1 code implementation • 1 Nov 2021 • Byungsoo Jeon, Sunghyun Park, Peiyuan Liao, Sheng Xu, Tianqi Chen, Zhihao Jia
Given the fast-evolving nature of the DL ecosystem, this manual approach often slows down continuous innovations across different layers; it prevents hardware vendors from the fast deployment of their cutting-edge libraries, DL framework developers must repeatedly adjust their hand-coded rules to accommodate new versions of libraries, and machine learning practitioners need to wait for the integration of new technologies and often encounter unsatisfactory performance.
no code implementations • 1 Jan 2021 • Bharathan Balaji, Petros Christodoulou, Xiaoyu Lu, Byungsoo Jeon, Jordan Bell-Masterson
We propose a simple class of deep reinforcement learning (RL) methods, called FactoredRL, that can leverage factored environment structures to improve the sample efficiency of existing model-based and model-free RL algorithms.
no code implementations • 5 Feb 2020 • Byungsoo Jeon, Namyong Park
This paper addresses a key challenge in MOOC dropout prediction, namely to build meaningful representations from clickstream data.
no code implementations • 5 Feb 2020 • Byungsoo Jeon, Namyong Park, Seojin Bang
Massive Open Online Courses (MOOCs) have become popular platforms for online learning.
no code implementations • 1 May 2019 • Byungsoo Jeon, Eyal Shafran, Luke Breitfeller, Jason Levin, Carolyn P. Rose
This paper addresses a key challenge in Educational Data Mining, namely to model student behavioral trajectories in order to provide a means for identifying students most at-risk, with the goal of providing supportive interventions.
1 code implementation • NAACL 2018 • Yohan Jo, Shivani Poddar, Byungsoo Jeon, Qinlan Shen, Carolyn P. Rose, Graham Neubig
We present a neural architecture for modeling argumentative dialogue that explicitly models the interplay between an Opinion Holder's (OH's) reasoning and a challenger's argument, with the goal of predicting if the argument successfully changes the OH's view.