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ORCIDCe Zhang 0001
Person information
- affiliation: ETH Zurich, Institut für Computing Platforms, Switzerland
- affiliation (former): Stanford University, Computer Science Department, CA, USA
- affiliation (former): University of Wisconsin-Madison, Department of Computer Science, Madison, WI, USA
- affiliation (former): Peking University, Beijing, China
Other persons with the same name
- Ce Zhang — disambiguation page
- Ce Zhang 0002 — Renmin University of China, School of Information, Beijing, China
- Ce Zhang 0003 — Shenyang Ligong University, School of Information Science and Engineering, China
- Ce Zhang 0004
![[0000-0001-5571-9766]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0001-5571-9766")
(aka: Delvin Ce Zhang) — Singapore Management University, Singapore - Ce Zhang 0005 — University of Bristol, School of Geographical Sciences, UK (and 1 more)
- Ce Zhang 0006 — Chinese Academy of Sciences, Institute of Automation, Interactive Digital Media Technology Research Center, Beijing, China
- Ce Zhang 0007 — Hong Kong Baptist University, China
- Ce Zhang 0008 — Virginia Tech, Blacksburg, VA, USA
- Ce Zhang 0009 — Carnegie Mellon University, Machine Learning Department, Pittsburgh, PA, USA
- Ce Zhang 0010 — University of North Carolina at Chapel Hill, NC, USA
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2020 – today
- 2024
[j51]Stefan Grafberger, Zeyu Zhang, Sebastian Schelter, Ce Zhang:
Red Onions, Soft Cheese and Data: From Food Safety to Data Traceability for Responsible AI. IEEE Data Eng. Bull. 47(1): 63-81 (2024)- [j50]Huaijun Jiang, Yu Shen, Yang Li, Beicheng Xu, Sixian Du, Wentao Zhang, Ce Zhang, Bin Cui:
OpenBox: A Python Toolkit for Generalized Black-box Optimization. J. Mach. Learn. Res. 25: 120:1-120:11 (2024) - [j49]Jiawei Jiang, Shaoduo Gan, Bo Du, Gustavo Alonso, Ana Klimovic, Ankit Singla, Wentao Wu, Sheng Wang, Ce Zhang:
A systematic evaluation of machine learning on serverless infrastructure. VLDB J. 33(2): 425-449 (2024) - [j48]Jiawei Jiang, Yi Wei, Yu Liu, Wentao Wu, Chuang Hu, Zhigao Zheng, Ziyi Zhang, Yingxia Shao, Ce Zhang:
How good are machine learning clouds? Benchmarking two snapshots over 5 years. VLDB J. 33(3): 833-857 (2024) - [j47]Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang:
Stochastic gradient descent without full data shuffle: with applications to in-database machine learning and deep learning systems. VLDB J. 33(5): 1231-1255 (2024) - [c141]Steve Rhyner, Haocong Luo, Juan Gómez-Luna, Mohammad Sadrosadati, Jiawei Jiang, Ataberk Olgun, Harshita Gupta, Ce Zhang, Onur Mutlu:
PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory System. PACT 2024: 201-218 - [c140]Bojan Karlas, David Dao, Matteo Interlandi, Sebastian Schelter, Wentao Wu, Ce Zhang:
Data Debugging with Shapley Importance over Machine Learning Pipelines. ICLR 2024 - [c139]Qinbin Li, Chulin Xie, Xiaojun Xu, Xiaoyuan Liu, Ce Zhang, Bo Li, Bingsheng He, Dawn Song:
Effective and Efficient Federated Tree Learning on Hybrid Data. ICLR 2024 - [c138]Michael Poli, Armin W. Thomas, Eric Nguyen, Pragaash Ponnusamy, Björn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian Hie, Stefano Ermon, Christopher Ré, Ce Zhang, Stefano Massaroli:
Mechanistic Design and Scaling of Hybrid Architectures. ICML 2024 - [c137]Chulin Xie, Pin-Yu Chen, Qinbin Li, Arash Nourian, Ce Zhang, Bo Li:
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM. SaTML 2024: 443-471 - [c136]Qi Chen, Xiubo Geng, Corby Rosset, Carolyn Buractaon, Jingwen Lu, Tao Shen, Kun Zhou, Chenyan Xiong, Yeyun Gong, Paul N. Bennett, Nick Craswell, Xing Xie, Fan Yang, Bryan Tower, Nikhil Rao, Anlei Dong, Wenqi Jiang, Zheng Liu, Mingqin Li, Chuanjie Liu, Zengzhong Li, Rangan Majumder, Jennifer Neville, Andy Oakley, Knut Magne Risvik, Harsha Vardhan Simhadri, Manik Varma, Yujing Wang, Linjun Yang, Mao Yang, Ce Zhang:
MS MARCO Web Search: A Large-scale Information-rich Web Dataset with Millions of Real Click Labels. WWW (Companion Volume) 2024: 292-301 - [i140]Lijie Xu, Chulin Xie, Yiran Guo, Gustavo Alonso, Bo Li, Guoliang Li, Wei Wang, Wentao Wu, Ce Zhang:
TablePuppet: A Generic Framework for Relational Federated Learning. CoRR abs/2403.15839 (2024) - [i139]Michael Poli, Armin W. Thomas, Eric Nguyen, Pragaash Ponnusamy, Björn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian Hie, Stefano Ermon, Christopher Ré, Ce Zhang, Stefano Massaroli:
Mechanistic Design and Scaling of Hybrid Architectures. CoRR abs/2403.17844 (2024) - [i138]Steve Rhyner, Haocong Luo, Juan Gómez-Luna, Mohammad Sadrosadati, Jiawei Jiang, Ataberk Olgun, Harshita Gupta, Ce Zhang, Onur Mutlu:
Analysis of Distributed Optimization Algorithms on a Real Processing-In-Memory System. CoRR abs/2404.07164 (2024) - [i137]Qi Chen, Xiubo Geng, Corby Rosset, Carolyn Buractaon, Jingwen Lu, Tao Shen, Kun Zhou, Chenyan Xiong, Yeyun Gong, Paul N. Bennett, Nick Craswell, Xing Xie, Fan Yang, Bryan Tower, Nikhil Rao, Anlei Dong, Wenqi Jiang, Zheng Liu, Mingqin Li, Chuanjie Liu, Zengzhong Li, Rangan Majumder, Jennifer Neville, Andy Oakley, Knut Magne Risvik, Harsha Vardhan Simhadri, Manik Varma, Yujing Wang, Linjun Yang, Mao Yang, Ce Zhang:
MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels. CoRR abs/2405.07526 (2024) - [i136]Josh Veitch-Michaelis, Andrew Cottam, Daniella Schweizer, Eben N. Broadbent, David Dao, Ce Zhang, Angelica Almeyda Zambrano, Simeon Max:
OAM-TCD: A globally diverse dataset of high-resolution tree cover maps. CoRR abs/2407.11743 (2024) - [i135]Baijun Cheng, Ce Zhang, Kailong Wang, Ling Shi, Yang Liu, Haoyu Wang, Yao Guo, Xiangqun Chen:
Semantic-Enhanced Indirect Call Analysis with Large Language Models. CoRR abs/2408.04344 (2024) - 2023
- [j46]Yujing Wang, Yaming Yang, Zhuo Li, Jiangang Bai, Mingliang Zhang, Xiangtai Li, Jing Yu, Ce Zhang, Gao Huang, Yunhai Tong:
Convolution-Enhanced Evolving Attention Networks. IEEE Trans. Pattern Anal. Mach. Intell. 45(7): 8176-8192 (2023) - [j45]Yang Li, Huaijun Jiang, Yu Shen, Yide Fang, Xiaofeng Yang, Danqing Huang, Xinyi Zhang, Wentao Zhang, Ce Zhang, Peng Chen, Bin Cui:
Towards General and Efficient Online Tuning for Spark. Proc. VLDB Endow. 16(12): 3570-3583 (2023) - [j44]Xupeng Miao, Wentao Zhang, Yingxia Shao, Bin Cui, Lei Chen, Ce Zhang, Jiawei Jiang:
Lasagne: A Multi-Layer Graph Convolutional Network Framework via Node-Aware Deep Architecture. IEEE Trans. Knowl. Data Eng. 35(2): 1721-1733 (2023) - [j43]Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda:
Holistic Evaluation of Language Models. Trans. Mach. Learn. Res. 2023 (2023) - [j42]Yang Li, Yu Shen, Wentao Zhang, Ce Zhang, Bin Cui:
VolcanoML: speeding up end-to-end AutoML via scalable search space decomposition. VLDB J. 32(2): 389-413 (2023) - [c135]Cédric Renggli, Luka Rimanic, Luka Kolar, Wentao Wu, Ce Zhang:
Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label Noise. ICDE 2023: 218-231 - [c134]Guangyu Zhang, Chunhua Li, Ke Zhou, Li Liu, Ce Zhang, Wancheng Chen, Haotian Fang, Bin Cheng, Jie Yang, Jiashu Xing:
DBCatcher: A Cloud Database Online Anomaly Detection System based on Indicator Correlation. ICDE 2023: 1126-1139 - [c133]Johannes Rausch, Gentiana Rashiti, Maxim Gusev, Ce Zhang, Stefan Feuerriegel:
DSG: An End-to-End Document Structure Generator. ICDM 2023: 518-527 - [c132]Yilmazcan Özyurt, Stefan Feuerriegel, Ce Zhang:
Contrastive Learning for Unsupervised Domain Adaptation of Time Series. ICLR 2023 - [c131]Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Ré, Beidi Chen:
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time. ICML 2023: 22137-22176 - [c130]Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Beidi Chen, Percy Liang, Christopher Ré, Ion Stoica, Ce Zhang:
FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU. ICML 2023: 31094-31116 - [c129]Zhen Wang, Weirui Kuang, Ce Zhang, Bolin Ding, Yaliang Li:
FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization. ICML 2023: 35908-35948 - [c128]Jue Wang, Yucheng Lu, Binhang Yuan, Beidi Chen, Percy Liang, Christopher De Sa, Christopher Ré, Ce Zhang:
CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks. ICML 2023: 36058-36076 - [c127]Mayee F. Chen, Nicholas Roberts, Kush Bhatia, Jue Wang, Ce Zhang, Frederic Sala, Christopher Ré:
