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pro vyhledávání: '"Das, Nirjhar"'
Autor:
Das, Nirjhar, Sinha, Gaurav
We study the Linear Contextual Bandit problem in the hybrid reward setting. In this setting every arm's reward model contains arm specific parameters in addition to parameters shared across the reward models of all the arms. We can reduce this settin
Externí odkaz:
http://arxiv.org/abs/2406.10131
We study the generalized linear contextual bandit problem within the constraints of limited adaptivity. In this paper, we present two algorithms, $\texttt{B-GLinCB}$ and $\texttt{RS-GLinCB}$, that address, respectively, two prevalent limited adaptivi
Externí odkaz:
http://arxiv.org/abs/2404.06831
Reinforcement Learning from Human Feedback (RLHF) is pivotal in aligning Large Language Models (LLMs) with human preferences. Although aligned generative models have shown remarkable abilities in various tasks, their reliance on high-quality human pr
Externí odkaz:
http://arxiv.org/abs/2402.10500
Autor:
Das, Nirjhar, Chattopadhyay, Arpan
In this work, we propose a novel inverse reinforcement learning (IRL) algorithm for constrained Markov decision process (CMDP) problems. In standard IRL problems, the inverse learner or agent seeks to recover the reward function of the MDP, given a s
Externí odkaz:
http://arxiv.org/abs/2305.08130
Yoga is a globally acclaimed and widely recommended practice for a healthy living. Maintaining correct posture while performing a Yogasana is of utmost importance. In this work, we employ transfer learning from Human Pose Estimation models for extrac
Externí odkaz:
http://arxiv.org/abs/2206.13577
Akademický článek
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Autor:
Chasmai M; Computer Science and Engineering, Indian Institute of Technology Delhi, Delhi, India., Das N; Electrical Engineering, Indian Institute of Technology Delhi, Delhi, India., Bhardwaj A; School of Information Technology, Indian Institute of Technology Delhi, Delhi, India., Garg R; Computer Science and Engineering, Indian Institute of Technology Delhi, Delhi, India.
Publikováno v:
SN computer science [SN Comput Sci] 2022; Vol. 3 (6), pp. 476. Date of Electronic Publication: 2022 Sep 13.