Zobrazeno 1 - 10
of 530
pro vyhledávání: '"Kar, Soummya"'
The occlusion of the sun by clouds is one of the primary sources of uncertainties in solar power generation, and is a factor that affects the wide-spread use of solar power as a primary energy source. Real-time forecasting of cloud movement and, as a
Externí odkaz:
http://arxiv.org/abs/2409.12016
We develop a clearance and settlement model for Peer-to-Peer (P2P) energy trading in low-voltage networks. The model enables direct transactions between parties within an open and distributed system and integrates unused capacity while respecting net
Externí odkaz:
http://arxiv.org/abs/2407.21403
The rapid adoption of Electric Vehicles (EVs) poses challenges for electricity grids to accommodate or mitigate peak demand. Vehicle-to-Vehicle Charging (V2VC) has been recently adopted by popular EVs, posing new opportunities and challenges to the m
Externí odkaz:
http://arxiv.org/abs/2404.08837
In this paper, we study the problem of ensuring safety with a few shots of samples for partially unknown systems. We first characterize a fundamental limit when producing safe actions is not possible due to insufficient information or samples. Then,
Externí odkaz:
http://arxiv.org/abs/2403.06045
We develop a family of distributed clustering algorithms that work over networks of users. In the proposed scenario, users contain a local dataset and communicate only with their immediate neighbours, with the aim of finding a clustering of the full,
Externí odkaz:
http://arxiv.org/abs/2402.01302
Autor:
Armacki, Aleksandar, Sharma, Pranay, Joshi, Gauri, Bajovic, Dragana, Jakovetic, Dusan, Kar, Soummya
We study high-probability convergence guarantees of learning on streaming data in the presence of heavy-tailed noise. In the proposed scenario, the model is updated in an online fashion, as new information is observed, without storing any additional
Externí odkaz:
http://arxiv.org/abs/2310.18784
Motivated by understanding and analysis of large-scale machine learning under heavy-tailed gradient noise, we study distributed optimization with gradient clipping, i.e., in which certain clipping operators are applied to the gradients or gradient es
Externí odkaz:
http://arxiv.org/abs/2310.16920
Autor:
Fiscko, Carmel, Agarwal, Aayushya, Ruan, Yihan, Kar, Soummya, Pileggi, Larry, Sinopoli, Bruno
We present a stochastic first-order optimization method specialized for deep neural networks (DNNs), ECCO-DNN. This method models the optimization variable trajectory as a dynamical system and develops a discretization algorithm that adaptively selec
Externí odkaz:
http://arxiv.org/abs/2310.13901
We introduce a new workflow for unconstrained optimization whereby objective functions are mapped onto a physical domain to more easily design algorithms that are robust to hyperparameters and achieve fast convergence rates. Specifically, we represen
Externí odkaz:
http://arxiv.org/abs/2305.14061
This work studies a multi-agent Markov decision process (MDP) that can undergo agent dropout and the computation of policies for the post-dropout system based on control and sampling of the pre-dropout system. The central planner's objective is to fi
Externí odkaz:
http://arxiv.org/abs/2304.12458