Skill-it! A data-driven skills framework for understanding and training language models. NeurIPS 2023 - [c126]Stefano Massaroli, Michael Poli, Daniel Y. Fu, Hermann Kumbong, Rom N. Parnichkun, David W. Romero, Aman Timalsina, Quinn McIntyre, Beidi Chen, Atri Rudra, Ce Zhang, Christopher Ré, Stefano Ermon, Yoshua Bengio:
Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions. NeurIPS 2023 - [c125]Mark Mazumder, Colby R. Banbury, Xiaozhe Yao, Bojan Karlas, William Gaviria Rojas, Sudnya Frederick Diamos, Greg Diamos, Lynn He, Alicia Parrish, Hannah Rose Kirk, Jessica Quaye, Charvi Rastogi, Douwe Kiela, David Jurado, David Kanter, Rafael Mosquera, Will Cukierski, Juan Ciro, Lora Aroyo, Bilge Acun, Lingjiao Chen, Mehul Raje, Max Bartolo, Evan Sabri Eyuboglu, Amirata Ghorbani, Emmett D. Goodman, Addison Howard, Oana Inel, Tariq Kane, Christine R. Kirkpatrick, D. Sculley, Tzu-Sheng Kuo, Jonas W. Mueller, Tristan Thrush, Joaquin Vanschoren, Margaret Warren, Adina Williams, Serena Yeung, Newsha Ardalani, Praveen K. Paritosh, Ce Zhang, James Y. Zou, Carole-Jean Wu, Cody Coleman, Andrew Y. Ng, Peter Mattson, Vijay Janapa Reddi:
DataPerf: Benchmarks for Data-Centric AI Development. NeurIPS 2023 - [c124]Maurice Weber, Carlo Siebenschuh, Rory Butler, Anton Alexandrov, Valdemar Thanner, Georgios Tsolakis, Haris Jabbar, Ian T. Foster, Bo Li, Rick Stevens, Ce Zhang:
WordScape: a Pipeline to extract multilingual, visually rich Documents with Layout Annotations from Web Crawl Data. NeurIPS 2023 - [c123]Jiawei Zhang, Linyi Li, Ce Zhang, Bo Li:
CARE: Certifiably Robust Learning with Reasoning via Variational Inference. SaTML 2023: 554-574 - [c122]Sebastian Schelter, Stefan Grafberger, Shubha Guha, Bojan Karlas, Ce Zhang:
Proactively Screening Machine Learning Pipelines with ARGUSEYES. SIGMOD Conference Companion 2023: 91-94 - [c121]Maurice Weber, Xiaojun Xu, Bojan Karlas, Ce Zhang, Bo Li:
RAB: Provable Robustness Against Backdoor Attacks. SP 2023: 1311-1328 - [e1]Ana Gainaru, Ce Zhang, Chunjie Luo:
Benchmarking, Measuring, and Optimizing - 14th BenchCouncil International Symposium, Bench 2022, Virtual Event, November 7-9, 2022, Revised Selected Papers. Lecture Notes in Computer Science 13852, Springer 2023, ISBN 978-3-031-31179-6 [contents] - [i134]Susie Xi Rao, Peter H. Egger, Ce Zhang:
Hierarchical Classification of Research Fields in the "Web of Science" Using Deep Learning. CoRR abs/2302.00390 (2023) - [i133]Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Daniel Y. Fu, Zhiqiang Xie, Beidi Chen, Clark W. Barrett, Joseph E. Gonzalez, Percy Liang, Christopher Ré, Ion Stoica, Ce Zhang:
High-throughput Generative Inference of Large Language Models with a Single GPU. CoRR abs/2303.06865 (2023) - [i132]Huaijun Jiang, Yu Shen, Yang Li, Wentao Zhang, Ce Zhang, Bin Cui:
OpenBox: A Python Toolkit for Generalized Black-box Optimization. CoRR abs/2304.13339 (2023) - [i131]Xiaozhong Lyu, Stefan Grafberger, Samantha Biegel, Shaopeng Wei, Meng Cao, Sebastian Schelter, Ce Zhang:
Improving Retrieval-Augmented Large Language Models via Data Importance Learning. CoRR abs/2307.03027 (2023) - [i130]Mayee F. Chen, Nicholas Roberts, Kush Bhatia, Jue Wang, Ce Zhang, Frederic Sala, Christopher Ré:
Skill-it! A Data-Driven Skills Framework for Understanding and Training Language Models. CoRR abs/2307.14430 (2023) - [i129]Qiang Huang, Jiawei Jiang, Susie Xi Rao, Ce Zhang, Zhichao Han, Zitao Zhang, Xin Wang, Yongjun He, Quanqing Xu, Yang Zhao, Chuang Hu, Shuo Shang, Bo Du:
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks. CoRR abs/2308.16385 (2023) - [i128]Yang Li, Huaijun Jiang, Yu Shen, Yide Fang, Xiaofeng Yang, Danqing Huang, Xinyi Zhang, Wentao Zhang, Ce Zhang, Peng Chen, Bin Cui:
Towards General and Efficient Online Tuning for Spark. CoRR abs/2309.01901 (2023) - [i127]Johannes Rausch, Gentiana Rashiti, Maxim Gusev, Ce Zhang, Stefan Feuerriegel:
DSG: An End-to-End Document Structure Generator. CoRR abs/2310.09118 (2023) - [i126]Yilmazcan Özyurt, Stefan Feuerriegel, Ce Zhang:
In-Context Few-Shot Relation Extraction via Pre-Trained Language Models. CoRR abs/2310.11085 (2023) - [i125]Qinbin Li, Chulin Xie, Xiaojun Xu, Xiaoyuan Liu, Ce Zhang, Bo Li, Bingsheng He, Dawn Song:
Effective and Efficient Federated Tree Learning on Hybrid Data. CoRR abs/2310.11865 (2023) - [i124]Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Ré, Beidi Chen:
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time. CoRR abs/2310.17157 (2023) - [i123]Stefano Massaroli, Michael Poli, Daniel Y. Fu, Hermann Kumbong, Rom N. Parnichkun, Aman Timalsina, David W. Romero, Quinn McIntyre, Beidi Chen, Atri Rudra, Ce Zhang, Christopher Ré, Stefano Ermon, Yoshua Bengio:
Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions. CoRR abs/2310.18780 (2023) - [i122]Luis Oala, Manil Maskey, Lilith Bat-Leah, Alicia Parrish, Nezihe Merve Gürel, Tzu-Sheng Kuo, Yang Liu, Rotem Dror, Danilo Brajovic, Xiaozhe Yao, Max Bartolo, William Gaviria Rojas, Ryan Hileman, Rainier Aliment, Michael W. Mahoney, Meg Risdal, Matthew Lease, Wojciech Samek, Debojyoti Dutta, Curtis G. Northcutt, Cody Coleman, Braden Hancock, Bernard Koch, Girmaw Abebe Tadesse, Bojan Karlas, Ahmed M. Alaa, Adji Bousso Dieng, Natasha F. Noy, Vijay Janapa Reddi, James Zou, Praveen K. Paritosh, Mihaela van der Schaar, Kurt D. Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson:
DMLR: Data-centric Machine Learning Research - Past, Present and Future. CoRR abs/2311.13028 (2023) - [i121]Maurice Weber, Carlo Siebenschuh, Rory Butler, Anton Alexandrov, Valdemar Thanner, Georgios Tsolakis, Haris Jabbar, Ian T. Foster, Bo Li, Rick Stevens, Ce Zhang:
WordScape: a Pipeline to extract multilingual, visually rich Documents with Layout Annotations from Web Crawl Data. CoRR abs/2312.10188 (2023) - 2022
- [b1]Jiawei Jiang, Bin Cui, Ce Zhang:
Distributed Machine Learning and Gradient Optimization. Springer 2022, ISBN 978-981-16-3419-2, pp. 1-169 - [j41]Stefan Feuerriegel, Yash Raj Shrestha, Georg von Krogh, Ce Zhang:
Bringing artificial intelligence to business management. Nat. Mach. Intell. 4(7): 611-613 (2022) - [j40]Weixin Liang, Girmaw Abebe Tadesse, Daniel E. Ho, Li Fei-Fei, Matei Zaharia, Ce Zhang, James Zou:
Advances, challenges and opportunities in creating data for trustworthy AI. Nat. Mach. Intell. 4(8): 669-677 (2022) - [j39]Weixin Liang, Girmaw Abebe Tadesse, Daniel E. Ho, Li Fei-Fei, Matei Zaharia, Ce Zhang, James Zou:
Author Correction: Advances, challenges and opportunities in creating data for trustworthy AI. Nat. Mac. Intell. 4(10): 904 (2022) - [j38]Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Jixiang Li, Ji Liu, Ce Zhang, Bin Cui:
Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale. Proc. VLDB Endow. 15(6): 1256-1265 (2022) - [j37]Cédric Renggli, Xiaozhe Yao, Luka Kolar, Luka Rimanic, Ana Klimovic, Ce Zhang:
SHiFT: An Efficient, Flexible Search Engine for Transfer Learning. Proc. VLDB Endow. 16(2): 304-316 (2022) - [j36]Fotis Psallidas, Yiwen Zhu, Bojan Karlas, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, K. Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos:
Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines. SIGMOD Rec. 51(2): 30-37 (2022) - [c120]Susie Xi Rao, Johannes Rausch, Peter H. Egger, Ce Zhang:
TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets. SDU@AAAI 2022 - [c119]Gyri Reiersen, David Dao, Björn Lütjens, Konstantin Klemmer, Kenza Amara, Attila Steinegger, Ce Zhang, Xiaoxiang Zhu:
ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery. AAAI 2022: 12119-12125 - [c118]Patrick Damme, Marius Birkenbach, Constantinos Bitsakos, Matthias Boehm, Philippe Bonnet, Florina M. Ciorba, Mark Dokter, Pawel Dowgiallo, Ahmed Eleliemy, Christian Faerber, Georgios I. Goumas, Dirk Habich, Niclas Hedam, Marlies Hofer, Wenjun Huang, Kevin Innerebner, Vasileios Karakostas, Roman Kern, Tomaz Kosar, Alexander Krause, Daniel Krems, Andreas Laber, Wolfgang Lehner, Eric Mier, Marcus Paradies, Bernhard Peischl, Gabrielle Poerwawinata, Stratos Psomadakis, Tilmann Rabl, Piotr Ratuszniak, Pedro Silva, Nikolai Skuppin, Andreas Starzacher, Benjamin Steinwender, Ilin Tolovski, Pinar Tözün, Wojciech Ulatowski, Yuanyuan Wang, Izajasz P. Wrosz, Ales Zamuda, Ce Zhang, Xiaoxiang Zhu:
DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines. CIDR 2022 - [c117]Sebastian Schelter, Stefan Grafberger, Shubha Guha, Olivier Sprangers, Bojan Karlas, Ce Zhang:
