260 citations · 367 across the 10 of their papers we have counts for
10 papers
Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors
Letian Wang, Jie Liu, Hao Shao +4
When autonomous vehicles are deployed on public roads, they will encounter countless and diverse driving situations. Many manually designed driving policies are difficult to scale…
A New Penalty Dual-Primal Augmented Lagrangian Method and Its Extensions
Jie Liu, Xiaoqing Ou, Jiawei Chen
In this paper, we propose a penalty dual-primal augmented lagrangian method for solving convex minimization problems under linear equality or inequality constraints. The proposed m…
Data valuation: The partial ordinal Shapley value for machine learning
Jie Liu, Peizheng Wang, Chao Wu
Data valuation using Shapley value has emerged as a prevalent research domain in machine learning applications. However, it is a challenge to address the role of order in data coop…
Optimal Power Flow Pursuit in the Alternating Current Model
Jie Liu, Antonio Bellon, Andrea Simonetto +2
Transmission-constrained problems in power systems can be cast as polynomial optimization problems whose coefficients vary over time. We consider the complications therein and sugg…
A Coordinate-Descent Algorithm for Tracking Solutions in Time-Varying Optimal Power Flows
Jie Liu, Jakub Marecek, Andrea Simonetto +1
Consider a polynomial optimisation problem, whose instances vary continuously over time. We propose to use a coordinate-descent algorithm for solving such time-varying optimisation…
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient
Lam M. Nguyen, Jie Liu, Katya Scheinberg +1
In this paper, we propose a StochAstic Recursive grAdient algoritHm (SARAH), as well as its practical variant SARAH+, as a novel approach to the finite-sum minimization problems. D…
Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption
Jie Liu, Martin Takac
We propose a projected semi-stochastic gradient descent method with mini-batch for improving both the theoretical complexity and practical performance of the general stochastic gra…
Hybrid Methods in Solving Alternating-Current Optimal Power Flows
Alan C. Liddell, Jie Liu, Jakub Marecek +1
Many steady-state problems in power systems, including rectangular power-voltage formulations of optimal power flows in the alternating-current model (ACOPF), can be cast as polyno…
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting
Jakub Konečný, Jie Liu, Peter Richtárik +1
We propose mS2GD: a method incorporating a mini-batching scheme for improving the theoretical complexity and practical performance of semi-stochastic gradient descent (S2GD). We co…
mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting
Jakub Konečný, Jie Liu, Peter Richtárik +1
We propose a mini-batching scheme for improving the theoretical complexity and practical performance of semi-stochastic gradient descent applied to the problem of minimizing a stro…