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Geng Ji
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Metabalance: improving multi-task recommendations via adapting gradient magnitudes of auxiliary tasks
Y He, X Feng, C Cheng, G Ji, Y Guo, J Caverlee
Proceedings of the ACM Web Conference 2022, 2205-2215, 2022
352022
Variational training for large-scale noisy-OR Bayesian networks
G Ji, D Cheng, H Ning, C Yuan, H Zhou, L Xiong, EB Sudderth
Uncertainty in Artificial Intelligence, 873-882, 2019
72019
Bayesian paragraph vectors
G Ji, R Bamler, EB Sudderth, S Mandt
arXiv preprint arXiv:1711.03946, 2017
72017
From patches to images: a nonparametric generative model
G Ji, MC Hughes, EB Sudderth
International Conference on Machine Learning, 1675-1683, 2017
72017
Marginalized stochastic natural gradients for black-box variational inference
G Ji, D Sujono, EB Sudderth
International conference on machine learning, 4870-4881, 2021
62021
Correction to: Learning to bid and rank together in recommendation systems
G Ji, W Jiang, J Li, FM Fahid, Z Chen, Y Li, J Xiao, C Bao, Z Zhu
Machine Learning, 1-1, 2024
2024
Learning to bid and rank together in recommendation systems
G Ji, W Jiang, J Li, FM Fahid, Z Chen, Y Li, J Xiao, C Bao, Z Zhu
Machine Learning, 1-15, 2023
2023
Effective Monte Carlo Variational Inference for Binary-Variable Probabilistic Programs
G Ji, EB Sudderth
International Conference on Probabilistic Programming, 2020
2020
Efficient Variational Inference for Hierarchical Models of Images, Text, and Networks
G Ji
University of California, Irvine, 2019
2019
Supplemental Material From Patches to Images: A Nonparametric Generative Model
G Ji, MC Hughes, EB Sudderth
From Patches to Natural Images via Hierarchical Dirichlet Processes
G Ji, MC Hughes, EB Sudderth
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Articles 1–11