Screening Native Machine Learning Pipelines with ArgusEyes. CIDR 2022 - [c116]Mingxuan Lu, Zhichao Han, Susie Xi Rao, Zitao Zhang, Yang Zhao, Yinan Shan, Ramesh Raghunathan, Ce Zhang, Jiawei Jiang:
BRIGHT - Graph Neural Networks in Real-time Fraud Detection. CIKM 2022: 3342-3351 - [c115]Cédric Renggli, André Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, Mario Lucic:
Which Model to Transfer? Finding the Needle in the Growing Haystack. CVPR 2022: 9195-9204 - [c114]Sabri Eyuboglu, Bojan Karlas, Christopher Ré, Ce Zhang, James Zou:
dcbench: a benchmark for data-centric AI systems. DEEM@SIGMOD 2022: 9:1-9:4 - [c113]Han Zhang, Zhefan Yu, Ce Zhang, Ruotian Zhang, Yuyang Liu, Seung Hee Lee:
User-Centered Information Architecture of Vehicle AR-HUD Interface. HCI (34) 2022: 309-325 - [c112]Xupeng Miao, Wentao Zhang, Yingxia Shao, Bin Cui, Lei Chen, Ce Zhang, Jiawei Jiang:
Lasagne: A Multi-Layer Graph Convolutional Network Framework via Node-aware Deep Architecture (Extended Abstract). ICDE 2022: 1561-1562 - [c111]Alfonso Amayuelas, Shuai Zhang, Susie Xi Rao, Ce Zhang:
Neural Methods for Logical Reasoning over Knowledge Graphs. ICLR 2022 - [c110]Yuexiang Xie, Zhen Wang, Yaliang Li, Ce Zhang, Jingren Zhou, Bolin Ding:
iFlood: A Stable and Effective Regularizer. ICLR 2022 - [c109]Maurice Weber, Linyi Li, Boxin Wang, Zhikuan Zhao, Bo Li, Ce Zhang:
Certifying Out-of-Domain Generalization for Blackbox Functions. ICML 2022: 23527-23548 - [c108]Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Zhi Yang, Ce Zhang, Bin Cui:
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning. KDD 2022: 956-966 - [c107]Yang Li, Yu Shen, Huaijun Jiang, Tianyi Bai, Wentao Zhang, Ce Zhang, Bin Cui:
Transfer Learning based Search Space Design for Hyperparameter Tuning. KDD 2022: 967-977 - [c106]Xiangru Lian, Binhang Yuan, Xuefeng Zhu, Yulong Wang, Yongjun He, Honghuan Wu, Lei Sun, Haodong Lyu, Chengjun Liu, Xing Dong, Yiqiao Liao, Mingnan Luo, Congfei Zhang, Jingru Xie, Haonan Li, Lei Chen, Renjie Huang, Jianying Lin, Chengchun Shu, Xuezhong Qiu, Zhishan Liu, Dongying Kong, Lei Yuan, Hai Yu, Sen Yang, Ce Zhang, Ji Liu:
Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters. KDD 2022: 3288-3298 - [c105]Kenza Amara, Zhitao Ying, Zitao Zhang, Zhichao Han, Yang Zhao, Yinan Shan, Ulrik Brandes, Sebastian Schemm, Ce Zhang:
GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks. LoG 2022: 44 - [c104]Thórhildur Thorleiksdóttir, Cédric Renggli, Nora Hollenstein, Ce Zhang:
Dynamic Human Evaluation for Relative Model Comparisons. LREC 2022: 5946-5955 - [c103]Jiawei Jiang, Lukas Burkhalter, Fangcheng Fu, Bolin Ding, Bo Du, Anwar Hithnawi, Bo Li, Ce Zhang:
VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely? NeurIPS 2022 - [c102]Mintong Kang, Linyi Li, Maurice Weber, Yang Liu, Ce Zhang, Bo Li:
Certifying Some Distributional Fairness with Subpopulation Decomposition. NeurIPS 2022 - [c101]Jue Wang, Binhang Yuan, Luka Rimanic, Yongjun He, Tri Dao, Beidi Chen, Christopher Ré, Ce Zhang:
Fine-tuning Language Models over Slow Networks using Activation Quantization with Guarantees. NeurIPS 2022 - [c100]Zhuolin Yang, Zhikuan Zhao, Boxin Wang, Jiawei Zhang, Linyi Li, Hengzhi Pei, Bojan Karlas, Ji Liu, Heng Guo, Ce Zhang, Bo Li:
Improving Certified Robustness via Statistical Learning with Logical Reasoning. NeurIPS 2022 - [c99]Binhang Yuan, Yongjun He, Jared Davis, Tianyi Zhang, Tri Dao, Beidi Chen, Percy Liang, Christopher Ré, Ce Zhang:
Decentralized Training of Foundation Models in Heterogeneous Environments. NeurIPS 2022 - [c98]Baoqing Cai, Yu Liu, Ce Zhang, Guangyu Zhang, Ke Zhou, Li Liu, Chunhua Li, Bin Cheng, Jie Yang, Jiashu Xing:
HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements. SIGMOD Conference 2022: 646-659 - [c97]Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang:
In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle. SIGMOD Conference 2022: 1286-1300 - [c96]Fan Wu, Yunhui Long, Ce Zhang, Bo Li:
LINKTELLER: Recovering Private Edges from Graph Neural Networks via Influence Analysis. SP 2022: 2005-2024 - [c95]Susie Xi Rao, Piriyakorn Piriyatamwong, Parijat Ghoshal, Sara Nasirian, Sandra Mitrovic, Emmanuel de Salis, Michael Wechner, Vanya Brucker, Peter H. Egger, Ce Zhang:
Keyword Extraction in Scientific Documents. SwissText 2022: 44-55 - [c94]Yilmazcan Özyurt, Tobias Hatt, Ce Zhang, Stefan Feuerriegel:
A Deep Markov Model for Clickstream Analytics in Online Shopping. WWW 2022: 3071-3081 - [r1]Shuai Zhang, Yi Tay, Lina Yao, Aixin Sun, Ce Zhang:
Deep Learning for Recommender Systems. Recommender Systems Handbook 2022: 173-210 - [d1]Sebastian Schelter, Stefan Grafberger, Shubha Guha, Olivier Sprangers, Bojan Karlas, Ce Zhang:
schelterlabs/arguseyes. Zenodo, 2022
- [i120]Susie Xi Rao, Johannes Rausch, Peter H. Egger, Ce Zhang:
TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets. CoRR abs/2201.01654 (2022) - [i119]Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Jixiang Li, Ji Liu, Ce Zhang, Bin Cui:
Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale. CoRR abs/2201.06834 (2022) - [i118]Gyri Reiersen, David Dao, Björn Lütjens, Konstantin Klemmer, Kenza Amara, Attila Steinegger, Ce Zhang, Xiaoxiang Zhu:
ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery. CoRR abs/2201.11192 (2022) - [i117]Maurice Weber, Linyi Li, Boxin Wang, Zhikuan Zhao, Bo Li, Ce Zhang:
Certifying Out-of-Domain Generalization for Blackbox Functions. CoRR abs/2202.01679 (2022) - [i116]Leonel Aguilar, Michal Gath-Morad, Jascha Grübel, Jasper Ermatinger, Hantao Zhao, Stefan Wehrli, Robert W. Sumner, Ce Zhang, Dirk Helbing, Christoph Hölscher:
Experiments as Code: A Concept for Reproducible, Auditable, Debuggable, Reusable, & Scalable Experiments. CoRR abs/2202.12050 (2022) - [i115]Cédric Renggli, Xiaozhe Yao, Luka Kolar, Luka Rimanic, Ana Klimovic, Ce Zhang:
SHiFT: An Efficient, Flexible Search Engine for Transfer Learning. CoRR abs/2204.01457 (2022) - [i114]Susie Xi Rao, Clémence Lanfranchi, Shuai Zhang, Zhichao Han, Zitao Zhang, Wei Min, Mo Cheng, Yinan Shan, Yang Zhao, Ce Zhang:
Modelling graph dynamics in fraud detection with "Attention". CoRR abs/2204.10614 (2022) - [i113]Bojan Karlas, David Dao, Matteo Interlandi, Bo Li, Sebastian Schelter, Wentao Wu, Ce Zhang:
Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines. CoRR abs/2204.11131 (2022) - [i112]Mingxuan Lu, Zhichao Han, Susie Xi Rao, Zitao Zhang, Yang Zhao, Yinan Shan, Ramesh Raghunathan, Ce Zhang, Jiawei Jiang:
BRIGHT - Graph Neural Networks in Real-Time Fraud Detection. CoRR abs/2205.13084 (2022) - [i111]Mintong Kang, Linyi Li, Maurice Weber, Yang Liu, Ce Zhang, Bo Li:
Certifying Some Distributional Fairness with Subpopulation Decomposition. CoRR abs/2205.15494 (2022) - [i110]Binhang Yuan, Yongjun He, Jared Quincy Davis, Tianyi Zhang, Tri Dao, Beidi Chen, Percy Liang, Christopher Ré, Ce Zhang:
Decentralized Training of Foundation Models in Heterogeneous Environments. CoRR abs/2206.01288 (2022) - [i109]Jue Wang, Binhang Yuan, Luka Rimanic, Yongjun He, Tri Dao, Beidi Chen, Christopher Ré, Ce Zhang:
Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees. CoRR abs/2206.01299 (2022) - [i108]Yang Li, Yu Shen, Huaijun Jiang, Tianyi Bai, Wentao Zhang, Ce Zhang, Bin Cui:
Transfer Learning based Search Space Design for Hyperparameter Tuning. CoRR abs/2206.02511 (2022) - [i107]Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Zhi Yang, Ce Zhang, Bin Cui:
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning. CoRR abs/2206.02663 (2022) - [i106]Zhen Wang, Weirui Kuang, Ce Zhang, Bolin Ding, Yaliang Li:
FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization. CoRR abs/2206.03966 (2022) - [i105]Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang:
Stochastic Gradient Descent without Full Data Shuffle. CoRR abs/2206.05830 (2022) - [i104]Yilmazcan Özyurt, Stefan Feuerriegel, Ce Zhang:
Contrastive Learning for Unsupervised Domain Adaptation of Time Series. CoRR abs/2206.06243 (2022) - [i103]Yang Li, Yu Shen, Wentao Zhang, Ce Zhang, Bin Cui:
Efficient End-to-End AutoML via Scalable Search Space Decomposition. CoRR abs/2206.09423 (2022) - [i102]Kenza Amara, Rex Ying, Zitao Zhang, Zhihao Han, Yinan Shan, Ulrik Brandes, Sebastian Schemm, Ce Zhang:
GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks. CoRR abs/2206.09677 (2022) - [i101]Susie Xi Rao, Piriyakorn Piriyatamwong, Parijat Ghoshal, Sara Nasirian, Emmanuel de Salis, Sandra Mitrovic, Michael Wechner, Vanya Brucker, Peter H. Egger, Ce Zhang:
