12 papers
Objective-Behavior Alignment: Diagnostics for MORL Policy Selection
Antonio Mone, Zuzanna Osika, Florian Felten +4
Real-world decision-making often requires optimizing multiple competing objectives simultaneously. In reinforcement learning (RL), this is typically addressed by combining reward s…
Diverse Committees with Incomplete or Inaccurate Approval Ballots
Feline Lindeboom, Martijn Brehm, Davide Grossi +1
We study diversity in approval-based committee elections with incomplete or inaccurate information. We define diversity according to the Maximum Coverage problem, which is known to…
Not All Subjectivity Is the Same! Defining Desiderata for the Evaluation of Subjectivity in NLP
Urja Khurana, Michiel van der Meer, Enrico Liscio +2
Subjective judgments are part of several NLP datasets and recent work is increasingly prioritizing models whose outputs reflect this diversity of perspectives. Such responses allow…
Exploring Equity of Climate Policies using Multi-Agent Multi-Objective Reinforcement Learning
Palok Biswas, Zuzanna Osika, Isidoro Tamassia +5
Addressing climate change requires coordinated policy efforts of nations worldwide. These efforts are informed by scientific reports, which rely in part on Integrated Assessment Mo…
Multi-Objective Reinforcement Learning for Water Management
Zuzanna Osika, Roxana RÄdulescu, Jazmin Zatarain Salazar +2
Many real-world problems (e.g., resource management, autonomous driving, drug discovery) require optimizing multiple, conflicting objectives. Multi-objective reinforcement learning…
Signs of Struggle: Spotting Cognitive Distortions across Language and Register
Abhishek Kuber, Enrico Liscio, Ruixuan Zhang +2
Rising mental health issues among youth have increased interest in automated approaches for detecting early signs of psychological distress in digital text. One key focus is the id…