b'Kimin Lee'

research

∙ 05/24/2022

Reward Uncertainty for Exploration in Preference-based Reinforcement Learning

Conveying complex objectives to reinforcement learning (RL) agents often...

Xinran Liang, et al. ∙

research

∙ 03/25/2022

Reinforcement Learning with Action-Free Pre-Training from Videos

Recent unsupervised pre-training methods have shown to be effective on l...

Younggyo Seo, et al. ∙

research

∙ 03/18/2022

SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning

Preference-based reinforcement learning (RL) has shown potential for tea...

Jongjin Park, et al. ∙

research

∙ 11/18/2021

Improving Transferability of Representations via Augmentation-Aware Self-Supervision

Recent unsupervised representation learning methods have shown to be eff...

Hankook Lee, et al. ∙

research

∙ 11/04/2021

B-Pref: Benchmarking Preference-Based Reinforcement Learning

Reinforcement learning (RL) requires access to a reward function that in...

Kimin Lee, et al. ∙

research

∙ 10/28/2021

URLB: Unsupervised Reinforcement Learning Benchmark

Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to s...

Michael (Misha) Laskin, et al. ∙

research

∙ 10/26/2021

Towards More Generalizable One-shot Visual Imitation Learning

A general-purpose robot should be able to master a wide range of tasks a...

Zhao Mandi, et al. ∙

research

∙ 08/11/2021

Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback

A promising approach to solving challenging long-horizon tasks has been ...

Xiaofei Wang, et al. ∙

research

∙ 07/01/2021

Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble

Recent advance in deep offline reinforcement learning (RL) has made it p...

Seunghyun Lee, et al. ∙

research

∙ 06/18/2021

Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments

The capability of reinforcement learning (RL) agent directly depends on ...

Abdus Salam Azad, et al. ∙

research

∙ 06/09/2021

PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training

Conveying complex objectives to reinforcement learning (RL) agents can o...

Kimin Lee, et al. ∙

research

∙ 06/02/2021

Decision Transformer: Reinforcement Learning via Sequence Modeling

We present a framework that abstracts Reinforcement Learning (RL) as a s...

Lili Chen, et al. ∙

research

∙ 03/04/2021

Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings

Recent advances in off-policy deep reinforcement learning (RL) have led ...

Lili Chen, et al. ∙

research

∙ 02/18/2021

State Entropy Maximization with Random Encoders for Efficient Exploration

Recent exploration methods have proven to be a recipe for improving samp...

Younggyo Seo, et al. ∙

research

∙ 12/17/2020

MASKER: Masked Keyword Regularization for Reliable Text Classification

Pre-trained language models have achieved state-of-the-art accuracies on...

Seung Jun Moon, et al. ∙

research

∙ 10/28/2020

Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in First-person Simulated 3D Environments

First-person object-interaction tasks in high-fidelity, 3D, simulated en...

Wilka Carvalho, et al. ∙

research

∙ 10/26/2020

Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning

Model-based reinforcement learning (RL) has shown great potential in var...

Younggyo Seo, et al. ∙

research

∙ 09/14/2020

Decoupling Representation Learning from Reinforcement Learning

In an effort to overcome limitations of reward-driven feature learning i...

Adam Stooke, et al. ∙

research

∙ 08/03/2020

Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning

Despite the significant progress of deep reinforcement learning (RL) in ...

Xingyu Lu, et al. ∙

research

∙ 07/14/2020

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

Experience replay, which enables the agents to remember and reuse experi...

Youngmin Oh, et al. ∙

research

∙ 07/09/2020

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

Model-free deep reinforcement learning (RL) has been successful in a ran...

Kimin Lee, et al. ∙

research

∙ 05/14/2020

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

Model-based reinforcement learning (RL) enjoys several benefits, such as...

Kimin Lee, et al. ∙

research

∙ 04/30/2020

Reinforcement Learning with Augmented Data

Learning from visual observations is a fundamental yet challenging probl...

Michael (Misha) Laskin, et al. ∙

research

∙ 03/31/2020

Regularizing Class-wise Predictions via Self-knowledge Distillation

Deep neural networks with millions of parameters may suffer from poor ge...

Sukmin Yun, et al. ∙

research

∙ 10/11/2019

A Simple Randomization Technique for Generalization in Deep Reinforcement Learning

Deep reinforcement learning (RL) agents often fail to generalize to unse...

Kimin Lee, et al. ∙

research

∙ 03/29/2019

Incremental Learning with Unlabeled Data in the Wild

Deep neural networks are known to suffer from catastrophic forgetting in...

Kibok Lee, et al. ∙

research

∙ 01/31/2019

Robust Inference via Generative Classifiers for Handling Noisy Labels

Large-scale datasets may contain significant proportions of noisy (incor...

Kimin Lee, et al. ∙

research

∙ 01/28/2019

Using Pre-Training Can Improve Model Robustness and Uncertainty

Tuning a pre-trained network is commonly thought to improve data efficie...

Dan Hendrycks, et al. ∙

research

∙ 07/10/2018

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

Detecting test samples drawn sufficiently far away from the training dis...

Kimin Lee, et al. ∙

research

∙ 04/02/2018

Hierarchical Novelty Detection for Visual Object Recognition

Deep neural networks have achieved impressive success in large-scale vis...

Kibok Lee, et al. ∙

research

∙ 11/26/2017

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

The problem of detecting whether a test sample is from in-distribution (...

Kimin Lee, et al. ∙

research

∙ 06/12/2017

Confident Multiple Choice Learning

Ensemble methods are arguably the most trustworthy techniques for boosti...

Kimin Lee, et al. ∙

Kimin Lee

Featured Co-authors