229 citations · 669 across the 20 of their papers we have counts for
7 papers · 1 filter
PEBO-SLAM: Observer design for visual inertial SLAM with convergence guarantees
Bowen Yi, Chi Jin, Lei Wang +3
This paper introduces a new linear parameterization to the problem of visual inertial simultaneous localization and mapping (VI-SLAM) -- without any approximation -- for the case o…
Is RLHF More Difficult than Standard RL?
Yuanhao Wang, Qinghua Liu, Chi Jin
Reinforcement learning from Human Feedback (RLHF) learns from preference signals, while standard Reinforcement Learning (RL) directly learns from reward signals. Preferences arguab…
Context-lumpable stochastic bandits
Chung-Wei Lee, Qinghua Liu, Yasin Abbasi-Yadkori +3
We consider a contextual bandit problem with contexts and actions. In each round , the learner observes a random context and chooses an action based on its pas…
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method
Ahmed Khaled, Konstantin Mishchenko, Chi Jin
This paper proposes a new easy-to-implement parameter-free gradient-based optimizer: DoWG (Distance over Weighted Gradients). We prove that DoWG is efficient -- matching the conver…
Learning a Universal Human Prior for Dexterous Manipulation from Human Preference
Zihan Ding, Yuanpei Chen, Allen Z. Ren +4
Generating human-like behavior on robots is a great challenge especially in dexterous manipulation tasks with robotic hands. Scripting policies from scratch is intractable due to t…
On the Provable Advantage of Unsupervised Pretraining
Jiawei Ge, Shange Tang, Jianqing Fan +1
Unsupervised pretraining, which learns a useful representation using a large amount of unlabeled data to facilitate the learning of downstream tasks, is a critical component of mod…