Keyword Extraction in Scientific Documents. CoRR abs/2207.01888 (2022) - [i100]Mark Mazumder, Colby R. Banbury, Xiaozhe Yao, Bojan Karlas, William Gaviria Rojas, Sudnya Frederick Diamos, Greg Diamos, Lynn He, Douwe Kiela, David Jurado, David Kanter, Rafael Mosquera, Juan Ciro, Lora Aroyo, Bilge Acun, Sabri Eyuboglu, Amirata Ghorbani, Emmett D. Goodman, Tariq Kane, Christine R. Kirkpatrick, Tzu-Sheng Kuo, Jonas Mueller, Tristan Thrush, Joaquin Vanschoren, Margaret Warren, Adina Williams, Serena Yeung, Newsha Ardalani, Praveen K. Paritosh, Ce Zhang, James Zou, Carole-Jean Wu, Cody Coleman, Andrew Y. Ng, Peter Mattson, Vijay Janapa Reddi:
DataPerf: Benchmarks for Data-Centric AI Development. CoRR abs/2207.10062 (2022) - [i99]Chulin Xie, Pin-Yu Chen, Ce Zhang, Bo Li:
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM. CoRR abs/2207.10226 (2022) - [i98]Simon Kassing, Vojislav Dukic, Ce Zhang, Ankit Singla:
New primitives for bounded degradation in network service. CoRR abs/2208.08429 (2022) - [i97]Jiawei Zhang, Linyi Li, Ce Zhang, Bo Li:
CARE: Certifiably Robust Learning with Reasoning via Variational Inference. CoRR abs/2209.05055 (2022) - [i96]Alfonso Amayuelas, Shuai Zhang, Susie Xi Rao, Ce Zhang:
Neural Methods for Logical Reasoning Over Knowledge Graphs. CoRR abs/2209.14464 (2022) - [i95]Yangheng Zhao, Jun Wang, Xiaolong Li, Yue Hu, Ce Zhang, Yanfeng Wang, Siheng Chen:
Number-Adaptive Prototype Learning for 3D Point Cloud Semantic Segmentation. CoRR abs/2210.09948 (2022) - [i94]Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda:
Holistic Evaluation of Language Models. CoRR abs/2211.09110 (2022) - [i93]Yujing Wang, Yaming Yang, Zhuo Li, Jiangang Bai, Mingliang Zhang, Xiangtai Li, Jing Yu, Ce Zhang, Gao Huang, Yunhai Tong:
Convolution-enhanced Evolving Attention Networks. CoRR abs/2212.08330 (2022) - 2021
- [j35]Cédric Renggli, Luka Rimanic, Nezihe Merve Gürel, Bojan Karlas, Wentao Wu, Ce Zhang:
A Data Quality-Driven View of MLOps. IEEE Data Eng. Bull. 44(1): 11-23 (2021) - [j34]Yang Li, Yu Shen, Wentao Zhang, Jiawei Jiang, Yaliang Li, Bolin Ding, Jingren Zhou, Zhi Yang, Wentao Wu, Ce Zhang, Bin Cui:
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition. Proc. VLDB Endow. 14(11): 2167-2176 (2021) - [j33]Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim, Artidoro Pagnoni, Ce Zhang, Byung-Gon Chun, Markus Weimer, Matteo Interlandi:
WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Modele. Proc. VLDB Endow. 15(1): 11-20 (2021) - [j32]Susie Xi Rao, Shuai Zhang, Zhichao Han, Zitao Zhang, Wei Min, Zhiyao Chen, Yinan Shan, Yang Zhao, Ce Zhang:
xFraud: Explainable Fraud Transaction Detection. Proc. VLDB Endow. 15(3): 427-436 (2021) - [j31]Shaoduo Gan, Xiangru Lian, Rui Wang, Jianbin Chang, Chengjun Liu, Hongmei Shi, Shengzhuo Zhang, Xianghong Li, Tengxu Sun, Jiawei Jiang, Binhang Yuan, Sen Yang, Ji Liu, Ce Zhang:
BAGUA: Scaling up Distributed Learning with System Relaxations. Proc. VLDB Endow. 15(4): 804-813 (2021) - [j30]Zitao Li, Bolin Ding, Ce Zhang, Ninghui Li, Jingren Zhou:
Federated Matrix Factorization with Privacy Guarantee. Proc. VLDB Endow. 15(4): 900-913 (2021) - [j29]Heqing Huang, Cong Zheng, Junyuan Zeng, Wu Zhou, Sencun Zhu, Peng Liu, Ian M. Molloy, Suresh Chari, Ce Zhang, Quanlong Guan:
A Large-Scale Study of Android Malware Development Phenomenon on Public Malware Submission and Scanning Platform. IEEE Trans. Big Data 7(2): 255-270 (2021) - [j28]Yunyan Guo, Zhipeng Zhang, Jiawei Jiang, Wentao Wu, Ce Zhang, Bin Cui, Jianzhong Li:
Model averaging in distributed machine learning: a case study with Apache Spark. VLDB J. 30(4): 693-712 (2021) - [c93]Johannes Rausch, Octavio Martinez, Fabian Bissig, Ce Zhang, Stefan Feuerriegel:
DocParser: Hierarchical Document Structure Parsing from Renderings. AAAI 2021: 4328-4338 - [c92]Yang Li, Yu Shen, Jiawei Jiang, Jinyang Gao, Ce Zhang, Bin Cui:
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements. AAAI 2021: 8491-8500 - [c91]Mohammad Reza Karimi, Nezihe Merve Gürel, Bojan Karlas, Johannes Rausch, Ce Zhang, Andreas Krause:
Online Active Model Selection for Pre-trained Classifiers. AISTATS 2021: 307-315 - [c90]Linyi Li, Maurice Weber, Xiaojun Xu, Luka Rimanic, Bhavya Kailkhura, Tao Xie, Ce Zhang, Bo Li:
TSS: Transformation-Specific Smoothing for Robustness Certification. CCS 2021: 535-557 - [c89]Boxin Wang, Fan Wu, Yunhui Long, Luka Rimanic, Ce Zhang, Bo Li:
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation. CCS 2021: 2146-2168 - [c88]Leonel Aguilar Melgar, David Dao, Shaoduo Gan, Nezihe Merve Gürel, Nora Hollenstein, Jiawei Jiang, Bojan Karlas, Thomas Lemmin, Tian Li, Yang Li, Susie Xi Rao, Johannes Rausch, Cédric Renggli, Luka Rimanic, Maurice Weber, Shuai Zhang, Zhikuan Zhao, Kevin Schawinski, Wentao Wu, Ce Zhang:
Ease.ML: A Lifecycle Management System for Machine Learning. CIDR 2021 - [c87]Yaliang Li, Zhen Wang, Yuexiang Xie, Bolin Ding, Kai Zeng, Ce Zhang:
AutoML: From Methodology to Application. CIKM 2021: 4853-4856 - [c86]Ruoxi Jia, Fan Wu, Xuehui Sun, Jiacen Xu, David Dao, Bhavya Kailkhura, Ce Zhang, Bo Li, Dawn Song:
Scalability vs. Utility: Do We Have To Sacrifice One for the Other in Data Importance Quantification? CVPR 2021: 8239-8247 - [c85]Peng Li, Xi Rao, Jennifer Blase, Yue Zhang, Xu Chu, Ce Zhang:
CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks. ICDE 2021: 13-24 - [c84]Nezihe Merve Gürel, Xiangyu Qi, Luka Rimanic, Ce Zhang, Bo Li:
Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks. ICML 2021: 3976-3987 - [c83]Hanlin Tang, Shaoduo Gan, Ammar Ahmad Awan, Samyam Rajbhandari, Conglong Li, Xiangru Lian, Ji Liu, Ce Zhang, Yuxiong He:
1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed. ICML 2021: 10118-10129 - [c82]Yujing Wang, Yaming Yang, Jiangang Bai, Mingliang Zhang, Jing Bai, Jing Yu, Ce Zhang, Gao Huang, Yunhai Tong:
Evolving Attention with Residual Convolutions. ICML 2021: 10971-10980 - [c81]Xupeng Miao, Nezihe Merve Gürel, Wentao Zhang, Zhichao Han, Bo Li, Wei Min, Susie Xi Rao, Hansheng Ren, Yinan Shan, Yingxia Shao, Yujie Wang, Fan Wu, Hui Xue, Yaming Yang, Zitao Zhang, Yang Zhao, Shuai Zhang, Yujing Wang, Bin Cui, Ce Zhang:
DeGNN: Improving Graph Neural Networks with Graph Decomposition. KDD 2021: 1223-1233 - [c80]Wenqi Jiang, Zhenhao He, Shuai Zhang, Kai Zeng, Liang Feng, Jiansong Zhang, Tongxuan Liu, Yong Li, Jingren Zhou, Ce Zhang, Gustavo Alonso:
FleetRec: Large-Scale Recommendation Inference on Hybrid GPU-FPGA Clusters. KDD 2021: 3097-3105 - [c79]Yang Li, Yu Shen, Wentao Zhang, Yuanwei Chen, Huaijun Jiang, Mingchao Liu, Jiawei Jiang, Jinyang Gao, Wentao Wu, Zhi Yang, Ce Zhang, Bin Cui:
OpenBox: A Generalized Black-box Optimization Service. KDD 2021: 3209-3219 - [c78]Yuexiang Xie, Zhen Wang, Yaliang Li, Bolin Ding, Nezihe Merve Gürel, Ce Zhang, Minlie Huang, Wei Lin, Jingren Zhou:
FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data. KDD 2021: 3795-3805 - [c77]Yaliang Li, Zhen Wang, Bolin Ding, Ce Zhang:
AutoML: A Perspective where Industry Meets Academy. KDD 2021: 4048-4049 - [c76]Wenqi Jiang, Zhenhao He, Shuai Zhang, Thomas B. Preußer, Kai Zeng, Liang Feng, Jiansong Zhang, Tongxuan Liu, Yong Li, Jingren Zhou, Ce Zhang, Gustavo Alonso:
MicroRec: Efficient Recommendation Inference by Hardware and Data Structure Solutions. MLSys 2021 - [c75]Shuai Zhang, Xi Rao, Yi Tay, Ce Zhang:
Knowledge Router: Learning Disentangled Representations for Knowledge Graphs. NAACL-HLT 2021: 1-10 - [c74]Nora Hollenstein, Federico Pirovano, Ce Zhang, Lena A. Jäger, Lisa Beinborn:
Multilingual Language Models Predict Human Reading Behavior. NAACL-HLT 2021: 106-123 - [c73]Cédric Renggli, Luka Rimanic, Nora Hollenstein, Ce Zhang:
Evaluating Bayes Error Estimators on Real-World Datasets with FeeBee. NeurIPS Datasets and Benchmarks 2021 - [c72]Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen, Pan Zhou, Benjamin I. P. Rubinstein, Ce Zhang, Bo Li:
TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness. NeurIPS 2021: 17642-17655 - [c71]Wentao Wu, Ce Zhang:
Towards understanding end-to-end learning in the context of data: machine learning dancing over semirings & Codd's table. DEEM@SIGMOD 2021: 1:1-1:4 - [c70]Jiawei Jiang, Shaoduo Gan, Yue Liu, Fanlin Wang, Gustavo Alonso, Ana Klimovic, Ankit Singla, Wentao Wu, Ce Zhang:
