Kimin Lee

Robust Inference via Generative Classifiers for Handling Noisy Labels

2019 · arXiv (Cornell University)

Large-scale datasets may contain significant proportions of noisy (incorrect) class labels, and it is well-known that modern deep neural networks (DNNs) poorly generalize from such noisy training datasets. To mitigate the issue, we propose a …

A Simple Randomization Technique for Generalization in Deep Reinforcement Learning

2019 · arXiv (Cornell University)

Deep reinforcement learning (RL) agents often fail to generalize to unseen environments (yet semantically similar to trained agents), particularly when they are trained on high-dimensional state spaces, such as images. In this paper, we propose …

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

2020 · arXiv (Cornell University)

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 model that can generalize across different dynamics is a challenging task. …

Regularizing Class-Wise Predictions via Self-Knowledge Distillation

2020

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 penalizes the predictive distribution between similar samples. In particular, …

Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments

2021 · arXiv (Cornell University)

The capability of a reinforcement learning (RL) agent heavily depends on the diversity of the learning scenarios generated by the environment. Generation of diverse realistic scenarios is challenging for real-time strategy (RTS) environments. The RTS …