58 citations · 276 across the 26 of their papers we have counts for
6 papers · 1 filter
Risk-Constrained Reinforcement Learning with Percentile Risk Criteria
Yinlam Chow, Mohammad Ghavamzadeh, Lucas Janson +1
In many sequential decision-making problems one is interested in minimizing an expected cumulative cost while taking into account \emph{risk}, i.e., increased awareness of events o…
An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning
Joseph A. Starek, Javier V. Gomez, Edward Schmerling +3
Bi-directional search is a widely used strategy to increase the success and convergence rates of sampling-based motion planning algorithms. Yet, few results are available that merg…
Monte Carlo Motion Planning for Robot Trajectory Optimization Under Uncertainty
Lucas Janson, Edward Schmerling, Marco Pavone
This article presents a novel approach, named MCMP (Monte Carlo Motion Planning), to the problem of motion planning under uncertainty, i.e., to the problem of computing a low-cost…
Familywise Error Rate Control via Knockoffs
Lucas Janson, Weijie Su
We present a novel method for controlling the -familywise error rate (-FWER) in the linear regression setting using the knockoffs framework first introduced by Barber and Can…
EigenPrism: Inference for High-Dimensional Signal-to-Noise Ratios
Lucas Janson, Rina Foygel Barber, Emmanuel Candès
Consider the following three important problems in statistical inference, namely, constructing confidence intervals for (1) the error of a high-dimensional () regression estim…
Deterministic Sampling-Based Motion Planning: Optimality, Complexity, and Performance
Lucas Janson, Brian Ichter, Marco Pavone
Probabilistic sampling-based algorithms, such as the probabilistic roadmap (PRM) and the rapidly-exploring random tree (RRT) algorithms, represent one of the most successful approa…