6 accepted papers
Stochastic sequential decision-making systems — such as Markov decision processes and their variants — are increasingly used in areas such as transportation, healthcare, and communication. However, the ability to explain these systems’ outputs to non-technical end users has not kept pace with their
Prompt optimization methods have demonstrated significant effectiveness in aligning black-box large language models (LLMs). In parallel, inference scaling strategies such as Best-of-N Sampling and Majority Voting have likewise been shown to improve alignment and performance by trading additional com
The advent of large language models (LLMs) has sparked significant interest in using natural language for preference learning. However, existing methods often suffer from high computational burdens, taxing human supervision, and lack of interpretability. To address these issues, we introduce MAPLE,…
Agents often need to infer a reward function from observations to learn desired behaviors. However, agents may infer a reward function that does not align with the original intent because there can be multiple reward functions consistent with its observations. Operating based on such misaligned rewa…
Learning from feedback is a common paradigm to acquire information that is hard to specify a priori. In this work, we consider an agent with a known nominal reward model that captures its high-level task objective. Furthermore, the agent operates subject to constraints that are unknown a priori and…
Distributed Constraint Optimization Problems (DCOPs) are an important framework for modeling coordinated decision-making problems in multi-agent systems with a set of discrete variables. Later works have extended DCOPs to model problems with a set of continuous variables, named Functional DCOPs (F-D…