Ce Zhang
Ce Zhang
Together AI; University of Chicago
Verified email at - Homepage
Cited by
Cited by
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
X Lian, C Zhang, H Zhang, CJ Hsieh, W Zhang, J Liu
NIPS, 2017
Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
KH Yu, C Zhang, GJ Berry, RB Altman, C Ré, DL Rubin, M Snyder
Nature communications 7 (1), 12474, 2016
Holistic Evaluation of Language Models
P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ...
arXiv preprint arXiv:2211.09110, 2022
Asynchronous Decentralized Parallel Stochastic Gradient Descent
X Lian, W Zhang, C Zhang, J Liu
ICML, 2018
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
D Cao, Y Wang, J Duan, C Zhang, X Zhu, C Huang, Y Tong, B Xu, J Bai, ...
Advances in Neural Information Processing Systems 33, 2020
Towards Efficient Data Valuation Based on the Shapley Value
R Jia, D Dao, B Wang, FA Hubis, M Gurel, N Hynes, B Li, C Zhang, ...
D2: Decentralized Training over Decentralized Data
H Tang, X Lian, M Yan, C Zhang, J Liu
ICML, 2018
Incremental knowledge base construction using deepdive
J Shin, S Wu, F Wang, C De Sa, C Zhang, C Ré
Proceedings of the VLDB Endowment International Conference on Very Large …, 2015
Communication compression for decentralized training
H Tang, S Gan, C Zhang, T Zhang, J Liu
Advances in Neural Information Processing Systems 31, 7652-7662, 2018
Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit
K Schawinski, C Zhang, H Zhang, L Fowler, GK Santhanam
Monthly Notices of the Royal Astronomical Society: Letters 467 (1), L110-L114, 2017
DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference.
F Niu, C Zhang, C Ré, JW Shavlik
VLDS 12, 25-28, 2012
Doublesqueeze: Parallel stochastic gradient descent with double-pass error-compensated compression
H Tang, C Yu, X Lian, T Zhang, J Liu
International Conference on Machine Learning, 6155-6165, 2019
ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning
H Zhang, J Li, K Kara, D Alistarh, J Liu, C Zhang
International Conference on Machine Learning, 4035-4043, 2017
Advances, challenges and opportunities in creating data for trustworthy AI
W Liang, GA Tadesse, D Ho, L Fei-Fei, M Zaharia, C Zhang, J Zou
Nature Machine Intelligence 4 (8), 669-677, 2022
Heterogeneity-Aware Distributed Parameter Servers
J Jiang, B Cui, C Zhang, LYHAD Parameter
2017 ACM International Conference on Management of Data (SIGMOD) 10 (3035918 …, 2017
Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms
R Jia, D Dao, B Wang, FA Hubis, NM Gurel, B Li, C Zhang, CJ Spanos, ...
VLDB, 2019
Taming the wild: A unified analysis of hogwild-style algorithms
CM De Sa, C Zhang, K Olukotun, C Ré
Advances in neural information processing systems 28, 2674-2682, 2015
A principled approach to data valuation for federated learning
T Wang, J Rausch, C Zhang, R Jia, D Song
Federated Learning: Privacy and Incentive, 153-167, 2020
DL2: Training and Querying Neural Networks with Logic
M Fischer, M Balunovic, D Drachsler-Cohen, T Gehr, C Zhang, M Vechev
ICML, 2019
Materialization optimizations for feature selection workloads
C Zhang, A Kumar, C Ré
ACM Transactions on Database Systems (TODS) 41 (1), 2, 2016
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