2 citations · 3 across the 10 of their papers we have counts for
11 papers
LLM-Powered Predictive Decision-Making for Sustainable Data Center Operations
Hanzhao Wang, Jingxuan Wu, Yumeng Li +2
The growing demand for AI-driven workloads, particularly from Large Language Models (LLMs), has raised concerns about the significant energy and resource consumption in data center…
Calibrating an Imperfect Auxiliary Predictor for Unobserved No-Purchase Choice
Jiangkai Xiong, Kalyan Talluri, Hanzhao Wang
Firms typically cannot observe key consumer actions: whether customers buy from a competitor, choose not to buy, or even fully consider the firm's offer. This missing outside-optio…
Learning Shortest Paths When Data is Scarce
Dmytro Matsypura, Yu Pan, Hanzhao Wang
Digital twins and other simulators are increasingly used to support routing decisions in large-scale networks. However, simulator outputs often exhibit systematic bias, while groun…
Online-Optimized RAG for Tool Use and Function Calling
Yu Pan, Xiaocheng Li, Hanzhao Wang
In many applications, retrieval-augmented generation (RAG) drives tool use and function calling by embedding the (user) queries and matching them to pre-specified tool/function des…
OMGPT: A Sequence Modeling Framework for Data-driven Operational Decision Making
Hanzhao Wang, Guanting Chen, Kalyan Talluri +1
We build a Generative Pre-trained Transformer (GPT) model from scratch to solve sequential decision making tasks arising in contexts of operations research and management science w…
Incentivizing High-Quality Human Annotations with Golden Questions
Shang Liu, Zhongze Cai, Hanzhao Wang +2
Human-annotated data plays a vital role in training large language models (LLMs), such as supervised fine-tuning and human preference alignment. However, it is not guaranteed that…
How Humans Help LLMs: Assessing and Incentivizing Human Preference Annotators
Shang Liu, Hanzhao Wang, Zhongyao Ma +1
Human-annotated preference data play an important role in aligning large language models (LLMs). In this paper, we study two connected questions: how to monitor the quality of huma…
Understanding the Training and Generalization of Pretrained Transformer for Sequential Decision Making
Hanzhao Wang, Yu Pan, Fupeng Sun +4
In this paper, we consider the supervised pre-trained transformer for a class of sequential decision-making problems. The class of considered problems is a subset of the general fo…
Towards Better Statistical Understanding of Watermarking LLMs
Zhongze Cai, Shang Liu, Hanzhao Wang +2
In this paper, we study the problem of watermarking large language models (LLMs). We consider the trade-off between model distortion and detection ability and formulate it as a con…
Transformer Choice Net: A Transformer Neural Network for Choice Prediction
Hanzhao Wang, Xiaocheng Li, Kalyan Talluri
Discrete-choice models, such as Multinomial Logit, Probit, or Mixed-Logit, are widely used in Marketing, Economics, and Operations Research: given a set of alternatives, the custom…
Learning to Make Adherence-Aware Advice
Guanting Chen, Xiaocheng Li, Chunlin Sun +1
As artificial intelligence (AI) systems play an increasingly prominent role in human decision-making, challenges surface in the realm of human-AI interactions. One challenge arises…