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ORCIDYi Zhou 0015
Person information
- affiliation: IBM Research - Almaden, San Jose, CA, USA
Other persons with the same name
- Yi Zhou — disambiguation page
- Yi Zhou 0001
![[0000-0001-5420-8016]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0001-5420-8016")
— Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Beijing, China - Yi Zhou 0002 — Singapore Polytechnic,School of Electrical and Electronic Engineering, Singapore (and 1 more)
- Yi Zhou 0003 — Shanghai Jiao Tong University, Computer Science Department, China
- Yi Zhou 0004 — Henan University, School of Computer and Information Engineering, Kaifeng, China (and 1 more)
- Yi Zhou 0005 — Sun Yat-sen University, Zhongshan School of Medicine, Guangzhou, China
- Yi Zhou 0006 — Monash University, Department of Management, Caulfield East, VIC, Australia (and 1 more)
- Yi Zhou 0007 — Southeast University, Nanjing, Jiangsu, China (and 2 more)
- Yi Zhou 0008 — Fudan University, Shanghai Engineering Research Center of Ultra Precision Optical Manufacturing, Shanghai, China
- Yi Zhou 0009 — Columbus State University, GA, USA (and 1 more)
- Yi Zhou 0010 — Hunan University, China (and 1 more)
- Yi Zhou 0011 — Dalian Maritime University, College of Information Science and Technology, China (and 1 more)
- Yi Zhou 0012 — Southwest Jiaotong University, Provincial Key Laboratory of Information Coding and Transmission, Chengdu, China (and 1 more)
- Yi Zhou 0013 — University of Western Sydney, School of Computing, Engineering and Mathematics, Australia
- Yi Zhou 0014 — Chongqing University of Posts and Telecommunications, School of Communication and Information Engineering, China (and 2 more)
- Yi Zhou 0016 — University of Electronic Science and Technology of China, School of Computer Science and Engineering, Chengdu, China
- Yi Zhou 0017 — University of Utah, Department of Electrical and Computer Engineering, Salt Lake City, UT, USA
- Yi Zhou 0018 — Bytedance AI Lab, China (and 1 more)
- Yi Zhou 0019 — University of Liverpool, UK
- Yi Zhou 0020 — National University of Singapore, Department of Electrical, and Computer Engineering, Singapore
- Yi Zhou 0022 — Wuhan University of Science and Technology, School of Information Science and Engineering, Engineering Research Center of Metallurgical Automation and Measurement Technology, China
- Yi Zhou 0023 — Adobe, USA (and 2 more)
- Yi Zhou 0024 — Soochow University, School of Electronics and Information Engineering, China
- Yi Zhou 0025 — Carnegie Mellon University, Pittsburgh, PA, USA
- Yi Zhou 0026 — University ofElectronic Science and Technology of China, School of Automation Engineering, China
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2020 – today
- 2025
[c21]Xinran Wang, Qi Le, Ammar Ahmed, Enmao Diao, Yi Zhou, Nathalie Baracaldo, Jie Ding, Ali Anwar:
MAP: Multi-Human-Value Alignment Palette. ICLR 2025- [i29]Inwon Kang, Parikshit Ram, Yi Zhou, Horst Samulowitz, Oshani Seneviratne:
On Learning Representations for Tabular Data Distillation. CoRR abs/2501.13905 (2025) - [i28]Hajar Emami-Gohari, Swanand Ravindra Kadhe, Syed Yousaf Shah, Constantin Adam, Abdulhamid Adebayo, Praneet Adusumilli, Farhan Ahmed, Nathalie Baracaldo Angel, Santosh Borse, Yuan Chi Chang, Xuan-Hong Dang, Nirmit Desai, Revital Eres, Ran Iwamoto, Alexei Karve, Yan Koyfman, Wei-Han Lee, Changchang Liu, Boris Lublinsky, Takuya Ohko, Pablo Pesce, Maroun Touma, Shiqiang Wang, Shalisha Witherspoon, Herbert Woisetschlaeger, David Wood, Kun-Lung Wu, Issei Yoshida, Syed Zawad, Petros Zerfos, Yi Zhou, Bishwaranjan Bhattacharjee:
GneissWeb: Preparing High Quality Data for LLMs at Scale. CoRR abs/2502.14907 (2025) - 2024
