Yi Zhou - RankMe

67 papers A* 8A 3B 5Journal 40Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Entropy-Aware On-Policy Distillation of Language Models. CoRR Woogyeol Jin, Taywon Min, Yongjin Yang, Swanand Ravindra Kadhe, Yi Zhou, Dennis Wei, Nathalie Baracaldo, Kimin Lee
2026	A*	`conf`	Language Model Representations for Efficient Few-Shot Tabular Classification. WWW Inwon Kang, Parikshit Ram, Yi Zhou, Horst Samulowitz, Oshani Seneviratne
2026	J	`jnl`	Language Model Representations for Efficient Few-Shot Tabular Classification. CoRR Inwon Kang, Parikshit Ram, Yi Zhou, Horst Samulowitz, Oshani Seneviratne
2025	J	`jnl`	GneissWeb: Preparing High Quality Data for LLMs at Scale. CoRR 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
2025	A*	`conf`	MAP: Multi-Human-Value Alignment Palette. ICLR Xinran Wang, Qi Le, Ammar Ahmed, Enmao Diao, Yi Zhou, Nathalie Baracaldo, Jie Ding, Ali Anwar
2025	J	`jnl`	Mitigating Modality Imbalance in Multi-modal Learning via Multi-objective Optimization. CoRR Heshan Devaka Fernando, Parikshit Ram, Yi Zhou, Soham Dan, Horst Samulowitz, Nathalie Baracaldo, Tianyi Chen
2025	J	`jnl`	On Learning Representations for Tabular Data Distillation. CoRR Inwon Kang, Parikshit Ram, Yi Zhou, Horst Samulowitz, Oshani Seneviratne
2025	J	`jnl`	On Learning Representations for Tabular Data Distillation. Trans. Mach. Learn. Res. Inwon Kang, Parikshit Ram, Yi Zhou, Horst Samulowitz, Oshani Seneviratne
2024	—	`conf`	Byzantine-Resilient Bilevel Federated Learning. SAM Momin Abbas, Yi Zhou, Nathalie Baracaldo, Horst Samulowitz, Parikshit Ram, Theodoros Salonidis
2024	A*	`conf`	Effective Data Distillation for Tabular Datasets (Student Abstract). AAAI Inwon Kang, Parikshit Ram, Yi Zhou, Horst Samulowitz, Oshani Seneviratne
2024	A	`conf`	Enhancing In-context Learning via Linear Probe Calibration. AISTATS Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, Tianyi Chen
2024	J	`jnl`	Enhancing In-context Learning via Linear Probe Calibration. CoRR Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, Tianyi Chen
2024	J	`jnl`	Granite Code Models: A Family of Open Foundation Models for Code Intelligence. CoRR 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, Syed 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
2024	J	`jnl`	MAP: Multi-Human-Value Alignment Palette. CoRR Xinran Wang, Qi Le, Ammar Ahmed, Enmao Diao, Yi Zhou, Nathalie Baracaldo, Jie Ding, Ali Anwar
2024	J	`jnl`	Mitigating Forgetting in LLM Supervised Fine-Tuning and Preference Learning. CoRR Heshan Devaka Fernando, Han Shen, Parikshit Ram, Yi Zhou, Horst Samulowitz, Nathalie Baracaldo, Tianyi Chen
2024	A	`conf`	Towards Collecting Royalties for Copyrighted Data for Generative Models. ICWS Heiko Ludwig, Yi Zhou, Syed Zawad, Yuya Jeremy Ong, Pengyuan Li, Eric Butler, Eelaaf Zahid
2024	J	`jnl`	Turning Generative Models Degenerate: The Power of Data Poisoning Attacks. CoRR Shuli Jiang, Swanand Ravindra Kadhe, Yi Zhou, Farhan Ahmed, Ling Cai, Nathalie Baracaldo
2023	—	`conf`	Benchmarking the Effect of Poisoning Defenses on the Security and Bias of Deep Learning Models. SP (Workshops) Nathalie Baracaldo, Farhan Ahmed, Kevin Eykholt, Yi Zhou, Shriti Priya, Taesung Lee, Swanand Kadhe, Mike Tan, Sridevi Polavaram, Sterling Suggs, Yuyang Gao, David Slater
2023	J	`jnl`	Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks. CoRR Shuli Jiang, Swanand Ravindra Kadhe, Yi Zhou, Ling Cai, Nathalie Baracaldo
