Kimin Lee

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KAIST 수리과학과 ∙ berkeley college

Decoupling Representation Learning from Reinforcement Learning

In an effort to overcome limitations of reward-driven feature learning i...
09/14/2020 ∙ by Adam Stooke, et al. ∙ 22 ∙ share

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Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning

Despite the significant progress of deep reinforcement learning (RL) in ...
08/03/2020 ∙ by Xingyu Lu, et al. ∙ 30 ∙ share

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Learning to Sample with Local and Global Contexts in Experience Replay Buffer

Experience replay, which enables the agents to remember and reuse experi...
07/14/2020 ∙ by Youngmin Oh, et al. ∙ 7 ∙ share

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SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning

Model-free deep reinforcement learning (RL) has been successful in a ran...
07/09/2020 ∙ by Kimin Lee, et al. ∙ 66 ∙ share

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Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning

Model-based reinforcement learning (RL) enjoys several benefits, such as...
05/14/2020 ∙ by Kimin Lee, et al. ∙ 7 ∙ share

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Reinforcement Learning with Augmented Data

Learning from visual observations is a fundamental yet challenging probl...
04/30/2020 ∙ by Michael (Misha) Laskin, et al. ∙ 14 ∙ share

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Regularizing Class-wise Predictions via Self-knowledge Distillation

Deep neural networks with millions of parameters may suffer from poor ge...
03/31/2020 ∙ by Sukmin Yun, et al. ∙ 9 ∙ share

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A Simple Randomization Technique for Generalization in Deep Reinforcement Learning

Deep reinforcement learning (RL) agents often fail to generalize to unse...
10/11/2019 ∙ by Kimin Lee, et al. ∙ 24 ∙ share

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Incremental Learning with Unlabeled Data in the Wild

Deep neural networks are known to suffer from catastrophic forgetting in...
03/29/2019 ∙ by Kibok Lee, et al. ∙ 13 ∙ share

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Robust Inference via Generative Classifiers for Handling Noisy Labels

Large-scale datasets may contain significant proportions of noisy (incor...
01/31/2019 ∙ by Kimin Lee, et al. ∙ 7 ∙ share

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Using Pre-Training Can Improve Model Robustness and Uncertainty

Tuning a pre-trained network is commonly thought to improve data efficie...
01/28/2019 ∙ by Dan Hendrycks, et al. ∙ 17 ∙ share

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A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks

Detecting test samples drawn sufficiently far away from the training dis...
07/10/2018 ∙ by Kimin Lee, et al. ∙ 3 ∙ share

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Hierarchical Novelty Detection for Visual Object Recognition

Deep neural networks have achieved impressive success in large-scale vis...
04/02/2018 ∙ by Kibok Lee, et al. ∙ 1 ∙ share

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Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples

The problem of detecting whether a test sample is from in-distribution (...
11/26/2017 ∙ by Kimin Lee, et al. ∙ 1 ∙ share

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Confident Multiple Choice Learning

Ensemble methods are arguably the most trustworthy techniques for boosti...
06/12/2017 ∙ by Kimin Lee, et al. ∙ 1 ∙ share

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Kimin Lee

Featured Co-authors

Bo Li

Pieter Abbeel

Honglak Lee

Jinwoo Shin

Sung Ju Hwang

Eunho Yang

Lerrel Pinto

Dan Hendrycks

Kibok Lee

Yuting Zhang

Adam Stooke

Decoupling Representation Learning from Reinforcement Learning

Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning

Learning to Sample with Local and Global Contexts in Experience Replay Buffer

SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning

Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning

Reinforcement Learning with Augmented Data

Regularizing Class-wise Predictions via Self-knowledge Distillation

A Simple Randomization Technique for Generalization in Deep Reinforcement Learning

Incremental Learning with Unlabeled Data in the Wild

Robust Inference via Generative Classifiers for Handling Noisy Labels

Using Pre-Training Can Improve Model Robustness and Uncertainty

A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks

Hierarchical Novelty Detection for Visual Object Recognition

Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples

Confident Multiple Choice Learning