Towards Demystifying Serverless Machine Learning Training. SIGMOD Conference 2021: 857-871 - [c69]Shuai Zhang, Huoyu Liu, Aston Zhang, Yue Hu, Ce Zhang, Yumeng Li, Tanchao Zhu, Shaojian He, Wenwu Ou:
Learning User Representations with Hypercuboids for Recommender Systems. WSDM 2021: 716-724 - [i92]Hanlin Tang, Shaoduo Gan, Ammar Ahmad Awan, Samyam Rajbhandari, Conglong Li, Xiangru Lian, Ji Liu, Ce Zhang, Yuxiong He:
1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed. CoRR abs/2102.02888 (2021) - [i91]Cédric Renggli, Luka Rimanic, Nezihe Merve Gürel, Bojan Karlas, Wentao Wu, Ce Zhang:
A Data Quality-Driven View of MLOps. CoRR abs/2102.07750 (2021) - [i90]Nora Hollenstein, Cédric Renggli, Benjamin Glaus, Maria Barrett, Marius Troendle, Nicolas Langer, Ce Zhang:
Decoding EEG Brain Activity for Multi-Modal Natural Language Processing. CoRR abs/2102.08655 (2021) - [i89]Shuai Zhang, Yi Tay, Wenqi Jiang, Da-Cheng Juan, Ce Zhang:
Switch Spaces: Learning Product Spaces with Sparse Gating. CoRR abs/2102.08688 (2021) - [i88]Yujing Wang, Yaming Yang, Jiangang Bai, Mingliang Zhang, Jing Bai, Jing Yu, Ce Zhang, Gao Huang, Yunhai Tong:
Evolving Attention with Residual Convolutions. CoRR abs/2102.12895 (2021) - [i87]Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang:
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting. CoRR abs/2103.07719 (2021) - [i86]Boxin Wang, Fan Wu, Yunhui Long, Luka Rimanic, Ce Zhang, Bo Li:
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation. CoRR abs/2103.11109 (2021) - [i85]Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen, Benjamin I. P. Rubinstein, Ce Zhang, Bo Li:
TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness. CoRR abs/2104.00671 (2021) - [i84]Ji Liu, Ce Zhang:
Distributed Learning Systems with First-order Methods. CoRR abs/2104.05245 (2021) - [i83]Nora Hollenstein, Federico Pirovano, Ce Zhang, Lena A. Jäger, Lisa Beinborn:
Multilingual Language Models Predict Human Reading Behavior. CoRR abs/2104.05433 (2021) - [i82]Jiawei Jiang, Shaoduo Gan, Yue Liu, Fanlin Wang, Gustavo Alonso, Ana Klimovic, Ankit Singla, Wentao Wu, Ce Zhang:
Towards Demystifying Serverless Machine Learning Training. CoRR abs/2105.07806 (2021) - [i81]Yang Li, Yu Shen, Wentao Zhang, Yuanwei Chen, Huaijun Jiang, Mingchao Liu, Jiawei Jiang, Jinyang Gao, Wentao Wu, Zhi Yang, Ce Zhang, Bin Cui:
OpenBox: A Generalized Black-box Optimization Service. CoRR abs/2106.00421 (2021) - [i80]Nezihe Merve Gürel, Xiangyu Qi, Luka Rimanic, Ce Zhang, Bo Li:
Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks. CoRR abs/2106.06235 (2021) - [i79]Shaoduo Gan, Xiangru Lian, Rui Wang, Jianbin Chang, Chengjun Liu, Hongmei Shi, Shengzhuo Zhang, Xianghong Li, Tengxu Sun, Jiawei Jiang, Binhang Yuan, Sen Yang, Ji Liu, Ce Zhang:
BAGUA: Scaling up Distributed Learning with System Relaxations. CoRR abs/2107.01499 (2021) - [i78]Yang Li, Yu Shen, Wentao Zhang, Jiawei Jiang, Bolin Ding, Yaliang Li, Jingren Zhou, Zhi Yang, Wentao Wu, Ce Zhang, Bin Cui:
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition. CoRR abs/2107.08861 (2021) - [i77]Gyri Reiersen, David Dao, Björn Lütjens, Konstantin Klemmer, Xiaoxiang Zhu, Ce Zhang:
Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery. CoRR abs/2107.11320 (2021) - [i76]Fan Wu, Yunhui Long, Ce Zhang, Bo Li:
LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis. CoRR abs/2108.06504 (2021) - [i75]Cédric Renggli, Luka Rimanic, Nora Hollenstein, Ce Zhang:
Evaluating Bayes Error Estimators on Read-World Datasets with FeeBee. CoRR abs/2108.13034 (2021) - [i74]Xiangru Lian, Binhang Yuan, Xuefeng Zhu, Yulong Wang, Yongjun He, Honghuan Wu, Lei Sun, Haodong Lyu, Chengjun Liu, Xing Dong, Yiqiao Liao, Mingnan Luo, Congfei Zhang, Jingru Xie, Haonan Li, Lei Chen, Renjie Huang, Jianying Lin, Chengchun Shu, Xuezhong Qiu, Zhishan Liu, Dongying Kong, Lei Yuan, Hai Yu, Sen Yang, Ce Zhang, Ji Liu:
Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters. CoRR abs/2111.05897 (2021) - [i73]Thórhildur Thorleiksdóttir, Cédric Renggli, Nora Hollenstein, Ce Zhang:
Dynamic Human Evaluation for Relative Model Comparisons. CoRR abs/2112.08048 (2021) - [i72]Ghislain Fourny, David Dao, Can Berker Cikis, Ce Zhang, Gustavo Alonso:
RumbleML: program the lakehouse with JSONiq. CoRR abs/2112.12638 (2021) - 2020
- [j27]Ji Liu, Ce Zhang:
Distributed Learning Systems with First-Order Methods. Found. Trends Databases 9(1): 1-100 (2020) - [j26]Hussein Hassan-Harrirou, Ce Zhang, Thomas Lemmin:
RosENet: Improving Binding Affinity Prediction by Leveraging Molecular Mechanics Energies with an Ensemble of 3D Convolutional Neural Networks. J. Chem. Inf. Model. 60(6): 2791-2802 (2020) - [j25]Christian Pfeiffer, Nora Hollenstein, Ce Zhang, Nicolas Langer:
Neural dynamics of sentiment processing during naturalistic sentence reading. NeuroImage 218: 116934 (2020) - [j24]Cédric Renggli, Luka Rimanic, Luka Kolar, Wentao Wu, Ce Zhang:
Ease.ml/snoopy in Action: Towards Automatic Feasibility Analysis for Machine Learning Application Development. Proc. VLDB Endow. 13(12): 2837-2840 (2020) - [j23]Bojan Karlas, Peng Li, Renzhi Wu, Nezihe Merve Gürel, Xu Chu, Wentao Wu, Ce Zhang:
Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions. Proc. VLDB Endow. 14(3): 255-267 (2020) - [j22]Nezihe Merve Gürel, Kaan Kara, Alen Stojanov, Tyler M. Smith, Thomas Lemmin, Dan Alistarh, Markus Püschel, Ce Zhang:
Compressive Sensing Using Iterative Hard Thresholding With Low Precision Data Representation: Theory and Applications. IEEE Trans. Signal Process. 68: 4268-4282 (2020) - [c68]Yang Li, Jiawei Jiang, Jinyang Gao, Yingxia Shao, Ce Zhang, Bin Cui:
Efficient Automatic CASH via Rising Bandits. AAAI 2020: 4763-4771 - [c67]Yujing Wang, Yaming Yang, Yiren Chen, Jing Bai, Ce Zhang, Guinan Su, Xiaoyu Kou, Yunhai Tong, Mao Yang, Lidong Zhou:
TextNAS: A Neural Architecture Search Space Tailored for Text Representation. AAAI 2020: 9242-9249 - [c66]Nora Hollenstein, Adrian Van der Lek, Ce Zhang:
CogniVal in Action: An Interface for Customizable Cognitive Word Embedding Evaluation. COLING (Demonstrations) 2020: 34-40 - [c65]Maurice Weber, Cédric Renggli, Helmut Grabner, Ce Zhang:
Observer Dependent Lossy Image Compression. GCPR 2020: 130-144 - [c64]Giuseppe Russo, Nora Hollenstein, Claudiu Cristian Musat, Ce Zhang:
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation. EMNLP (Findings) 2020: 351-366 - [c63]Zhipeng Zhang, Wentao Wu, Jiawei Jiang, Lele Yu, Bin Cui, Ce Zhang:
C olumnSGD: A Column-oriented Framework for Distributed Stochastic Gradient Descent. ICDE 2020: 1513-1524 - [c62]Fangcheng Fu, Yuzheng Hu, Yihan He, Jiawei Jiang, Yingxia Shao, Ce Zhang, Bin Cui:
Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript. ICML 2020: 3304-3314 - [c61]Bojan Karlas, Matteo Interlandi, Cédric Renggli, Wentao Wu, Ce Zhang, Deepak Mukunthu Iyappan Babu, Jordan Edwards, Chris Lauren, Andy Xu, Markus Weimer:
Building Continuous Integration Services for Machine Learning. KDD 2020: 2407-2415 - [c60]Nora Hollenstein, Marius Troendle, Ce Zhang, Nicolas Langer:
ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation. LREC 2020: 138-146 - [c59]Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang:
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting. NeurIPS 2020 - [c58]Luka Rimanic, Cédric Renggli, Bo Li, Ce Zhang:
On Convergence of Nearest Neighbor Classifiers over Feature Transformations. NeurIPS 2020 - [c57]Zhiqiang Tao, Yaliang Li, Bolin Ding, Ce Zhang, Jingren Zhou, Yun Fu:
Learning to Mutate with Hypergradient Guided Population. NeurIPS 2020 - [p1]Tianhao Wang, Johannes Rausch, Ce Zhang, Ruoxi Jia, Dawn Song:
A Principled Approach to Data Valuation for Federated Learning. Federated Learning 2020: 153-167 - [i71]Linyi Li, Maurice Weber, Xiaojun Xu, Luka Rimanic, Tao Xie, Ce Zhang, Bo Li:
Provable Robust Learning Based on Transformation-Specific Smoothing. CoRR abs/2002.12398 (2020) - [i70]Zhuolin Yang, Zhikuan Zhao, Hengzhi Pei, Boxin Wang, Bojan Karlas, Ji Liu, Heng Guo, Bo Li, Ce Zhang:
End-to-end Robustness for Sensing-Reasoning Machine Learning Pipelines. CoRR abs/2003.00120 (2020) - [i69]Maurice Weber, Xiaojun Xu, Bojan Karlas, Ce Zhang, Bo Li:
RAB: Provable Robustness Against Backdoor Attacks. CoRR abs/2003.08904 (2020) - [i68]Xiaoyu Kou, Yaming Yang, Yujing Wang, Ce Zhang, Yiren Chen, Yunhai Tong, Yan Zhang, Jing Bai:
