17 citations · 36 across the 8 of their papers we have counts for
9 papers
Don't throw away your value model! Generating more preferable text with Value-Guided Monte-Carlo Tree Search decoding
Jiacheng Liu, Andrew Cohen, Ramakanth Pasunuru +3
Inference-time search algorithms such as Monte-Carlo Tree Search (MCTS) may seem unnecessary when generating natural language text based on state-of-the-art reinforcement learning…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Transfer RL across Observation Feature Spaces via Model-Based Regularization
Yanchao Sun, Ruijie Zheng, Xiyao Wang +2
In many reinforcement learning (RL) applications, the observation space is specified by human developers and restricted by physical realizations, and may thus be subject to dramati…
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning
Andrew Cohen, Ervin Teng, Vincent-Pierre Berges +5
The creation and destruction of agents in cooperative multi-agent reinforcement learning (MARL) is a critically under-explored area of research. Current MARL algorithms often assum…
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…