:
In Applied Catalysis B: Environment and Energy 5 November 2023 336
In many real-world applications, we want to exploit multiple source datasets of similar tasks to learn a model for a different but related target dataset -- e.g., recognizing characters of a new font using a set of different fonts. While most recent
Externí odkaz:
http://arxiv.org/abs/1909.05352