Improving BERT with Self-Supervised Attention. CoRR abs/2004.03808 (2020) - [i67]Simona Santamaria, David Dao, Björn Lütjens, Ce Zhang:
TrueBranch: Metric Learning-based Verification of Forest Conservation Projects. CoRR abs/2004.09725 (2020) - [i66]Giuseppe Russo, Nora Hollenstein, Claudiu Musat, Ce Zhang:
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation. CoRR abs/2004.14983 (2020) - [i65]Bojan Karlas, Peng Li, Renzhi Wu, Nezihe Merve Gürel, Xu Chu, Wentao Wu, Ce Zhang:
Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions. CoRR abs/2005.05117 (2020) - [i64]Yuexiang Xie, Zhen Wang, Yaliang Li, Bolin Ding, Nezihe Merve Gürel, Ce Zhang, Minlie Huang, Wei Lin, Jingren Zhou:
Interactive Feature Generation via Learning Adjacency Tensor of Feature Graph. CoRR abs/2007.14573 (2020) - [i63]Hanlin Tang, Shaoduo Gan, Samyam Rajbhandari, Xiangru Lian, Ji Liu, Yuxiong He, Ce Zhang:
APMSqueeze: A Communication Efficient Adam-Preconditioned Momentum SGD Algorithm. CoRR abs/2008.11343 (2020) - [i62]Tianhao Wang, Johannes Rausch, Ce Zhang, Ruoxi Jia, Dawn Song:
A Principled Approach to Data Valuation for Federated Learning. CoRR abs/2009.06192 (2020) - [i61]Maurice Weber, Nana Liu, Bo Li, Ce Zhang, Zhikuan Zhao:
Optimal Provable Robustness of Quantum Classification via Quantum Hypothesis Testing. CoRR abs/2009.10064 (2020) - [i60]Wenqi Jiang, Zhenhao He, Shuai Zhang, Thomas B. Preußer, Kai Zeng, Liang Feng, Jiansong Zhang, Tongxuan Liu, Yong Li, Jingren Zhou, Ce Zhang, Gustavo Alonso:
MicroRec: Accelerating Deep Recommendation Systems to Microseconds by Hardware and Data Structure Solutions. CoRR abs/2010.05894 (2020) - [i59]Cédric Renggli, André Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, Mario Lucic:
Which Model to Transfer? Finding the Needle in the Growing Haystack. CoRR abs/2010.06402 (2020) - [i58]Luka Rimanic, Cédric Renggli, Bo Li, Ce Zhang:
On Convergence of Nearest Neighbor Classifiers over Feature Transformations. CoRR abs/2010.07765 (2020) - [i57]Cédric Renggli, Luka Rimanic, Luka Kolar, Nora Hollenstein, Wentao Wu, Ce Zhang:
On Automatic Feasibility Study for Machine Learning Application Development with ease.ml/snoopy. CoRR abs/2010.08410 (2020) - [i56]Mohammad Reza Karimi, Nezihe Merve Gürel, Bojan Karlas, Johannes Rausch, Ce Zhang, Andreas Krause:
Online Active Model Selection for Pre-trained Classifiers. CoRR abs/2010.09818 (2020) - [i55]Shuai Zhang, Huoyu Liu, Aston Zhang, Yue Hu, Ce Zhang, Yumeng Li, Tanchao Zhu, Shaojian He, Wenwu Ou:
Learning User Representations with Hypercuboids for Recommender Systems. CoRR abs/2011.05742 (2020) - [i54]Susie Xi Rao, Shuai Zhang, Zhichao Han, Zitao Zhang, Wei Min, Zhiyao Chen, Yinan Shan, Yang Zhao, Ce Zhang:
xFraud: Explainable Fraud Transaction Detection on Heterogeneous Graphs. CoRR abs/2011.12193 (2020) - [i53]Yang Li, Yu Shen, Jiawei Jiang, Jinyang Gao, Ce Zhang, Bin Cui:
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements. CoRR abs/2012.03011 (2020) - [i52]Yang Li, Jiawei Jiang, Jinyang Gao, Yingxia Shao, Ce Zhang, Bin Cui:
Efficient Automatic CASH via Rising Bandits. CoRR abs/2012.04371 (2020) - [i51]Susie Xi Rao, Shuai Zhang, Zhichao Han, Zitao Zhang, Wei Min, Mo Cheng, Yinan Shan, Yang Zhao, Ce Zhang:
Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks. CoRR abs/2012.10831 (2020)
2010 – 2019
- 2019
- [j21]Zeke Wang, Kaan Kara, Hantian Zhang, Gustavo Alonso, Ce Zhang, Onur Mutlu:
Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-precision Learning. Proc. VLDB Endow. 12(7): 807-821 (2019) - [j20]Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nezihe Merve Gürel, Bo Li, Ce Zhang, Costas J. Spanos, Dawn Song:
Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms. Proc. VLDB Endow. 12(11): 1610-1623 (2019) - [j19]Kaan Kara, Zeke Wang, Ce Zhang, Gustavo Alonso:
doppioDB 2.0: Hardware Techniques for Improved Integration of Machine Learning into Databases. Proc. VLDB Endow. 12(12): 1818-1821 (2019) - [j18]Cédric Renggli, Frances Ann Hubis, Bojan Karlas, Kevin Schawinski, Wentao Wu, Ce Zhang:
Ease.ml/ci and Ease.ml/meter in Action: Towards Data Management for Statistical Generalization. Proc. VLDB Endow. 12(12): 1962-1965 (2019) - [j17]Theodoros Rekatsinas, Sudeepa Roy, Manasi Vartak, Ce Zhang, Neoklis Polyzotis:
Opportunities for Data Management Research in the Era of Horizontal AI/ML. Proc. VLDB Endow. 12(12): 2323-2324 (2019) - [c56]Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song, Costas J. Spanos:
Towards Efficient Data Valuation Based on the Shapley Value. AISTATS 2019: 1167-1176 - [c55]Chen Yu, Bojan Karlas, Jie Zhong, Ce Zhang, Ji Liu:
AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI. AISTATS 2019: 2829-2838 - [c54]Nora Hollenstein, Antonio de la Torre, Nicolas Langer, Ce Zhang:
CogniVal: A Framework for Cognitive Word Embedding Evaluation. CoNLL 2019: 538-549 - [c53]Zhipeng Zhang, Jiawei Jiang, Wentao Wu, Ce Zhang, Lele Yu, Bin Cui:
MLlib*: Fast Training of GLMs Using Spark MLlib. ICDE 2019: 1778-1789 - [c52]Chen Yu, Hanlin Tang, Cédric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ce Zhang, Ji Liu:
Distributed Learning over Unreliable Networks. ICML 2019: 7202-7212 - [c51]Cédric Renggli, Bojan Karlas, Bolin Ding, Feng Liu, Kevin Schawinski, Wentao Wu, Ce Zhang:
Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment. SysML 2019 - [c50]Nora Hollenstein, Ce Zhang:
Entity Recognition at First Sight: Improving NER with Eye Movement Information. NAACL-HLT (1) 2019: 1-10 - [c49]Vojislav Dukic, Sangeetha Abdu Jyothi, Bojan Karlas, Muhsen Owaida, Ce Zhang, Ankit Singla:
Is advance knowledge of flow sizes a plausible assumption? NSDI 2019: 565-580 - [c48]Johannes Beck, Roberta Huang, David Lindner, Tian Guo, Ce Zhang, Dirk Helbing, Nino Antulov-Fantulin:
Sensing Social Media Signals for Cryptocurrency News. WWW (Companion Volume) 2019: 1051-1054 - [i50]Nora Hollenstein, Ce Zhang:
Entity Recognition at First Sight: Improving NER with Eye Movement Information. CoRR abs/1902.10068 (2019) - [i49]Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song, Costas J. Spanos:
Towards Efficient Data Valuation Based on the Shapley Value. CoRR abs/1902.10275 (2019) - [i48]Cédric Renggli, Bojan Karlas, Bolin Ding, Feng Liu, Kevin Schawinski, Wentao Wu, Ce Zhang:
Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment. CoRR abs/1903.00278 (2019) - [i47]Zeke Wang, Kaan Kara, Hantian Zhang, Gustavo Alonso, Onur Mutlu, Ce Zhang:
Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-precision Learning (Technical Report). CoRR abs/1903.03404 (2019) - [i46]Johannes Beck, Roberta Huang, David Lindner, Tian Guo, Ce Zhang, Dirk Helbing, Nino Antulov-Fantulin:
Sensing Social Media Signals for Cryptocurrency News. CoRR abs/1903.11451 (2019) - [i45]Nora Hollenstein, Maria Barrett, Marius Troendle, Francesco Bigiolli, Nicolas Langer, Ce Zhang:
Advancing NLP with Cognitive Language Processing Signals. CoRR abs/1904.02682 (2019) - [i44]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i43]Peng Li, Xi Rao, Jennifer Blase, Yue Zhang, Xu Chu, Ce Zhang:
CleanML: A Benchmark for Joint Data Cleaning and Machine Learning [Experiments and Analysis]. CoRR abs/1904.09483 (2019) - [i42]Frances Ann Hubis, Wentao Wu, Ce Zhang:
Quantitative Overfitting Management for Human-in-the-loop ML Application Development with ease.ml/meter. CoRR abs/1906.00299 (2019) - [i41]Hanlin Tang, Xiangru Lian, Shuang Qiu, Lei Yuan, Ce Zhang, Tong Zhang, Ji Liu:
DeepSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression. CoRR abs/1907.07346 (2019) - [i40]Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nezihe Merve Gürel, Bo Li, Ce Zhang, Costas J. Spanos, Dawn Song:
Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms. CoRR abs/1908.08619 (2019) - [i39]Nora Hollenstein, Antonio de la Torre, Nicolas Langer, Ce Zhang:
CogniVal: A Framework for Cognitive Word Embedding Evaluation. CoRR abs/1909.09001 (2019) - [i38]Maurice Weber, Cédric Renggli, Helmut Grabner, Ce Zhang:
Lossy Image Compression with Recurrent Neural Networks: from Human Perceived Visual Quality to Classification Accuracy. CoRR abs/1910.03472 (2019) - [i37]Nezihe Merve Gürel, Hansheng Ren, Yujing Wang, Hui Xue, Yaming Yang, Ce Zhang:
An Anatomy of Graph Neural Networks Going Deep via the Lens of Mutual Information: Exponential Decay vs. Full Preservation. CoRR abs/1910.04499 (2019) - [i36]Johannes Rausch, Octavio Martinez, Fabian Bissig, Ce Zhang, Stefan Feuerriegel:
