1 citations · 2 across the 5 of their papers we have counts for
5 papers
Parameter-Free Encoders Remain Viable for RDB Foundation Models
Linjie Xu, David Wipf
Given a relational database (RDB) storing heterogeneous tabular information, how can we predict missing (or future) values in some target column of interest? As the space of potent…
RDBLearn: Simple In-Context Prediction Over Relational Databases
Yanlin Zhang, Linjie Xu, Quan Gan +2
Recent advances in tabular in-context learning (ICL) show that a single pretrained model can adapt to new prediction tasks from a small set of labeled examples, avoiding per-task t…
C-MORL: Multi-Objective Reinforcement Learning through Efficient Discovery of Pareto Front
Ruohong Liu, Yuxin Pan, Linjie Xu +4
Multi-objective reinforcement learning (MORL) excels at handling rapidly changing preferences in tasks that involve multiple criteria, even for unseen preferences. However, previou…
Unveiling Markov Heads in Pretrained Language Models for Offline Reinforcement Learning
Wenhao Zhao, Qiushui Xu, Linjie Xu +4
Recently, incorporating knowledge from pretrained language models (PLMs) into decision transformers (DTs) has generated significant attention in offline reinforcement learning (RL)…
Protecting Your LLMs with Information Bottleneck
Zichuan Liu, Zefan Wang, Linjie Xu +6
The advent of large language models (LLMs) has revolutionized the field of natural language processing, yet they might be attacked to produce harmful content. Despite efforts to et…