2 citations · 4 across the 12 of their papers we have counts for
4 papers · 1 filter
Incremental Summarization for Customer Support via Progressive Note-Taking and Agent Feedback
Yisha Wu, Cen Mia Zhao, Yuanpei Cao +4
We introduce an incremental summarization system for customer support agents that intelligently determines when to generate concise bullet notes during conversations, reducing agen…
ObjectRL: An Object-Oriented Reinforcement Learning Codebase
Gulcin Baykal, Abdullah Akgül, Manuel Haussmann +4
ObjectRL is an open-source Python codebase for deep reinforcement learning (RL), designed for research-oriented prototyping with minimal programming effort. Unlike existing codebas…
Adaptive Ensemble Aggregation for Actor-Critics
Nicklas Werge, Yi-Shan Wu, Manuel Haussmann +2
Ensembles are ubiquitous in off-policy actor-critic learning, yet their efficacy depends critically on how they are aggregated. Current methods typically rely on static rules or ta…
Deep Actor-Critics with Tight Risk Certificates
Bahareh Tasdighi, Manuel Haussmann, Yi-Shan Wu +2
Deep actor-critic algorithms have reached a level where they influence everyday life. They are a driving force behind continual improvement of large language models through user fe…