Zobrazeno 1 - 10
of 364
pro vyhledávání: '"Rangi P"'
Autor:
Bernadette Jones, Paula Toko King, Gabrielle Baker, Linda Waimarie Nikora, Huhana Hickey, Meredith Perry, Rangi Pouwhare, Tristram Richard Ingham
Publikováno v:
Kōtuitui, Vol 19, Iss 1, Pp 45-64 (2024)
ABSTRACTFor Indigenous Māori in Aotearoa New Zealand, the impact of disability can be pervasive yet often invisible due to considerable gaps in the accuracy and interpretations of disability data and information for Māori. We present findings from
Externí odkaz:
https://doaj.org/article/5446a5bc51a249be8e79ca31317b2875
We introduce an extension of the non-equilibrium dynamical mean field theory to incorporate the effects of static random disorder in the dynamics of a many-particle system by integrating out different disorder configurations resulting in an effective
Externí odkaz:
http://arxiv.org/abs/2405.13876
The out of time order correlator (OTOC) serves as a powerful tool for investigating quantum information spreading and chaos in complex systems. We present a method employing non-equilibrium dynamical mean-field theory (DMFT) and coherent potential ap
Externí odkaz:
http://arxiv.org/abs/2403.03214
Autor:
Ren, Yinuo, Xiao, Tesi, Gangwani, Tanmay, Rangi, Anshuka, Rahmanian, Holakou, Ying, Lexing, Sanyal, Subhajit
Multi-objective optimization (MOO) aims to optimize multiple, possibly conflicting objectives with widespread applications. We introduce a novel interacting particle method for MOO inspired by molecular dynamics simulations. Our approach combines ove
Externí odkaz:
http://arxiv.org/abs/2311.13159
Non-Hermitian topological phases have gained immense attention due to their potential to unlock novel features beyond Hermitian bounds. PT-symmetric (Parity Time-reversal symmetric) non-Hermitian models have been studied extensively over the past dec
Externí odkaz:
http://arxiv.org/abs/2307.03178
Autor:
Krishnamurthy, Sanath Kumar, Gangwani, Tanmay, Katariya, Sumeet, Kveton, Branislav, Modi, Shrey, Rangi, Anshuka
We consider the finite-horizon offline reinforcement learning (RL) setting, and are motivated by the challenge of learning the policy at any step h in dynamic programming (DP) algorithms. To learn this, it is sufficient to evaluate the treatment effe
Externí odkaz:
http://arxiv.org/abs/2302.00284
Motivated by cognitive radios, stochastic Multi-Player Multi-Armed Bandits has been extensively studied in recent years. In this setting, each player pulls an arm, and receives a reward corresponding to the arm if there is no collision, namely the ar
Externí odkaz:
http://arxiv.org/abs/2211.07817
To understand the security threats to reinforcement learning (RL) algorithms, this paper studies poisoning attacks to manipulate \emph{any} order-optimal learning algorithm towards a targeted policy in episodic RL and examines the potential damage of
Externí odkaz:
http://arxiv.org/abs/2208.13663
We study bandit algorithms under data poisoning attacks in a bounded reward setting. We consider a strong attacker model in which the attacker can observe both the selected actions and their corresponding rewards and can contaminate the rewards with
Externí odkaz:
http://arxiv.org/abs/2102.07711
In this work, we study sequential choice bandits with feedback. We propose bandit algorithms for a platform that personalizes users' experience to maximize its rewards. For each action directed to a given user, the platform is given a positive reward
Externí odkaz:
http://arxiv.org/abs/2101.01572