Search Results for author:
Found 8 papers, 2 papers with code
Grounded Persuasive Language Generation for Automated Marketing
This paper develops an agentic framework that employs large language models (LLMs) to automate the generation of persuasive and grounded marketing content, using real estate listing descriptions as our focal application domain.
Contractual Reinforcement Learning: Pulling Arms with Invisible Hands
The agency problem emerges in today's large scale machine learning tasks, where the learners are unable to direct content creation or enforce data collection.
Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model
Such a problem is modeled as a Stackelberg game between the principal and the agent, where the principal announces a scoring rule that specifies the payment, and then the agent then chooses an effort level that maximizes her own profit and reports the information.
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning
This paper proposes a novel model of sequential information design, namely the Markov persuasion processes (MPPs), where a sender, with informational advantage, seeks to persuade a stream of myopic receivers to take actions that maximizes the sender's cumulative utilities in a finite horizon Markovian environment with varying prior and utility functions.
Least Square Calibration for Peer Reviews
Peer review systems such as conference paper review often suffer from the issue of miscalibration.
Uncoupled Bandit Learning towards Rationalizability: Benchmarks, Barriers, and Algorithms
Under the uncoupled learning setup, the last-iterate convergence guarantee towards Nash equilibrium is shown to be impossible in many games.
Least Square Calibration for Peer Review
Peer review systems such as conference paper review often suffer from the issue of miscalibration.
Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation
Predicting users' preferences based on their sequential behaviors in history is challenging and crucial for modern recommender systems.
