Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Le, Hoang M."'
Autor:
Bialokozowicz, Lilian W., Le, Hoang M., Sylvain, Tristan, Forsyth, Peter A. I., Nagisetty, Vineel, Mori, Greg
This paper introduces the Orthogonal Polynomials Quadrature Algorithm for Survival Analysis (OPSurv), a new method providing time-continuous functional outputs for both single and competing risks scenarios in survival analysis. OPSurv utilizes the in
Externí odkaz:
http://arxiv.org/abs/2402.01955
Cameras and image-editing software often process images in the wide-gamut ProPhoto color space, encompassing 90% of all visible colors. However, when images are encoded for sharing, this color-rich representation is transformed and clipped to fit wit
Externí odkaz:
http://arxiv.org/abs/2304.11743
We study the problem of policy optimization (PO) with linear temporal logic (LTL) constraints. The language of LTL allows flexible description of tasks that may be unnatural to encode as a scalar cost function. We consider LTL-constrained PO as a sys
Externí odkaz:
http://arxiv.org/abs/2206.09546
Publikováno v:
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics 2021, PMLR 130:3475-3483
Robust control is a core approach for controlling systems with performance guarantees that are robust to modeling error, and is widely used in real-world systems. However, current robust control approaches can only handle small system uncertainty, an
Externí odkaz:
http://arxiv.org/abs/2103.11055
We offer an experimental benchmark and empirical study for off-policy policy evaluation (OPE) in reinforcement learning, which is a key problem in many safety critical applications. Given the increasing interest in deploying learning-based methods, t
Externí odkaz:
http://arxiv.org/abs/1911.06854
We study the problem of programmatic reinforcement learning, in which policies are represented as short programs in a symbolic language. Programmatic policies can be more interpretable, generalizable, and amenable to formal verification than neural p
Externí odkaz:
http://arxiv.org/abs/1907.05431
When learning policies for real-world domains, two important questions arise: (i) how to efficiently use pre-collected off-policy, non-optimal behavior data; and (ii) how to mediate among different competing objectives and constraints. We thus study
Externí odkaz:
http://arxiv.org/abs/1903.08738
Autor:
Taylor, Andrew J., Dorobantu, Victor D., Krishnamoorthy, Meera, Le, Hoang M., Yue, Yisong, Ames, Aaron D.
The goal of this paper is to understand the impact of learning on control synthesis from a Lyapunov function perspective. In particular, rather than consider uncertainties in the full system dynamics, we employ Control Lyapunov Functions (CLFs) as lo
Externí odkaz:
http://arxiv.org/abs/1903.07214
Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics. However, model uncertainty remains a persistent challenge, weakening the
Externí odkaz:
http://arxiv.org/abs/1903.01577
We study how to effectively leverage expert feedback to learn sequential decision-making policies. We focus on problems with sparse rewards and long time horizons, which typically pose significant challenges in reinforcement learning. We propose an a
Externí odkaz:
http://arxiv.org/abs/1803.00590