Search Results for author: Zheyuan Hu

Found 6 papers, 4 papers with code

Enhancing Collaborative Filtering Recommender with Prompt-Based Sentiment Analysis

1 code implementation19 Jul 2022 Elliot Dang, Zheyuan Hu, Tong Li

We build the recommenders on the Amazon US Reviews dataset, and tune the pretrained BERT and RoBERTa with the traditional fine-tuned paradigm as well as the new prompt-based learning paradigm.

Collaborative Filtering Sentiment Analysis

Integrated Latent Heterogeneity and Invariance Learning in Kernel Space

no code implementations NeurIPS 2021 Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen

The ability to generalize under distributional shifts is essential to reliable machine learning, while models optimized with empirical risk minimization usually fail on non-$i. i. d$ testing data.

Kernelized Heterogeneous Risk Minimization

1 code implementation24 Oct 2021 Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen

The ability to generalize under distributional shifts is essential to reliable machine learning, while models optimized with empirical risk minimization usually fail on non-$i. i. d$ testing data.

When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?

no code implementations20 Sep 2021 Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi

Specifically, for general multi-layer PINNs and XPINNs, we first provide a prior generalization bound via the complexity of the target functions in the PDE problem, and a posterior generalization bound via the posterior matrix norms of the networks after optimization.

Heterogeneous Risk Minimization

1 code implementation9 May 2021 Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen

In this paper, we propose Heterogeneous Risk Minimization (HRM) framework to achieve joint learning of latent heterogeneity among the data and invariant relationship, which leads to stable prediction despite distributional shifts.

ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction

1 code implementation7 Jul 2020 Zhongkai Hao, Chengqiang Lu, Zheyuan Hu, Hao Wang, Zhenya Huang, Qi Liu, Enhong Chen, Cheekong Lee

Here we propose a novel framework called Active Semi-supervised Graph Neural Network (ASGN) by incorporating both labeled and unlabeled molecules.

Active Learning Molecular Property Prediction

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