12 citations · 19 across the 11 of their papers we have counts for
11 papers
Reweighting Improves Conditional Risk Bounds
Yikai Zhang, Jiahe Lin, Fengpei Li +4
In this work, we study the weighted empirical risk minimization (weighted ERM) schema, in which an additional data-dependent weight function is incorporated when the empirical risk…
Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate
Fengpei Li, Haoxian Chen, Jiahe Lin +7
For many complex simulation tasks spanning areas such as healthcare, engineering, and finance, Monte Carlo (MC) methods are invaluable due to their unbiased estimates and precise e…
Short-term Temporal Dependency Detection under Heterogeneous Event Dynamic with Hawkes Processes
Yu Chen, Fengpei Li, Anderson Schneider +3
Many event sequence data exhibit mutually exciting or inhibiting patterns. Reliable detection of such temporal dependency is crucial for scientific investigation. The de facto mode…
Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation
Yu Chen, Wei Deng, Shikai Fang +7
The Schrödinger bridge problem (SBP) is gaining increasing attention in generative modeling and showing promising potential even in comparison with the score-based generative model…
Do price trajectory data increase the efficiency of market impact estimation?
Fengpei Li, Vitalii Ihnatiuk, Ryan Kinnear +2
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 fr…
Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling
Mengdi Xu, Peide Huang, Fengpei Li +6
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 wher…
General Feasibility Bounds for Sample Average Approximation via Vapnik-Chervonenkis Dimension
Henry Lam, Fengpei Li
We investigate the feasibility of sample average approximation (SAA) for general stochastic optimization problems, including two-stage stochastic programming without the relatively…
Robust Importance Weighting for Covariate Shift
Henry Lam, Fengpei Li, Siddharth Prusty
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…
Parametric Scenario Optimization under Limited Data: A Distributionally Robust Optimization View
Henry Lam, Fengpei Li
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…
Unbiased Sampling of Multidimensional Partial Differential Equations with Random Coefficients
Jose Blanchet, Fengpei Li, Xiaoou Li
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…
Unbiased Simulation for Optimizing Stochastic Function Compositions
Jose Blanchet, Donald Goldfarb, Garud Iyengar +2
In this paper, we introduce an unbiased gradient simulation algorithms for solving convex optimization problem with stochastic function compositions. We show that the unbiased grad…