- [c20]Inwon Kang, Parikshit Ram, Yi Zhou, Horst Samulowitz, Oshani Seneviratne:
Effective Data Distillation for Tabular Datasets (Student Abstract). AAAI 2024: 23533-23534 - [c19]Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, Tianyi Chen:
Enhancing In-context Learning via Linear Probe Calibration. AISTATS 2024: 307-315 - [c18]Heiko Ludwig, Yi Zhou, Syed Zawad, Yuya Jeremy Ong, Pengyuan Li, Eric Butler, Eelaaf Zahid:
Towards Collecting Royalties for Copyrighted Data for Generative Models. ICWS 2024: 20-26 - [c17]Momin Abbas, Yi Zhou, Nathalie Baracaldo, Horst Samulowitz, Parikshit Ram, Theodoros Salonidis:
Byzantine-Resilient Bilevel Federated Learning. SAM 2024: 1-5 - [i27]Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, Tianyi Chen:
Enhancing In-context Learning via Linear Probe Calibration. CoRR abs/2401.12406 (2024) - [i26]Mayank Mishra, Matt Stallone, Gaoyuan Zhang, Yikang Shen, Aditya Prasad, Adriana Meza Soria, Michele Merler, Parameswaran Selvam, Saptha Surendran, Shivdeep Singh, Manish Sethi, Xuan-Hong Dang, Pengyuan Li, Kun-Lung Wu, Syed Zawad, Andrew Coleman, Matthew White, Mark Lewis, Raju Pavuluri, Yan Koyfman, Boris Lublinsky, Maximilien de Bayser, Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal, Yi Zhou, Chris Johnson, Aanchal Goyal, Hima Patel, S. Yousaf Shah, Petros Zerfos, Heiko Ludwig, Asim Munawar, Maxwell Crouse, Pavan Kapanipathi, Shweta Salaria, Bob Calio, Sophia Wen, Seetharami Seelam, Brian Belgodere, Carlos A. Fonseca, Amith Singhee, Nirmit Desai, David D. Cox, Ruchir Puri, Rameswar Panda:
Granite Code Models: A Family of Open Foundation Models for Code Intelligence. CoRR abs/2405.04324 (2024) - [i25]Shuli Jiang, Swanand Ravindra Kadhe, Yi Zhou, Farhan Ahmed, Ling Cai, Nathalie Baracaldo:
Turning Generative Models Degenerate: The Power of Data Poisoning Attacks. CoRR abs/2407.12281 (2024) - [i24]Heshan Devaka Fernando, Han Shen, Parikshit Ram, Yi Zhou, Horst Samulowitz, Nathalie Baracaldo, Tianyi Chen:
Mitigating Forgetting in LLM Supervised Fine-Tuning and Preference Learning. CoRR abs/2410.15483 (2024) - [i23]Xinran Wang, Qi Le, Ammar Ahmed, Enmao Diao, Yi Zhou, Nathalie Baracaldo, Jie Ding, Ali Anwar:
MAP: Multi-Human-Value Alignment Palette. CoRR abs/2410.19198 (2024) - 2023
- [j7]Guanghui Lan, Yuyuan Ouyang, Yi Zhou:
Graph Topology Invariant Gradient and Sampling Complexity for Decentralized and Stochastic Optimization. SIAM J. Optim. 33(3): 1647-1675 (2023) - [c16]Syed Zawad, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Feng Yan:
HDFL: A Heterogeneity and Client Dropout-Aware Federated Learning Framework. CCGrid 2023: 311-321 - [c15]Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig:
Single-shot General Hyper-parameter Optimization for Federated Learning. ICLR 2023 - [c14]Timothy Castiglia, Yi Zhou, Shiqiang Wang, Swanand Kadhe, Nathalie Baracaldo, Stacy Patterson:
LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning. ICML 2023: 3757-3781 - [c13]Nathalie Baracaldo, Farhan Ahmed, Kevin Eykholt, Yi Zhou, Shriti Priya, Taesung Lee, Swanand Kadhe, Mike Tan, Sridevi Polavaram, Sterling Suggs, Yuyang Gao, David Slater:
Benchmarking the Effect of Poisoning Defenses on the Security and Bias of Deep Learning Models. SP (Workshops) 2023: 45-56 - [i22]Timothy Castiglia, Yi Zhou, Shiqiang Wang, Swanand Kadhe, Nathalie Baracaldo, Stacy Patterson:
LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning. CoRR abs/2305.02219 (2023) - [i21]Swanand Ravindra Kadhe, Heiko Ludwig, Nathalie Baracaldo, Alan King, Yi Zhou, Keith Houck, Ambrish Rawat, Mark Purcell, Naoise Holohan, Mikio Takeuchi, Ryo Kawahara, Nir Drucker, Hayim Shaul, Eyal Kushnir, Omri Soceanu:
Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection. CoRR abs/2310.19304 (2023) - [i20]Shuli Jiang, Swanand Ravindra Kadhe, Yi Zhou, Ling Cai, Nathalie Baracaldo:
Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks. CoRR abs/2312.04748 (2023) - 2022
- [c12]Jingoo Han, Ahmad Faraz Khan, Syed Zawad, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Feng Yan, Ali Raza Butt:
TIFF: Tokenized Incentive for Federated Learning. CLOUD 2022: 407-416 - [c11]Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, Swanand Kadhe, Heiko Ludwig:
DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting. CLOUD 2022: 417-426 - [c10]Jingoo Han, Ahmad Faraz Khan, Syed Zawad, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Feng Yan, Ali Raza Butt:
Heterogeneity-Aware Adaptive Federated Learning Scheduling. IEEE Big Data 2022: 911-920 - [p5]Yuya Jeremy Ong, Nathalie Baracaldo, Yi Zhou:
Tree-Based Models for Federated Learning Systems. Federated Learning 2022: 27-52 - [p4]Annie Abay, Yi Zhou, Nathalie Baracaldo, Heiko Ludwig:
Federated Learning and Fairness. Federated Learning 2022: 177-191 - [p3]Yi Zhou, Nathalie Baracaldo, Ali Anwar, Kamala Varma:
Dealing with Byzantine Threats to Neural Networks. Federated Learning 2022: 391-414 - [p2]Runhua Xu, Nathalie Baracaldo, Yi Zhou, Annie Abay, Ali Anwar:
Privacy-Preserving Vertical Federated Learning. Federated Learning 2022: 417-438 - [p1]Toyotaro Suzumura, Yi Zhou, Ryo Kawahara, Nathalie Baracaldo, Heiko Ludwig:
Federated Learning for Collaborative Financial Crimes Detection. Federated Learning 2022: 455-466 - [i19]Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig:
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis. CoRR abs/2202.08338 (2022) - [i18]Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, Swanand Kadhe, Heiko Ludwig:
DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting. CoRR abs/2207.07779 (2022) - [i17]Katelinh Jones, Yuya Jeremy Ong, Yi Zhou, Nathalie Baracaldo:
Federated XGBoost on Sample-Wise Non-IID Data. CoRR abs/2209.01340 (2022) - 2021
- [j6]Guanghui Lan, Yi Zhou:
Asynchronous Decentralized Accelerated Stochastic Gradient Descent. IEEE J. Sel. Areas Inf. Theory 2(2): 802-811 (2021) - [c9]Kamala Varma, Yi Zhou, Nathalie Baracaldo, Ali Anwar:
LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning. CLOUD 2021: 272-277 - [c8]Syed Zawad, Ahsan Ali, Pin-Yu Chen, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Yuan Tian, Feng Yan:
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning. AAAI 2021: 10807-10814 - [c7]Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, James Joshi, Heiko Ludwig:
FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data. AISec@CCS 2021: 181-192 - [i16]Syed Zawad, Ahsan Ali, Pin-Yu Chen, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Yuan Tian, Feng Yan:
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning. CoRR abs/2102.00655 (2021) - [i15]Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, James Joshi, Heiko Ludwig:
FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data. CoRR abs/2103.03918 (2021) - [i14]Kamala Varma, Yi Zhou, Nathalie Baracaldo, Ali Anwar:
LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning. CoRR abs/2107.12490 (2021) - [i13]Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig:
FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning. CoRR abs/2112.08524 (2021) - 2020
- [j5]Guanghui Lan, Soomin Lee, Yi Zhou:
Communication-efficient algorithms for decentralized and stochastic optimization. Math. Program. 180(1): 237-284 (2020) - [c6]Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, Yue Cheng:
TiFL: A Tier-based Federated Learning System. HPDC 2020: 125-136 - [i12]Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, Yue Cheng:
TiFL: A Tier-based Federated Learning System. CoRR abs/2001.09249 (2020) - [i11]Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas, Yi Zhou, Ali Anwar, Shashank Rajamoni, Yuya Jeremy Ong, Jayaram Radhakrishnan, Ashish Verma, Mathieu Sinn, Mark Purcell, Ambrish Rawat, Tran Ngoc Minh, Naoise Holohan, Supriyo Chakraborty, Shalisha Witherspoon, Dean Steuer, Laura Wynter, Hifaz Hassan, Sean Laguna, Mikhail Yurochkin, Mayank Agarwal, Ebube Chuba, Annie Abay:
IBM Federated Learning: an Enterprise Framework White Paper V0.1. CoRR abs/2007.10987 (2020) - [i10]Annie Abay, Yi Zhou, Nathalie Baracaldo, Shashank Rajamoni, Ebube Chuba, Heiko Ludwig:
Mitigating Bias in Federated Learning. CoRR abs/2012.02447 (2020) - [i9]Yuya Jeremy Ong, Yi Zhou, Nathalie Baracaldo, Heiko Ludwig:
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning. CoRR abs/2012.06670 (2020)
2010 – 2019
- 2019
- [j4]Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, Yi Zhou:
A Hybrid Approach to Privacy-Preserving Federated Learning - (Extended Abstract). Inform. Spektrum 42(5): 356-357 (2019) - [c5]Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, Yi Zhou:
A Hybrid Approach to Privacy-Preserving Federated Learning. AISec@CCS 2019: 1-11 - [c4]Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, Heiko Ludwig:
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning. AISec@CCS 2019: 13-23 - [c3]Guanghui Lan, Zhize Li, Yi Zhou:
A unified variance-reduced accelerated gradient method for convex optimization. NeurIPS 2019: 10462-10472 - [c2]Zheng Chai, Hannan Fayyaz, Zeshan Fayyaz, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Heiko Ludwig, Yue Cheng:
Towards Taming the Resource and Data Heterogeneity in Federated Learning. OpML 2019: 19-21 - [i8]Guanghui Lan, Zhize Li, Yi Zhou:
A unified variance-reduced accelerated gradient method for convex optimization. CoRR abs/1905.12412 (2019) - [i7]Toyotaro Suzumura, Yi Zhou, Nathalie Barcardo, Guangnan Ye, Keith Houck, Ryo Kawahara, Ali Anwar, Lucia Larise Stavarache, Daniel Klyashtorny, Heiko Ludwig, Kumar Bhaskaran:
Towards Federated Graph Learning for Collaborative Financial Crimes Detection. CoRR abs/1909.12946 (2019) - [i6]Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, Heiko Ludwig:
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning. CoRR abs/1912.05897 (2019) - 2018
- [j3]Guanghui Lan, Yi Zhou:
An optimal randomized incremental gradient method. Math. Program. 171(1-2): 167-215 (2018) - [j2]Guanghui Lan, Yi Zhou:
Random Gradient Extrapolation for Distributed and Stochastic Optimization. SIAM J. Optim. 28(4): 2753-2782 (2018) - [i5]Guanghui Lan, Yi Zhou:
Asynchronous decentralized accelerated stochastic gradient descent. CoRR abs/1809.09258 (2018) - 2017
- [c1]Guanghui Lan, Sebastian Pokutta, Yi Zhou, Daniel Zink:
Conditional Accelerated Lazy Stochastic Gradient Descent. ICML 2017: 1965-1974 - [i4]Guanghui Lan, Soomin Lee, Yi Zhou:
Communication-Efficient Algorithms for Decentralized and Stochastic Optimization. CoRR abs/1701.03961 (2017) - [i3]Guanghui Lan, Sebastian Pokutta, Yi Zhou, Daniel Zink:
Conditional Accelerated Lazy Stochastic Gradient Descent. CoRR abs/1703.05840 (2017) - [i2]Guanghui Lan, Yi Zhou:
Random gradient extrapolation for distributed and stochastic optimization. CoRR abs/1711.05762 (2017) - 2016
- [j1]Guanghui Lan, Yi Zhou:
Conditional Gradient Sliding for Convex Optimization. SIAM J. Optim. 26(2): 1379-1409 (2016) - 2015
- [i1]Guanghui Lan, Yi Zhou:
An optimal randomized incremental gradient method. CoRR abs/1507.02000 (2015)
Coauthor Index
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