2023	J	`jnl`	Graph Topology Invariant Gradient and Sampling Complexity for Decentralized and Stochastic Optimization. SIAM J. Optim. Guanghui Lan, Yuyuan Ouyang, Yi Zhou
2023	B	`conf`	HDFL: A Heterogeneity and Client Dropout-Aware Federated Learning Framework. CCGrid Syed Zawad, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Feng Yan
2023	A*	`conf`	LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning. ICML Timothy Castiglia, Yi Zhou, Shiqiang Wang, Swanand Kadhe, Nathalie Baracaldo, Stacy Patterson
2023	J	`jnl`	LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning. CoRR Timothy Castiglia, Yi Zhou, Shiqiang Wang, Swanand Kadhe, Nathalie Baracaldo, Stacy Patterson
2023	J	`jnl`	Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection. CoRR 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
2023	A*	`conf`	Single-shot General Hyper-parameter Optimization for Federated Learning. ICLR Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig
2022	B	`conf`	DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting. CLOUD Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, Swanand Kadhe, Heiko Ludwig
2022	J	`jnl`	DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting. CoRR Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, Swanand Kadhe, Heiko Ludwig
2022	—	`ch.`	Dealing with Byzantine Threats to Neural Networks. Federated Learning Yi Zhou, Nathalie Baracaldo, Ali Anwar, Kamala Varma
2022	—	`ch.`	Federated Learning and Fairness. Federated Learning Annie Abay, Yi Zhou, Nathalie Baracaldo, Heiko Ludwig
2022	—	`ch.`	Federated Learning for Collaborative Financial Crimes Detection. Federated Learning Toyotaro Suzumura, Yi Zhou, Ryo Kawahara, Nathalie Baracaldo, Heiko Ludwig
2022	J	`jnl`	Federated XGBoost on Sample-Wise Non-IID Data. CoRR Katelinh Jones, Yuya Jeremy Ong, Yi Zhou, Nathalie Baracaldo
2022	B	`conf`	Heterogeneity-Aware Adaptive Federated Learning Scheduling. IEEE Big Data Jingoo Han, Ahmad Faraz Khan, Syed Zawad, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Feng Yan, Ali Raza Butt
2022	—	`ch.`	Privacy-Preserving Vertical Federated Learning. Federated Learning Runhua Xu, Nathalie Baracaldo, Yi Zhou, Annie Abay, Ali Anwar
2022	J	`jnl`	Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis. CoRR Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig
2022	B	`conf`	TIFF: Tokenized Incentive for Federated Learning. CLOUD Jingoo Han, Ahmad Faraz Khan, Syed Zawad, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Feng Yan, Ali Raza Butt
2022	—	`ch.`	Tree-Based Models for Federated Learning Systems. Federated Learning Yuya Jeremy Ong, Nathalie Baracaldo, Yi Zhou
2021	J	`jnl`	Asynchronous Decentralized Accelerated Stochastic Gradient Descent. IEEE J. Sel. Areas Inf. Theory Guanghui Lan, Yi Zhou
2021	A*	`conf`	Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning. AAAI Syed Zawad, Ahsan Ali, Pin-Yu Chen, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Yuan Tian, Feng Yan
2021	J	`jnl`	Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning. CoRR Syed Zawad, Ahsan Ali, Pin-Yu Chen, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Yuan Tian, Feng Yan
2021	J	`jnl`	FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning. CoRR Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig
2021	—	`conf`	FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data. AISec@CCS Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, James Joshi, Heiko Ludwig
2021	J	`jnl`	FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data. CoRR Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, James Joshi, Heiko Ludwig