DocParser: Hierarchical Structure Parsing of Document Renderings. CoRR abs/1911.01702 (2019) - [i35]Ruoxi Jia, Xuehui Sun, Jiacen Xu, Ce Zhang, Bo Li, Dawn Song:
An Empirical and Comparative Analysis of Data Valuation with Scalable Algorithms. CoRR abs/1911.07128 (2019) - [i34]Nora Hollenstein, Marius Troendle, Ce Zhang, Nicolas Langer:
ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation. CoRR abs/1912.00903 (2019) - [i33]Fotis Psallidas, Yiwen Zhu, Bojan Karlas, Matteo Interlandi, Avrilia Floratou, Konstantinos Karanasos, Wentao Wu, Ce Zhang, Subru Krishnan, Carlo Curino, Markus Weimer:
Data Science through the looking glass and what we found there. CoRR abs/1912.09536 (2019) - [i32]Yujing Wang, Yaming Yang, Yiren Chen, Jing Bai, Ce Zhang, Guinan Su, Xiaoyu Kou, Yunhai Tong, Mao Yang, Lidong Zhou:
TextNAS: A Neural Architecture Search Space tailored for Text Representation. CoRR abs/1912.10729 (2019) - 2018
- [j16]Tian Li, Jie Zhong, Ji Liu, Wentao Wu, Ce Zhang:
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads. Proc. VLDB Endow. 11(5): 607-620 (2018) - [j15]Yu Liu, Hantian Zhang, Luyuan Zeng, Wentao Wu, Ce Zhang:
MLBench: Benchmarking Machine Learning Services Against Human Experts. Proc. VLDB Endow. 11(10): 1220-1232 (2018) - [j14]Bojan Karlas, Ji Liu, Wentao Wu, Ce Zhang:
Ease.ml in Action: Towards Multi-tenant Declarative Learning Services. Proc. VLDB Endow. 11(12): 2054-2057 (2018) - [j13]Kaan Kara, Ken Eguro, Ce Zhang, Gustavo Alonso:
ColumnML: Column-Store Machine Learning with On-The-Fly Data Transformation. Proc. VLDB Endow. 12(4): 348-361 (2018) - [c47]Ivan Girardi, Pengfei Ji, An-phi Nguyen, Nora Hollenstein, Adam Ivankay, Lorenz Kuhn, Chiara Marchiori, Ce Zhang:
Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks. Louhi@EMNLP 2018: 139-148 - [c46]Heng Guo, Kaan Kara, Ce Zhang:
Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond. AISTATS 2018: 178-187 - [c45]Vojislav Dukic, Sangeetha Abdu Jyothi, Bojan Karlas, Muhsen Owaida, Ce Zhang, Ankit Singla:
Network Scheduling in the Dark. SoCC 2018: 516 - [c44]Nino Antulov-Fantulin, Dijana Tolic, Matija Piskorec, Ce Zhang, Irena Vodenska:
Inferring Short-Term Volatility Indicators from the Bitcoin Blockchain. COMPLEX NETWORKS (2) 2018: 508-520 - [c43]Demjan Grubic, Leo Tam, Dan Alistarh, Ce Zhang:
Synchronous Multi-GPU Training for Deep Learning with Low-Precision Communications: An Empirical Study. EDBT 2018: 145-156 - [c42]Xiangru Lian, Wei Zhang, Ce Zhang, Ji Liu:
Asynchronous Decentralized Parallel Stochastic Gradient Descent. ICML 2018: 3049-3058 - [c41]Hanlin Tang, Xiangru Lian, Ming Yan, Ce Zhang, Ji Liu:
D2: Decentralized Training over Decentralized Data. ICML 2018: 4855-4863 - [c40]Hanlin Tang, Shaoduo Gan, Ce Zhang, Tong Zhang, Ji Liu:
Communication Compression for Decentralized Training. NeurIPS 2018: 7663-7673 - [c39]Jonathan Rotsztejn, Nora Hollenstein, Ce Zhang:
ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction. SemEval@NAACL-HLT 2018: 689-696 - [c38]Jiawei Jiang, Bin Cui, Ce Zhang, Fangcheng Fu:
DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions. SIGMOD Conference 2018: 1363-1376 - [i31]David Dao, Dan Alistarh, Claudiu Musat, Ce Zhang:
DataBright: Towards a Global Exchange for Decentralized Data Ownership and Trusted Computation. CoRR abs/1802.04780 (2018) - [i30]Nezihe Merve Gürel, Kaan Kara, Dan Alistarh, Ce Zhang:
Compressive Sensing with Low Precision Data Representation: Radio Astronomy and Beyond. CoRR abs/1802.04907 (2018) - [i29]Hanlin Tang, Ce Zhang, Shaoduo Gan, Tong Zhang, Ji Liu:
Decentralization Meets Quantization. CoRR abs/1803.06443 (2018) - [i28]Chen Yu, Bojan Karlas, Jie Zhong, Ce Zhang, Ji Liu:
Multi-device, Multi-tenant Model Selection with GP-EI. CoRR abs/1803.06561 (2018) - [i27]Hanlin Tang, Xiangru Lian, Ming Yan, Ce Zhang, Ji Liu:
D2: Decentralized Training over Decentralized Data. CoRR abs/1803.07068 (2018) - [i26]Jonathan Rotsztejn, Nora Hollenstein, Ce Zhang:
ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction. CoRR abs/1804.02042 (2018) - [i25]Sandro Ackermann, Kevin Schawinski, Ce Zhang, Anna K. Weigel, M. Dennis Turp:
Using transfer learning to detect galaxy mergers. CoRR abs/1805.10289 (2018) - [i24]Nino Antulov-Fantulin, Dijana Tolic, Matija Piskorec, Ce Zhang, Irena Vodenska:
Inferring short-term volatility indicators from Bitcoin blockchain. CoRR abs/1809.07856 (2018) - [i23]Ivan Girardi, Pengfei Ji, An-phi Nguyen, Nora Hollenstein, Adam Ivankay, Lorenz Kuhn, Chiara Marchiori, Ce Zhang:
Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks. CoRR abs/1809.10804 (2018) - [i22]Hanlin Tang, Chen Yu, Cédric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ji Liu, Ce Zhang:
Distributed Learning over Unreliable Networks. CoRR abs/1810.07766 (2018) - [i21]Kevin Schawinski, M. Dennis Turp, Ce Zhang:
Exploring galaxy evolution with generative models. CoRR abs/1812.01114 (2018) - 2017
- [j12]Ce Zhang, Christopher Ré, Michael J. Cafarella, Jaeho Shin, Feiran Wang, Sen Wu:
DeepDive: declarative knowledge base construction. Commun. ACM 60(5): 93-102 (2017) - [j11]Zhipeng Zhang, Yingxia Shao, Bin Cui, Ce Zhang:
An Experimental Evaluation of SimRank-based Similarity Search Algorithms. Proc. VLDB Endow. 10(5): 601-612 (2017) - [j10]Lele Yu, Bin Cui, Ce Zhang, Yingxia Shao:
LDA*: A Robust and Large-scale Topic Modeling System. Proc. VLDB Endow. 10(11): 1406-1417 (2017) - [j9]Xupeng Li, Bin Cui, Yiru Chen, Wentao Wu, Ce Zhang:
MLog: Towards Declarative In-Database Machine Learning. Proc. VLDB Endow. 10(12): 1933-1936 (2017) - [j8]Christopher De Sa, Alexander Ratner, Christopher Ré, Jaeho Shin, Feiran Wang, Sen Wu, Ce Zhang:
Incremental knowledge base construction using DeepDive. VLDB J. 26(1): 81-105 (2017) - [c37]Kun-Hsing Yu, Ce Zhang, Gerald J. Berry, Russ B. Altman, Christopher Ré, Daniel L. Rubin, Michael Snyder:
Predicting Non-Small Cell Lung Cancer Diagnosis and Prognosis by Fully Automated Microscopic Pathology Image Features. AMIA 2017 - [c36]Hantian Zhang, Luyuan Zeng, Wentao Wu, Ce Zhang:
How good are machine learning clouds for binary classification with good features?: extended abstract. SoCC 2017: 649 - [c35]Kaan Kara, Dan Alistarh, Gustavo Alonso, Onur Mutlu, Ce Zhang:
FPGA-Accelerated Dense Linear Machine Learning: A Precision-Convergence Trade-Off. FCCM 2017: 160-167 - [c34]Muhsen Owaida, Hantian Zhang, Ce Zhang, Gustavo Alonso:
Scalable inference of decision tree ensembles: Flexible design for CPU-FPGA platforms. FPL 2017: 1-8 - [c33]Jie Jiang, Jiawei Jiang, Bin Cui, Ce Zhang:
TencentBoost: A Gradient Boosting Tree System with Parameter Server. ICDE 2017: 281-284 - [c32]Hantian Zhang, Jerry Li, Kaan Kara, Dan Alistarh, Ji Liu, Ce Zhang:
ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning. ICML 2017: 4035-4043 - [c31]Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, Ji Liu:
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent. NIPS 2017: 5330-5340 - [c30]Ce Zhang, Wentao Wu, Tian Li:
An Overreaction to the Broken Machine Learning Abstraction: The ease.ml Vision. HILDA@SIGMOD 2017: 3:1-3:6 - [c29]Jiawei Jiang, Bin Cui, Ce Zhang, Lele Yu:
Heterogeneity-aware Distributed Parameter Servers. SIGMOD Conference 2017: 463-478 - [i20]Kevin Schawinski, Ce Zhang, Hantian Zhang, Lucas Fowler, Gokula Krishnan Santhanam:
Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit. CoRR abs/1702.00403 (2017) - [i19]Heng Guo, Kaan Kara, Ce Zhang:
Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond. CoRR abs/1705.05154 (2017) - [i18]Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, Ji Liu:
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent. CoRR abs/1705.09056 (2017) - [i17]Hantian Zhang, Luyuan Zeng, Wentao Wu, Ce Zhang:
How Good Are Machine Learning Clouds for Binary Classification with Good Features? CoRR abs/1707.09562 (2017) - [i16]Tian Li, Jie Zhong, Ji Liu, Wentao Wu, Ce Zhang:
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads. CoRR abs/1708.07308 (2017) - [i15]Xiangru Lian, Wei Zhang, Ce Zhang, Ji Liu:
Asynchronous Decentralized Parallel Stochastic Gradient Descent. CoRR abs/1710.06952 (2017) - 2016
- [j7]Emily K. Mallory, Ce Zhang, Christopher Ré, Russ B. Altman:
Large-scale extraction of gene interactions from full-text literature using DeepDive. Bioinform. 32(1): 106-113 (2016) - [j6]Christopher De Sa, Alexander Ratner, Christopher Ré, Jaeho Shin, Feiran Wang, Sen Wu, Ce Zhang:
