Li, Jingling

12 publications

ICLR 2025 Prompting Fairness: Integrating Causality to Debias Large Language Models Jingling Li, Zeyu Tang, Xiaoyu Liu, Peter Spirtes, Kun Zhang, Liu Leqi, Yang Liu

NeurIPSW 2024 CSRec: Rethinking Sequential Recommendation from a Causal Perspective. Xiaoyu Liu, Jiaxin Yuan, Yuhang Zhou, Jingling Li, Furong Huang, Wei Ai

NeurIPS 2024 How to Solve Contextual Goal-Oriented Problems with Offline Datasets? Ying Fan, Jingling Li, Adith Swaminathan, Aditya Modi, Ching-An Cheng

ICLRW 2024 Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing Framework Jingling Li, Zeyu Tang, Xiaoyu Liu, Peter Spirtes, Kun Zhang, Liu Leqi, Yang Liu

ICML 2023 Hindsight Learning for MDPs with Exogenous Inputs Sean R. Sinclair, Felipe Vieira Frujeri, Ching-An Cheng, Luke Marshall, Hugo De Oliveira Barbalho, Jingling Li, Jennifer Neville, Ishai Menache, Adith Swaminathan

NeurIPSW 2023 Simple Data Sharing for Multi-Tasked Goal-Oriented Problems Ying Fan, Jingling Li, Adith Swaminathan, Aditya Modi, Ching-An Cheng

NeurIPSW 2023 Simple Data Sharing for Multi-Tasked Goal-Oriented Problems Ying Fan, Jingling Li, Adith Swaminathan, Aditya Modi, Ching-An Cheng

NeurIPS 2021 How Does a Neural Network's Architecture Impact Its Robustness to Noisy Labels? Jingling Li, Mozhi Zhang, Keyulu Xu, John Dickerson, Jimmy Ba

ICLR 2021 How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks Keyulu Xu, Mozhi Zhang, Jingling Li, Simon Shaolei Du, Ken-Ichi Kawarabayashi, Stefanie Jegelka

NeurIPS 2021 VQ-GNN: A Universal Framework to Scale up Graph Neural Networks Using Vector Quantization Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, Tom Goldstein

AISTATS 2020 Understanding Generalization in Deep Learning via Tensor Methods Jingling Li, Yanchao Sun, Jiahao Su, Taiji Suzuki, Furong Huang

ICLR 2020 What Can Neural Networks Reason About? Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka