6 citations · 7 across the 9 of their papers we have counts for
6 papers · 1 filter
Calibrating conditional risk
Andrey Vasilyev, Yikai Wang, Xiaocheng Li +1
We introduce and study the problem of calibrating conditional risk, which involves estimating the expected loss of a prediction model conditional on input features. We analyze this…
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…
Reward Modeling with Ordinal Feedback: Wisdom of the Crowd
Shang Liu, Yu Pan, Guanting Chen +1
Learning a reward model (RM) from human preferences has been an important component in aligning large language models (LLMs). The canonical setup of learning RMs from pairwise pref…
Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification
Shang Liu, Zhongze Cai, Guanting Chen +1
Predicting simple function classes has been widely used as a testbed for developing theory and understanding of the trained Transformer's in-context learning (ICL) ability. In this…
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…
Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach
Linyu Liu, Yu Pan, Xiaocheng Li +1
In this paper, we study the problem of uncertainty estimation and calibration for LLMs. We begin by formulating the uncertainty estimation problem, a relevant yet underexplored are…