DeepDive: Declarative Knowledge Base Construction. SIGMOD Rec. 45(1): 60-67 (2016) - [j5]Ce Zhang, Arun Kumar, Christopher Ré:
Materialization Optimizations for Feature Selection Workloads. ACM Trans. Database Syst. 41(1): 2:1-2:32 (2016) - [c28]Ioannis Mitliagkas, Ce Zhang, Stefan Hadjis, Christopher Ré:
Asynchrony begets momentum, with an application to deep learning. Allerton 2016: 997-1004 - [c27]Heqing Huang, Cong Zheng, Junyuan Zeng, Wu Zhou, Sencun Zhu, Peng Liu, Suresh Chari, Ce Zhang:
Android malware development on public malware scanning platforms: A large-scale data-driven study. IEEE BigData 2016: 1090-1099 - [c26]Ce Zhang, Jaeho Shin, Christopher Ré, Michael J. Cafarella, Feng Niu:
Extracting Databases from Dark Data with DeepDive. SIGMOD Conference 2016: 847-859 - [i14]Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris S. Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, Christopher Ré, Benjamin Recht:
CYCLADES: Conflict-free Asynchronous Machine Learning. CoRR abs/1605.09721 (2016) - [i13]Ioannis Mitliagkas, Ce Zhang, Stefan Hadjis, Christopher Ré:
Asynchrony begets Momentum, with an Application to Deep Learning. CoRR abs/1605.09774 (2016) - [i12]Stefan Hadjis, Ce Zhang, Ioannis Mitliagkas, Christopher Ré:
Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs. CoRR abs/1606.04487 (2016) - [i11]Hantian Zhang, Kaan Kara, Jerry Li, Dan Alistarh, Ji Liu, Ce Zhang:
ZipML: An End-to-end Bitwise Framework for Dense Generalized Linear Models. CoRR abs/1611.05402 (2016) - 2015
- [j4]Jaeho Shin, Sen Wu, Feiran Wang, Christopher De Sa, Ce Zhang, Christopher Ré:
Incremental Knowledge Base Construction Using DeepDive. Proc. VLDB Endow. 8(11): 1310-1321 (2015) - [c25]Christopher De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré:
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms. NIPS 2015: 2674-2682 - [c24]Christopher De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré:
Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width. NIPS 2015: 3097-3105 - [c23]Stefan Hadjis, Firas Abuzaid, Ce Zhang, Christopher Ré:
Caffe con Troll: Shallow Ideas to Speed Up Deep Learning. DanaC@SIGMOD 2015: 2:1-2:4 - [i10]Sen Wu, Ce Zhang, Feiran Wang, Christopher Ré:
Incremental Knowledge Base Construction Using DeepDive. CoRR abs/1502.00731 (2015) - [i9]Firas Abuzaid, Stefan Hadjis, Ce Zhang, Christopher Ré:
Caffe con Troll: Shallow Ideas to Speed Up Deep Learning. CoRR abs/1504.04343 (2015) - [i8]Christopher De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré:
Taming the Wild: A Unified Analysis of Hogwild!-Style Algorithms. CoRR abs/1506.06438 (2015) - [i7]Yuke Zhu, Ce Zhang, Christopher Ré, Li Fei-Fei:
Building a Large-scale Multimodal Knowledge Base for Visual Question Answering. CoRR abs/1507.05670 (2015) - [i6]Christopher De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré:
Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width. CoRR abs/1510.00756 (2015) - 2014
- [j3]Christopher Ré, Amir Abbas Sadeghian, Zifei Shan, Jaeho Shin, Feiran Wang, Sen Wu, Ce Zhang:
Feature Engineering for Knowledge Base Construction. IEEE Data Eng. Bull. 37(3): 26-40 (2014) - [j2]Ce Zhang, Christopher Ré:
DimmWitted: A Study of Main-Memory Statistical Analytics. Proc. VLDB Endow. 7(12): 1283-1294 (2014) - [c22]Yingbo Zhou, Utkarsh Porwal, Ce Zhang, Hung Q. Ngo, Long Nguyen, Christopher Ré, Venu Govindaraju:
Parallel Feature Selection Inspired by Group Testing. NIPS 2014: 3554-3562 - [c21]Ce Zhang, Arun Kumar, Christopher Ré:
Materialization optimizations for feature selection workloads. SIGMOD Conference 2014: 265-276 - [c20]Victor Bittorf, Marcel Kornacker, Christopher Ré, Ce Zhang:
Tradeoffs in Main-Memory Statistical Analytics from Impala to DimmWitted. IMDM@VLDB 2014: 55-56 - [i5]Ce Zhang, Christopher Ré:
DimmWitted: A Study of Main-Memory Statistical Analytics. CoRR abs/1403.7550 (2014) - [i4]Shanan Peters, Ce Zhang, Miron Livny, Christopher Ré:
A machine-compiled macroevolutionary history of Phanerozoic life. CoRR abs/1406.2963 (2014) - [i3]Ce Zhang, Christopher Ré, Amir Abbas Sadeghian, Zifei Shan, Jaeho Shin, Feiran Wang, Sen Wu:
Feature Engineering for Knowledge Base Construction. CoRR abs/1407.6439 (2014) - 2013
- [c19]Vidhya Govindaraju, Ce Zhang, Christopher Ré:
Understanding Tables in Context Using Standard NLP Toolkits. ACL (2) 2013: 658-664 - [c18]Michael R. Anderson, Dolan Antenucci, Victor Bittorf, Matthew Burgess, Michael J. Cafarella, Arun Kumar, Feng Niu, Yongjoo Park, Christopher Ré, Ce Zhang:
Brainwash: A Data System for Feature Engineering. CIDR 2013 - [c17]Young Chol Song, Henry A. Kautz, James F. Allen, Mary D. Swift, Yuncheng Li, Jiebo Luo, Ce Zhang:
A Markov logic framework for recognizing complex events from multimodal data. ICMI 2013: 141-148 - [c16]Srikrishna Sridhar, Stephen J. Wright, Christopher Ré, Ji Liu, Victor Bittorf, Ce Zhang:
An Approximate, Efficient LP Solver for LP Rounding. NIPS 2013: 2895-2903 - [c15]Ce Zhang, Christopher Ré:
Towards high-throughput gibbs sampling at scale: a study across storage managers. SIGMOD Conference 2013: 397-408 - [c14]Ce Zhang, Vidhya Govindaraju, Jackson Borchardt, Tim Foltz, Christopher Ré, Shanan Peters:
GeoDeepDive: statistical inference using familiar data-processing languages. SIGMOD Conference 2013: 993-996 - [c13]John R. Frank, Steven J. Bauer, Max Kleiman-Weiner, Daniel A. Roberts, Nilesh Tripuraneni, Ce Zhang, Christopher Ré, Ellen M. Voorhees, Ian Soboroff:
Evaluating Stream Filtering for Entity Profile Updates for TREC 2013. TREC 2013 - [c12]Tushar Khot, Ce Zhang, Jude W. Shavlik, Sriraam Natarajan, Christopher Ré:
Bootstrapping Knowledge Base Acceleration. TREC 2013 - [i2]Srikrishna Sridhar, Victor Bittorf, Ji Liu, Ce Zhang, Christopher Ré, Stephen J. Wright:
An Approximate, Efficient Solver for LP Rounding. CoRR abs/1311.2661 (2013) - 2012
- [j1]Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik:
Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference. Int. J. Semantic Web Inf. Syst. 8(3): 42-73 (2012) - [c11]Ce Zhang, Feng Niu, Christopher Ré, Jude W. Shavlik:
Big Data versus the Crowd: Looking for Relationships in All the Right Places. ACL (1) 2012: 825-834 - [c10]Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik:
Scaling Inference for Markov Logic via Dual Decomposition. ICDM 2012: 1032-1037 - [c9]John R. Frank, Max Kleiman-Weiner, Daniel A. Roberts, Feng Niu, Ce Zhang, Christopher Ré, Ian Soboroff:
Building an Entity-Centric Stream Filtering Test Collection for TREC 2012. TREC 2012 - [c8]Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik:
DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference. VLDS 2012: 25-28 - 2011
- [c7]Junjie Yao, Bin Cui, Qiaosha Han, Ce Zhang, Yanhong Zhou:
Modeling User Expertise in Folksonomies by Fusing Multi-type Features. DASFAA (1) 2011: 53-67 - [i1]Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik:
Felix: Scaling Inference for Markov Logic with an Operator-based Approach. CoRR abs/1108.0294 (2011) - 2010
- [c6]Bin Cui, Ce Zhang, Gao Cong:
Content-enriched classifier for web video classification. SIGIR 2010: 619-626 - [c5]Bin Cui, Anthony K. H. Tung, Ce Zhang, Zhe Zhao:
Multiple feature fusion for social media applications. SIGMOD Conference 2010: 435-446
2000 – 2009
- 2009
- [c4]Xin Cao, Gao Cong, Bin Cui, Christian S. Jensen, Ce Zhang:
The use of categorization information in language models for question retrieval. CIKM 2009: 265-274 - [c3]Ce Zhang, Bin Cui, Gao Cong, Yu-Jing Wang:
A Revisit of Query Expansion with Different Semantic Levels. DASFAA 2009: 662-676 - [c2]Bin Cui, Bei Pan, Heng Tao Shen, Ying Wang, Ce Zhang:
Video Annotation System Based on Categorizing and Keyword Labelling. DASFAA 2009: 764-767 - 2008
- [c1]Ce Zhang, Yu-Jing Wang, Bin Cui, Gao Cong:
Semantic similarity based on compact concept ontology. WWW 2008: 1125-1126
Coauthor Index
[c138] [i139] [j43] [c131] [c130] [c128] [c127] [c126] [i133] [i130] [i124] [i123] [c114] [c101] [c99] [i110] [i109] [i94] [i44] [j12] [j8] [c37] [j7] [j6] [j5] [c28] [c26] [i14] [i13] [i12] [j4] [c25] [c24] [c23] [i10] [i9] [i8] [i7] [i6] [j3] [j2] [c22] [c21] [c20] [i5] [i4] [i3] [c19] [c18] [c16] [c15] [c14] [c13] [c12] [i2] [j1] [c11] [c10] [c9] [c8] [i1]
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