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Kimin Lee
Kimin Lee
Research Scientist, Google
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
K Lee, K Lee, H Lee, J Shin
Advances in neural information processing systems 31, 2018
8722018
Training confidence-calibrated classifiers for detecting out-of-distribution samples
K Lee, H Lee, K Lee, J Shin
International Conference on Learning Representations, 2017
5662017
Using pre-training can improve model robustness and uncertainty
D Hendrycks, K Lee, M Mazeika
International Conference on Machine Learning, 2712-2721, 2019
3852019
Reinforcement learning with augmented data
M Laskin, K Lee, A Stooke, L Pinto, P Abbeel, A Srinivas
Advances in neural information processing systems, 2020
2692020
Decision transformer: Reinforcement learning via sequence modeling
L Chen, K Lu, A Rajeswaran, K Lee, A Grover, M Laskin, P Abbeel, ...
Advances in neural information processing systems, 2021
1602021
Decoupling representation learning from reinforcement learning
A Stooke, K Lee, P Abbeel, M Laskin
International Conference on Machine Learning, 9870-9879, 2021
1172021
Regularizing class-wise predictions via self-knowledge distillation
S Yun, J Park, K Lee, J Shin
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020
1152020
Overcoming catastrophic forgetting with unlabeled data in the wild
K Lee, K Lee, J Shin, H Lee
Proceedings of the IEEE/CVF International Conference on Computer Vision, 312-321, 2019
1052019
Network randomization: A simple technique for generalization in deep reinforcement learning
K Lee, K Lee, J Shin, H Lee
International Conference on Learning Representations, 2019
952019
Sunrise: A simple unified framework for ensemble learning in deep reinforcement learning
K Lee, M Laskin, A Srinivas, P Abbeel
International Conference on Machine Learning, 6131-6141, 2021
742021
Robust inference via generative classifiers for handling noisy labels
K Lee, S Yun, K Lee, H Lee, B Li, J Shin
International Conference on Machine Learning, 3763-3772, 2019
652019
Hierarchical novelty detection for visual object recognition
K Lee, K Lee, K Min, Y Zhang, J Shin, H Lee
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
562018
Context-aware dynamics model for generalization in model-based reinforcement learning
K Lee, Y Seo, S Lee, H Lee, J Shin
International Conference on Machine Learning, 5757-5766, 2020
442020
Confident Multiple Choice Learning
K Lee, C Hwang, KS Park, J Shin
Proceedings of the 34th International Conference on Machine Learning, 2017
442017
State entropy maximization with random encoders for efficient exploration
Y Seo, L Chen, J Shin, H Lee, P Abbeel, K Lee
International Conference on Machine Learning, 2021
392021
Learning to specialize with knowledge distillation for visual question answering
J Mun, K Lee, J Shin, B Han
Advances in neural information processing systems 31, 2018
312018
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training
K Lee, L Smith, P Abbeel
International Conference on Machine Learning, 2021
192021
URLB: Unsupervised reinforcement learning benchmark
M Laskin, D Yarats, H Liu, K Lee, A Zhan, K Lu, C Cang, L Pinto, P Abbeel
Conference on Neural Information Processing Systems Datasets and Benchmarks …, 2021
172021
Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble
S Lee, Y Seo, K Lee, P Abbeel, J Shin
Annual Conference on Robot Learning, 2021
132021
Trajectory-wise multiple choice learning for dynamics generalization in reinforcement learning
Y Seo, K Lee, I Clavera, T Kurutach, J Shin, P Abbeel
Advances in neural information processing systems, 2020
102020
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