2 citations · 2 across the 2 of their papers we have counts for
5 papers
Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced Problem
Lun Jiang, Nima Salehi Sadghiani, Zhuo Tao +1
From the ad network standpoint, a user's activity is a multi-type sequence of temporal events consisting of event types and time intervals. Understanding user patterns in ad networ…
Perfecting the Crime Machine
Yigit Alparslan, Ioanna Panagiotou, Willow Livengood +2
This study explores using different machine learning techniques and workflows to predict crime related statistics, specifically crime type in Philadelphia. We use crime location an…
Maximum Entropy Diverse Exploration: Disentangling Maximum Entropy Reinforcement Learning
Andrew Cohen, Lei Yu, Xingye Qiao +1
Two hitherto disconnected threads of research, diverse exploration (DE) and maximum entropy RL have addressed a wide range of problems facing reinforcement learning algorithms via…
Diverse Exploration via Conjugate Policies for Policy Gradient Methods
Andrew Cohen, Xingye Qiao, Lei Yu +2
We address the challenge of effective exploration while maintaining good performance in policy gradient methods. As a solution, we propose diverse exploration (DE) via conjugate po…
Diverse Exploration for Fast and Safe Policy Improvement
Andrew Cohen, Lei Yu, Robert Wright
We study an important yet under-addressed problem of quickly and safely improving policies in online reinforcement learning domains. As its solution, we propose a novel exploration…