Zobrazeno 1 - 10
of 490
pro vyhledávání: '"Khargonekar, Pramod"'
Machine learning (ML) has demonstrated remarkable capabilities across many real-world systems, from predictive modeling to intelligent automation. However, the widespread integration of machine learning also makes it necessary to ensure machine learn
Externí odkaz:
http://arxiv.org/abs/2401.02552
In this paper, we consider the problem of learning online to manage Demand Response (DR) resources. A typical DR mechanism requires the DR manager to assign a baseline to the participating consumer, where the baseline is an estimate of the counterfac
Externí odkaz:
http://arxiv.org/abs/2303.15617
This paper proposes a two-level hierarchical matching framework for Integrated Hybrid Resources (IHRs) with grid constraints. An IHR is a collection of Renewable Energy Sources (RES) and flexible customers within a certain power system zone, endowed
Externí odkaz:
http://arxiv.org/abs/2301.13796
In this paper, we investigate the framework of Online Convex Optimization (OCO) for online learning. OCO offers a very powerful online learning framework for many applications. In this context, we study a specific framework of OCO called {\it OCO wit
Externí odkaz:
http://arxiv.org/abs/2210.16735
We study the problem of online learning in two-sided non-stationary matching markets, where the objective is to converge to a stable match. In particular, we consider the setting where one side of the market, the arms, has fixed known set of preferen
Externí odkaz:
http://arxiv.org/abs/2210.11692
We propose a novel change point detection approach for online learning control with full information feedback (state, disturbance, and cost feedback) for unknown time-varying dynamical systems. We show that our algorithm can achieve a sub-linear regr
Externí odkaz:
http://arxiv.org/abs/2210.11684
In this paper, we consider the problem of finding a meta-learning online control algorithm that can learn across the tasks when faced with a sequence of $N$ (similar) control tasks. Each task involves controlling a linear dynamical system for a finit
Externí odkaz:
http://arxiv.org/abs/2208.10259
Mixed-precision Deep Neural Networks achieve the energy efficiency and throughput needed for hardware deployment, particularly when the resources are limited, without sacrificing accuracy. However, the optimal per-layer bit precision that preserves a
Externí odkaz:
http://arxiv.org/abs/2208.06064
Incentive programs and ongoing reduction in costs are driving joint installation of solar PV panels and storage systems in residential households. There is a need for optimal investment decisions to reduce the electricity consumption costs of the hou
Externí odkaz:
http://arxiv.org/abs/2207.04635
We study the online robust control problem for linear dynamical systems with disturbances and uncertainties in the cost functions, with limited preview of the future disturbances and the cost functions, $N$. Our goal is to find an online control poli
Externí odkaz:
http://arxiv.org/abs/2111.15063