982 citations · 2.8k across the 47 of their papers we have counts for
10 papers · 1 filter
MASKER: Masked Keyword Regularization for Reliable Text Classification
Seung Jun Moon, Sangwoo Mo, Kimin Lee +2
Pre-trained language models have achieved state-of-the-art accuracies on various text classification tasks, e.g., sentiment analysis, natural language inference, and semantic textu…
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
Younggyo Seo, Kimin Lee, Ignasi Clavera +3
Model-based reinforcement learning (RL) has shown great potential in various control tasks in terms of both sample-efficiency and final performance. However, learning a generalizab…
Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment
Wilka Carvalho, Anthony Liang, Kimin Lee +4
First-person object-interaction tasks in high-fidelity, 3D, simulated environments such as the AI2Thor virtual home-environment pose significant sample-efficiency challenges for re…
Decoupling Representation Learning from Reinforcement Learning
Adam Stooke, Kimin Lee, Pieter Abbeel +1
In an effort to overcome limitations of reward-driven feature learning in deep reinforcement learning (RL) from images, we propose decoupling representation learning from policy le…
Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning
Xingyu Lu, Kimin Lee, Pieter Abbeel +1
Despite the significant progress of deep reinforcement learning (RL) in solving sequential decision making problems, RL agents often overfit to training environments and struggle t…
Learning to Sample with Local and Global Contexts in Experience Replay Buffer
Youngmin Oh, Kimin Lee, Jinwoo Shin +2
Experience replay, which enables the agents to remember and reuse experience from the past, has played a significant role in the success of off-policy reinforcement learning (RL).…
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
Kimin Lee, Michael Laskin, Aravind Srinivas +1
Off-policy deep reinforcement learning (RL) has been successful in a range of challenging domains. However, standard off-policy RL algorithms can suffer from several issues, such a…
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
Kimin Lee, Younggyo Seo, Seunghyun Lee +2
Model-based reinforcement learning (RL) enjoys several benefits, such as data-efficiency and planning, by learning a model of the environment's dynamics. However, learning a global…
Regularizing Class-wise Predictions via Self-knowledge Distillation
Sukmin Yun, Jongjin Park, Kimin Lee +1
Deep neural networks with millions of parameters may suffer from poor generalization due to overfitting. To mitigate the issue, we propose a new regularization method that penalize…
Reinforcement Learning with Augmented Data
Michael Laskin, Kimin Lee, Adam Stooke +3
Learning from visual observations is a fundamental yet challenging problem in Reinforcement Learning (RL). Although algorithmic advances combined with convolutional neural networks…