Conditional Gradient Sliding for Convex Optimization. - Guanghui Lan; Yi Zhou 015 (SIAM J. Optim., 2016)

Conditional Accelerated Lazy Stochastic Gradient Descent. - Guanghui Lan; Sebastian Pokutta; Yi Zhou 015; Daniel Zink (ICML, 2017)

An optimal randomized incremental gradient method. - Guanghui Lan; Yi Zhou 015 (Math. Program., 2018)

Random Gradient Extrapolation for Distributed and Stochastic Optimization. - Guanghui Lan; Yi Zhou 015 (SIAM J. Optim., 2018)

A Hybrid Approach to Privacy-Preserving Federated Learning. - Stacey Truex; Nathalie Baracaldo; Ali Anwar 001; Thomas Steinke 002; Heiko Ludwig; Rui Zhang 028; Yi Zhou 015 (AISec@CCS, 2019)

HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning. - Runhua Xu; Nathalie Baracaldo; Yi Zhou 015; Ali Anwar 001; Heiko Ludwig (AISec@CCS, 2019)

A unified variance-reduced accelerated gradient method for convex optimization. - Guanghui Lan; Zhize Li; Yi Zhou 015 (NeurIPS, 2019)

Towards Taming the Resource and Data Heterogeneity in Federated Learning. - Zheng Chai; Hannan Fayyaz; Zeshan Fayyaz; Ali Anwar 001; Yi Zhou 015; Nathalie Baracaldo; Heiko Ludwig; Yue Cheng 001 (OpML, 2019)

Towards Federated Graph Learning for Collaborative Financial Crimes Detection. - Toyotaro Suzumura; Yi Zhou 015; Nathalie Barcardo; Guangnan Ye; Keith Houck; Ryo Kawahara; Ali Anwar 001; Lucia Larise Stavarache; Daniel Klyashtorny; Heiko Ludwig; Kumar Bhaskaran (CoRR, 2019)

A Hybrid Approach to Privacy-Preserving Federated Learning - (Extended Abstract). - Stacey Truex; Nathalie Baracaldo; Ali Anwar 001; Thomas Steinke 002; Heiko Ludwig; Rui Zhang 028; Yi Zhou 015 (Inform. Spektrum, 2019)

TiFL: A Tier-based Federated Learning System. - Zheng Chai; Ahsan Ali; Syed Zawad; Stacey Truex; Ali Anwar 001; Nathalie Baracaldo; Yi Zhou 015; Heiko Ludwig; Feng Yan 001; Yue Cheng 001 (HPDC, 2020)

IBM Federated Learning: an Enterprise Framework White Paper V0.1. - Heiko Ludwig; Nathalie Baracaldo; Gegi Thomas; Yi Zhou 015; Ali Anwar 001; Shashank Rajamoni; Yuya Jeremy Ong; Jayaram Radhakrishnan; Ashish Verma 001; Mathieu Sinn; Mark Purcell; Ambrish Rawat; et al. (CoRR, 2020)

Mitigating Bias in Federated Learning. - Annie Abay; Yi Zhou 015; Nathalie Baracaldo; Shashank Rajamoni; Ebube Chuba; Heiko Ludwig (CoRR, 2020)

Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning. - Yuya Jeremy Ong; Yi Zhou 015; Nathalie Baracaldo; Heiko Ludwig (CoRR, 2020)

Communication-efficient algorithms for decentralized and stochastic optimization. - Guanghui Lan; Soomin Lee; Yi Zhou 015 (Math. Program., 2020)

LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning. - Kamala Varma; Yi Zhou 015; Nathalie Baracaldo; Ali Anwar 001 (CLOUD, 2021)

Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning. - Syed Zawad; Ahsan Ali; Pin-Yu Chen; Ali Anwar 001; Yi Zhou 015; Nathalie Baracaldo; Yuan Tian 001; Feng Yan 001 (AAAI, 2021)

FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data. - Runhua Xu; Nathalie Baracaldo; Yi Zhou 015; Ali Anwar 001; James Joshi; Heiko Ludwig (AISec@CCS, 2021)

FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning. - Yi Zhou 015; Parikshit Ram; Theodoros Salonidis; Nathalie Baracaldo; Horst Samulowitz; Heiko Ludwig (CoRR, 2021)

Asynchronous Decentralized Accelerated Stochastic Gradient Descent. - Guanghui Lan; Yi Zhou 015 (IEEE J. Sel. Areas Inf. Theory, 2021)

Dealing with Byzantine Threats to Neural Networks. - Yi Zhou 015; Nathalie Baracaldo; Ali Anwar 001; Kamala Varma (Federated Learning, 2022)

Federated Learning and Fairness. - Annie Abay; Yi Zhou 015; Nathalie Baracaldo; Heiko Ludwig (Federated Learning, 2022)

Tree-Based Models for Federated Learning Systems. - Yuya Jeremy Ong; Nathalie Baracaldo; Yi Zhou 015 (Federated Learning, 2022)

Federated Learning for Collaborative Financial Crimes Detection. - Toyotaro Suzumura; Yi Zhou 015; Ryo Kawahara; Nathalie Baracaldo; Heiko Ludwig (Federated Learning, 2022)

Privacy-Preserving Vertical Federated Learning. - Runhua Xu; Nathalie Baracaldo; Yi Zhou 015; Annie Abay; Ali Anwar 001 (Federated Learning, 2022)

