Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Li, Fengpei"'
Autor:
Li, Fengpei, Chen, Haoxian, Lin, Jiahe, Gupta, Arkin, Tan, Xiaowei, Xu, Gang, Nevmyvaka, Yuriy, Capponi, Agostino, Lam, Henry
Despite being an essential tool across engineering and finance, Monte Carlo simulation can be computationally intensive, especially in large-scale, path-dependent problems that hinder straightforward parallelization. A natural alternative is to repla
Externí odkaz:
http://arxiv.org/abs/2412.11257
Autor:
Chen, Yu, Li, Fengpei, Schneider, Anderson, Nevmyvaka, Yuriy, Amarasingham, Asohan, Lam, Henry
Many event sequence data exhibit mutually exciting or inhibiting patterns. Reliable detection of such temporal dependency is crucial for scientific investigation. The de facto model is the Multivariate Hawkes Process (MHP), whose impact function natu
Externí odkaz:
http://arxiv.org/abs/2305.18412
Autor:
Chen, Yu, Deng, Wei, Fang, Shikai, Li, Fengpei, Yang, Nicole Tianjiao, Zhang, Yikai, Rasul, Kashif, Zhe, Shandian, Schneider, Anderson, Nevmyvaka, Yuriy
The Schr\"odinger bridge problem (SBP) is gaining increasing attention in generative modeling and showing promising potential even in comparison with the score-based generative models (SGMs). SBP can be interpreted as an entropy-regularized optimal t
Externí odkaz:
http://arxiv.org/abs/2305.07247
Market impact is an important problem faced by large institutional investor and active market participant. In this paper, we rigorously investigate whether price trajectory data from the metaorder increases the efficiency of estimation, from an asymp
Externí odkaz:
http://arxiv.org/abs/2205.13423
Autor:
Li, Fengpei
Stochastic methods are indispensable to the modeling, analysis and design of complex systems involving randomness. In this thesis, we show how simulation techniques and simulation-based computational methods can be applied to a wide spectrum of appli
Autor:
Xu, Mengdi, Huang, Peide, Li, Fengpei, Zhu, Jiacheng, Qi, Xuewei, Oguchi, Kentaro, Huang, Zhiyuan, Lam, Henry, Zhao, Ding
Evaluating rare but high-stakes events is one of the main challenges in obtaining reliable reinforcement learning policies, especially in large or infinite state/action spaces where limited scalability dictates a prohibitively large number of testing
Externí odkaz:
http://arxiv.org/abs/2106.10566
Autor:
Lam, Henry, Li, Fengpei
We investigate the feasibility of sample average approximation (SAA) for general stochastic optimization problems, including two-stage stochastic programming without the relatively complete recourse assumption. Instead of analyzing problems with spec
Externí odkaz:
http://arxiv.org/abs/2103.01324
In many learning problems, the training and testing data follow different distributions and a particularly common situation is the \textit{covariate shift}. To correct for sampling biases, most approaches, including the popular kernel mean matching (
Externí odkaz:
http://arxiv.org/abs/1910.06324
Autor:
Lam, Henry, Li, Fengpei
We consider optimization problems with uncertain constraints that need to be satisfied probabilistically. When data are available, a common method to obtain feasible solutions for such problems is to impose sampled constraints, following the so-calle
Externí odkaz:
http://arxiv.org/abs/1904.11626
We construct an unbiased estimator for function value evaluated at the solution of a partial differential equation with random coefficients. We show that the variance and expected computational cost of our estimator are finite and our estimator is un
Externí odkaz:
http://arxiv.org/abs/1806.03362