Traditional recommender systems typically use user-item rating histories as their main data
source. However, deep generative models now have the capability to model and sample …

Designing preference elicitation (PE) methodologies that can quickly ascertain a user's top
item preferences in a cold-start setting is a key challenge for building effective and …

When prompting language models to complete a task, users often leave important aspects
unsaid. While asking questions could resolve this ambiguity\citep [GATE;][]{li2023eliciting} …

Generative models are a class of AI models capable of creating new instances of data by
learning and sampling from their statistical distributions. In recent years, these models have …

Algorithm Design (AD) is crucial for effective problem-solving across various domains. The
advent of Large Language Models (LLMs) has notably enhanced the automation and …

We give a model of how to infer natural language rules by doing experiments. The model
integrates Large Language Models (LLMs) with Monte Carlo algorithms for probabilistic …

While previous chapters focused on recommendation systems (RSs) based on
standardized, non-verbal user feedback such as purchases, views, and clicks--the advent of …

LLMs are tuned to follow instructions (aligned) by learning which of two outputs users prefer
for a prompt. However, this preference data format does not convey why users prefer …

Everyone aspires to achieve personal goals. However, getting started is often complex and
daunting, especially for large projects. AI has the potential to create plans and help jumpstart …

We present an online method for embodied agents to learn and accomplish diverse user
goals. While offline methods like RLHF can represent various goals but require large …