Zobrazeno 1 - 10
of 281
pro vyhledávání: '"Rao, Shrisha"'
Autor:
Karishma, Rao, Shrisha
We propose a multi-agent system that enables groups of agents to collaborate and work autonomously to execute tasks. Groups can work in a decentralized manner and can adapt to dynamic changes in the environment. Groups of agents solve assigned tasks
Externí odkaz:
http://arxiv.org/abs/2403.04370
Autor:
Perez, Guillermo A., Rao, Shrisha
A pushdown vector addition system with states (PVASS) extends the model of vector addition systems with a pushdown stack. The algorithmic analysis of PVASS has applications such as static analysis of recursive programs manipulating integer variables.
Externí odkaz:
http://arxiv.org/abs/2402.13237
We develop an iterative differentially private algorithm for client selection in federated settings. We consider a federated network wherein clients coordinate with a central server to complete a task; however, the clients decide whether to participa
Externí odkaz:
http://arxiv.org/abs/2310.09370
The rapid spread of fake news is a serious problem calling for AI solutions. We employ a deep learning based automated detector through a three level hierarchical attention network (3HAN) for fast, accurate detection of fake news. 3HAN has three leve
Externí odkaz:
http://arxiv.org/abs/2306.12014
We study the complexity of reductions for weighted reachability in parametric Markov decision processes. That is, we say a state p is never worse than q if for all valuations of the polynomial indeterminates it is the case that the maximal expected w
Externí odkaz:
http://arxiv.org/abs/2305.05739
Publikováno v:
IEEE Access, vol. 11, pp. 58254-58268, 2023
Federated optimization, wherein several agents in a network collaborate with a central server to achieve optimal social cost over the network with no requirement for exchanging information among agents, has attracted significant interest from the res
Externí odkaz:
http://arxiv.org/abs/2304.01510
This paper proposes a Robust Gradient Classification Framework (RGCF) for Byzantine fault tolerance in distributed stochastic gradient descent. The framework consists of a pattern recognition filter which we train to be able to classify individual gr
Externí odkaz:
http://arxiv.org/abs/2301.07498
Autor:
Karishma, Rao, Shrisha
Publikováno v:
Journal of Simulation, 2023
We present a multi-agent system where agents can cooperate to solve a system of dependent tasks, with agents having the capability to explore a solution space, make inferences, as well as query for information under a limited budget. Re-exploration o
Externí odkaz:
http://arxiv.org/abs/2202.05891
Publikováno v:
Journal of Simulation, vol. 17 (6), 2023
Existing studies on prejudice, which is important in multi-group dynamics in societies, focus on the social-psychological knowledge behind the processes involving prejudice and its propagation. We instead create a multi-agent framework that simulates
Externí odkaz:
http://arxiv.org/abs/2112.14801