Zobrazeno 1 - 10
of 157
pro vyhledávání: '"Sun, Haoyuan"'
Adaptive control achieves concurrent parameter learning and stable control under uncertainties that are linearly parameterized with known nonlinear features. Nonetheless, it is often difficult to obtain such nonlinear features. To address this diffic
Externí odkaz:
http://arxiv.org/abs/2407.20165
Autor:
Sun, Haoyuan, Wu, Zihao, Xia, Bo, Chang, Pu, Dong, Zibin, Yuan, Yifu, Chang, Yongzhe, Wang, Xueqian
The success of artificial neural networks (ANNs) hinges greatly on the judicious selection of an activation function, introducing non-linearity into network and enabling them to model sophisticated relationships in data. However, the search of activa
Externí odkaz:
http://arxiv.org/abs/2405.12954
Offline reinforcement learning (RL) provides a promising approach to avoid costly online interaction with the real environment. However, the performance of offline RL highly depends on the quality of the datasets, which may cause extrapolation error
Externí odkaz:
http://arxiv.org/abs/2405.04307
Autor:
Sun, Haoyuan, Jadbabaie, Ali
The focus of this paper is on linear system identification in the setting where it is known that the underlying partially-observed linear dynamical system lies within a finite collection of known candidate models. We first consider the problem of ide
Externí odkaz:
http://arxiv.org/abs/2404.08120
When machine learning models are trained on synthetic data and then deployed on real data, there is often a performance drop due to the distribution shift between synthetic and real data. In this paper, we introduce a new ensemble strategy for traini
Externí odkaz:
http://arxiv.org/abs/2310.09729
Autor:
Sun, Haoyuan
Inspired by the remarkable performance of deep neural networks, understanding the generalization performance of overparameterized models and the effect of optimization algorithms on it has become an increasingly popular question. In particular, there
Inspired by the remarkable success of large neural networks, there has been significant interest in understanding the generalization performance of over-parameterized models. Substantial efforts have been invested in characterizing how optimization a
Externí odkaz:
http://arxiv.org/abs/2306.13853
The study of market equilibria is central to economic theory, particularly in efficiently allocating scarce resources. However, the computation of equilibrium prices at which the supply of goods matches their demand typically relies on having access
Externí odkaz:
http://arxiv.org/abs/2303.11522
Publikováno v:
Advances in Neural Information Processing Systems 35 (NeurIPS 2022)
Driven by the empirical success and wide use of deep neural networks, understanding the generalization performance of overparameterized models has become an increasingly popular question. To this end, there has been substantial effort to characterize
Externí odkaz:
http://arxiv.org/abs/2205.12808
Autor:
Sun, Haoyuan
We study the performance of an online learner under a framework in which it receives partial information from a dynamic, and potentially adversarial, environment at discrete time steps. The goal of this learner is to minimize the sum of costs incurre