TIFF: Tokenized Incentive for Federated Learning. - Jingoo Han; Ahmad Faraz Khan 001; Syed Zawad; Ali Anwar 001; Nathalie Baracaldo; Yi Zhou 015; Feng Yan 001; Ali Raza Butt (CLOUD, 2022)

DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting. - Runhua Xu; Nathalie Baracaldo; Yi Zhou 015; Ali Anwar 001; Swanand Kadhe; Heiko Ludwig (CLOUD, 2022)

Heterogeneity-Aware Adaptive Federated Learning Scheduling. - Jingoo Han; Ahmad Faraz Khan 001; Syed Zawad; Ali Anwar 001; Nathalie Baracaldo; Yi Zhou 015; Feng Yan 001; Ali Raza Butt (IEEE Big Data, 2022)

Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis. - Yi Zhou 015; Parikshit Ram; Theodoros Salonidis; Nathalie Baracaldo; Horst Samulowitz; Heiko Ludwig (CoRR, 2022)

Federated XGBoost on Sample-Wise Non-IID Data. - Katelinh Jones; Yuya Jeremy Ong; Yi Zhou 015; Nathalie Baracaldo (CoRR, 2022)

HDFL: A Heterogeneity and Client Dropout-Aware Federated Learning Framework. - Syed Zawad; Ali Anwar 001; Yi Zhou 015; Nathalie Baracaldo; Feng Yan 001 (CCGrid, 2023)

Single-shot General Hyper-parameter Optimization for Federated Learning. - Yi Zhou 015; Parikshit Ram; Theodoros Salonidis; Nathalie Baracaldo; Horst Samulowitz; Heiko Ludwig (ICLR, 2023)

LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning. - Timothy Castiglia; Yi Zhou 015; Shiqiang Wang 001; Swanand Kadhe; Nathalie Baracaldo; Stacy Patterson (ICML, 2023)

Benchmarking the Effect of Poisoning Defenses on the Security and Bias of Deep Learning Models. - Nathalie Baracaldo; Farhan Ahmed; Kevin Eykholt; Yi Zhou 015; Shriti Priya; Taesung Lee; Swanand Kadhe; Mike Tan; Sridevi Polavaram; Sterling Suggs; Yuyang Gao; David Slater (SP (Workshops), 2023)

Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection. - Swanand Ravindra Kadhe; Heiko Ludwig; Nathalie Baracaldo; Alan King; Yi Zhou 015; Keith Houck; Ambrish Rawat; Mark Purcell; Naoise Holohan; Mikio Takeuchi; Ryo Kawahara; Nir Drucker; et al. (CoRR, 2023)

Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks. - Shuli Jiang; Swanand Ravindra Kadhe; Yi Zhou 015; Ling Cai; Nathalie Baracaldo (CoRR, 2023)

Graph Topology Invariant Gradient and Sampling Complexity for Decentralized and Stochastic Optimization. - Guanghui Lan; Yuyuan Ouyang; Yi Zhou 015 (SIAM J. Optim., 2023)

Effective Data Distillation for Tabular Datasets (Student Abstract). - Inwon Kang; Parikshit Ram; Yi Zhou 015; Horst Samulowitz; Oshani Seneviratne (AAAI, 2024)

Enhancing In-context Learning via Linear Probe Calibration. - Momin Abbas; Yi Zhou 015; Parikshit Ram; Nathalie Baracaldo; Horst Samulowitz; Theodoros Salonidis; Tianyi Chen (AISTATS, 2024)

Towards Collecting Royalties for Copyrighted Data for Generative Models. - Heiko Ludwig; Yi Zhou 015; Syed Zawad; Yuya Jeremy Ong; Pengyuan Li 001; Eric Butler; Eelaaf Zahid (ICWS, 2024)

Byzantine-Resilient Bilevel Federated Learning. - Momin Abbas; Yi Zhou 015; Nathalie Baracaldo; Horst Samulowitz; Parikshit Ram; Theodoros Salonidis (SAM, 2024)

Granite Code Models: A Family of Open Foundation Models for Code Intelligence. - 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; et al. (CoRR, 2024)

Turning Generative Models Degenerate: The Power of Data Poisoning Attacks. - Shuli Jiang; Swanand Ravindra Kadhe; Yi Zhou 015; Farhan Ahmed; Ling Cai; Nathalie Baracaldo (CoRR, 2024)

Mitigating Forgetting in LLM Supervised Fine-Tuning and Preference Learning. - Heshan Devaka Fernando; Han Shen; Parikshit Ram; Yi Zhou 015; Horst Samulowitz; Nathalie Baracaldo; Tianyi Chen (CoRR, 2024)

MAP: Multi-Human-Value Alignment Palette. - Xinran Wang; Qi Le; Ammar Ahmed; Enmao Diao; Yi Zhou 015; Nathalie Baracaldo; Jie Ding 002; Ali Anwar 001 (ICLR, 2025)

On Learning Representations for Tabular Data Distillation. - Inwon Kang; Parikshit Ram; Yi Zhou 015; Horst Samulowitz; Oshani Seneviratne (Trans. Mach. Learn. Res., 2025)

GneissWeb: Preparing High Quality Data for LLMs at Scale. - 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; et al. (CoRR, 2025)