2021	B	`conf`	LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning. CLOUD Kamala Varma, Yi Zhou, Nathalie Baracaldo, Ali Anwar
2021	J	`jnl`	LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning. CoRR Kamala Varma, Yi Zhou, Nathalie Baracaldo, Ali Anwar
2020	J	`jnl`	Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning. CoRR Yuya Jeremy Ong, Yi Zhou, Nathalie Baracaldo, Heiko Ludwig
2020	J	`jnl`	Communication-efficient algorithms for decentralized and stochastic optimization. Math. Program. Guanghui Lan, Soomin Lee, Yi Zhou
2020	J	`jnl`	IBM Federated Learning: an Enterprise Framework White Paper V0.1. CoRR 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
2020	J	`jnl`	Mitigating Bias in Federated Learning. CoRR Annie Abay, Yi Zhou, Nathalie Baracaldo, Shashank Rajamoni, Ebube Chuba, Heiko Ludwig
2020	A	`conf`	TiFL: A Tier-based Federated Learning System. HPDC Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, Yue Cheng
2020	J	`jnl`	TiFL: A Tier-based Federated Learning System. CoRR Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, Yue Cheng
2019	J	`jnl`	A Hybrid Approach to Privacy-Preserving Federated Learning - (Extended Abstract). Inform. Spektrum Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, Yi Zhou
2019	—	`conf`	A Hybrid Approach to Privacy-Preserving Federated Learning. AISec@CCS Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, Yi Zhou
2019	A*	`conf`	A unified variance-reduced accelerated gradient method for convex optimization. NeurIPS Guanghui Lan, Zhize Li, Yi Zhou
2019	J	`jnl`	A unified variance-reduced accelerated gradient method for convex optimization. CoRR Guanghui Lan, Zhize Li, Yi Zhou
2019	—	`conf`	HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning. AISec@CCS Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, Heiko Ludwig
2019	J	`jnl`	HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning. CoRR Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, Heiko Ludwig
2019	J	`jnl`	Towards Federated Graph Learning for Collaborative Financial Crimes Detection. CoRR Toyotaro Suzumura, Yi Zhou, Nathalie Barcardo, Guangnan Ye, Keith Houck, Ryo Kawahara, Ali Anwar, Lucia Larise Stavarache, Daniel Klyashtorny, Heiko Ludwig, Kumar Bhaskaran
2019	—	`conf`	Towards Taming the Resource and Data Heterogeneity in Federated Learning. OpML Zheng Chai, Hannan Fayyaz, Zeshan Fayyaz, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Heiko Ludwig, Yue Cheng
2018	J	`jnl`	An optimal randomized incremental gradient method. Math. Program. Guanghui Lan, Yi Zhou
2018	J	`jnl`	Asynchronous decentralized accelerated stochastic gradient descent. CoRR Guanghui Lan, Yi Zhou
2018	J	`jnl`	Random Gradient Extrapolation for Distributed and Stochastic Optimization. SIAM J. Optim. Guanghui Lan, Yi Zhou
2017	J	`jnl`	Communication-Efficient Algorithms for Decentralized and Stochastic Optimization. CoRR Guanghui Lan, Soomin Lee, Yi Zhou
2017	A*	`conf`	Conditional Accelerated Lazy Stochastic Gradient Descent. ICML Guanghui Lan, Sebastian Pokutta, Yi Zhou, Daniel Zink
2017	J	`jnl`	Conditional Accelerated Lazy Stochastic Gradient Descent. CoRR Guanghui Lan, Sebastian Pokutta, Yi Zhou, Daniel Zink
2017	J	`jnl`	Random gradient extrapolation for distributed and stochastic optimization. CoRR Guanghui Lan, Yi Zhou
2016	J	`jnl`	Conditional Gradient Sliding for Convex Optimization. SIAM J. Optim. Guanghui Lan, Yi Zhou
2015	J	`jnl`	An optimal randomized incremental gradient method. CoRR Guanghui Lan, Yi Zhou