15 citations · 31 across the 7 of their papers we have counts for
7 papers
Reflect-then-Plan: Offline Model-Based Planning through a Doubly Bayesian Lens
Jihwan Jeong, Xiaoyu Wang, Jingmin Wang +2
Offline reinforcement learning (RL) is crucial when online exploration is costly or unsafe but often struggles with high epistemic uncertainty due to limited data. Existing methods…
pyRDDLGym: From RDDL to Gym Environments
Ayal Taitler, Michael Gimelfarb, Jihwan Jeong +4
We present pyRDDLGym, a Python framework for auto-generation of OpenAI Gym environments from RDDL declerative description. The discrete time step evolution of variables in RDDL is…
Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization
Jihwan Jeong, Xiaoyu Wang, Michael Gimelfarb +3
Offline reinforcement learning (RL) addresses the problem of learning a performant policy from a fixed batch of data collected by following some behavior policy. Model-based approa…
RAPTOR: End-to-end Risk-Aware MDP Planning and Policy Learning by Backpropagation
Noah Patton, Jihwan Jeong, Michael Gimelfarb +1
Planning provides a framework for optimizing sequential decisions in complex environments. Recent advances in efficient planning in deterministic or stochastic high-dimensional dom…
Online Continual Learning in Image Classification: An Empirical Survey
Zheda Mai, Ruiwen Li, Jihwan Jeong +3
Online continual learning for image classification studies the problem of learning to classify images from an online stream of data and tasks, where tasks may include new classes (…
Online Class-Incremental Continual Learning with Adversarial Shapley Value
Dongsub Shim, Zheda Mai, Jihwan Jeong +3
As image-based deep learning becomes pervasive on every device, from cell phones to smart watches, there is a growing need to develop methods that continually learn from data while…
Batch-level Experience Replay with Review for Continual Learning
Zheda Mai, Hyunwoo Kim, Jihwan Jeong +1
Continual learning is a branch of deep learning that seeks to strike a balance between learning stability and plasticity. The CVPR 2020 CLVision Continual Learning for Computer Vis…