Zobrazeno 1 - 10
of 2 846
pro vyhledávání: '"Srikant, P."'
Autor:
Etesami, S. Rasoul, Srikant, R.
We consider the problem of learning stable matchings with unknown preferences in a decentralized and uncoordinated manner, where "decentralized" means that players make decisions individually without the influence of a central platform, and "uncoordi
Externí odkaz:
http://arxiv.org/abs/2407.21294
The celestial $Lw_{1+\infty}$ symmetries in asymptotically flat spacetimes have a natural geometric interpretation on twistor space in terms of Poisson diffeomorphisms. Using this framework, we provide a first-principle derivation of the canonical ge
Externí odkaz:
http://arxiv.org/abs/2407.04028
Carrollian amplitudes are flat space amplitudes written in position space at null infinity which can be re-interpreted as correlators in a putative dual Carrollian CFT. We argue that these amplitudes are the natural objects obtained in the flat space
Externí odkaz:
http://arxiv.org/abs/2406.19343
We consider policy optimization methods in reinforcement learning settings where the state space is arbitrarily large, or even countably infinite. The motivation arises from control problems in communication networks, matching markets, and other queu
Externí odkaz:
http://arxiv.org/abs/2405.20467
Transformer-based models have emerged as one of the most widely used architectures for natural language processing, natural language generation, and image generation. The size of the state-of-the-art models has increased steadily reaching billions of
Externí odkaz:
http://arxiv.org/abs/2405.10480
Conformally soft operators and their associated soft theorems on the celestial sphere encode the low energy behaviour of bulk scattering amplitudes. They lead to an infinite dimensional symmetry algebra of the celestial CFT at tree-level. In this pap
Externí odkaz:
http://arxiv.org/abs/2403.10443
We present the first finite time global convergence analysis of policy gradient in the context of infinite horizon average reward Markov decision processes (MDPs). Specifically, we focus on ergodic tabular MDPs with finite state and action spaces. Ou
Externí odkaz:
http://arxiv.org/abs/2403.06806
Phages and their bacterial hosts are locked in an evolutionary competition which in small and closed systems typically results in the extinction of one or the other. To resist phages bacteria have evolved numerous defense systems, which nevertheless
Externí odkaz:
http://arxiv.org/abs/2402.19388
Autor:
Liu, Ping, Wei, Haichao, Hou, Xiaochen, Shen, Jianqiang, He, Shihai, Shen, Kay Qianqi, Chen, Zhujun, Borisyuk, Fedor, Hewlett, Daniel, Wu, Liang, Veeraraghavan, Srikant, Tsun, Alex, Jiang, Chengming, Zhang, Wenjing
We present LinkSAGE, an innovative framework that integrates Graph Neural Networks (GNNs) into large-scale personalized job matching systems, designed to address the complex dynamics of LinkedIns extensive professional network. Our approach capitaliz
Externí odkaz:
http://arxiv.org/abs/2402.13430
Reinforcement Learning from Human Feedback (RLHF) has achieved impressive empirical successes while relying on a small amount of human feedback. However, there is limited theoretical justification for this phenomenon. Additionally, most recent studie
Externí odkaz:
http://arxiv.org/abs/2402.10342