Composite-Path Switching. - Shay Vargaftik; Katherine Barabash; Yaniv Ben-Itzhak; Ofer Biran; Isaac Keslassy; Dean H. Lorenz; Ariel Orda (CoNEXT, 2016)

Virtualized Congestion Control. - Bryce W. Cronkite-Ratcliff; Aran Bergman; Shay Vargaftik; Madhusudhan Ravi; Nick McKeown; Ittai Abraham; Isaac Keslassy (SIGCOMM, 2016)

Optics in Data Centers: Adapting to Diverse Modern Workloads. - Shay Vargaftik; Isaac Keslassy; Ariel Orda; Katherine Barabash; Yaniv Ben-Itzhak; Ofer Biran; Dean H. Lorenz (SYSTOR, 2016)

Stable user-defined priorities. - Shay Vargaftik; Isaac Keslassy; Ariel Orda (INFOCOM, 2017)

dRMT: Disaggregated Programmable Switching. - Sharad Chole; Andy Fingerhut; Sha Ma; Anirudh Sivaraman; Shay Vargaftik; Alon Berger; Gal Mendelson; Mohammad Alizadeh; Shang-Tse Chuang; Isaac Keslassy; Ariel Orda; Tom Edsall (SIGCOMM, 2017)

No Packet Left Behind: Avoiding Starvation in Dynamic Topologies. - Shay Vargaftik; Isaac Keslassy; Ariel Orda (IEEE/ACM Trans. Netw., 2017)

LUMOS: A Fast and Efficient Optical Circuit Switch Scheduling Technique. - Ariel Livshits; Shay Vargaftik (IEEE Commun. Lett., 2018)

RADE: Resource-Efficient Supervised Anomaly Detection Using Decision Tree-Based Ensemble Methods. - Shay Vargaftik; Isaac Keslassy; Yaniv Ben-Itzhak (CoRR, 2019)

Faster and More Accurate Measurement through Additive-Error Counters. - Ran Ben Basat; Gil Einziger; Michael Mitzenmacher; Shay Vargaftik (INFOCOM, 2020)

C-share: optical circuits sharing for software-defined data-centers. - Shay Vargaftik; Cosmin Caba; Liran Schour; Yaniv Ben-Itzhak (Comput. Commun. Rev., 2020)

LSQ: Load Balancing in Large-Scale Heterogeneous Systems With Multiple Dispatchers. - Shay Vargaftik; Isaac Keslassy; Ariel Orda (IEEE/ACM Trans. Netw., 2020)

Load balancing with JET: just enough tracking for connection consistency. - Gal Mendelson; Shay Vargaftik; Dean H. Lorenz; Kathy Barabash; Isaac Keslassy; Ariel Orda (CoNEXT, 2021)

Characterizing, exploiting, and detecting DMA code injection vulnerabilities in the presence of an IOMMU. - Alex Markuze; Shay Vargaftik; Gil Kupfer; Boris Pismenny; Nadav Amit; Adam Morrison 001; Dan Tsafrir (EuroSys, 2021)

How to Send a Real Number Using a Single Bit (And Some Shared Randomness). - Ran Ben Basat; Michael Mitzenmacher; Shay Vargaftik (ICALP, 2021)

SALSA: Self-Adjusting Lean Streaming Analytics. - Ran Ben Basat; Gil Einziger; Michael Mitzenmacher; Shay Vargaftik (ICDE, 2021)

DRIVE: One-bit Distributed Mean Estimation. - Shay Vargaftik; Ran Ben-Basat; Amit Portnoy; Gal Mendelson; Yaniv Ben-Itzhak; Michael Mitzenmacher (NeurIPS, 2021)

Communication-Efficient Federated Learning via Robust Distributed Mean Estimation. - Shay Vargaftik; Ran Ben Basat; Amit Portnoy; Gal Mendelson; Yaniv Ben-Itzhak; Michael Mitzenmacher (CoRR, 2021)

RADE: resource-efficient supervised anomaly detection using decision tree-based ensemble methods. - Shay Vargaftik; Isaac Keslassy; Ariel Orda; Yaniv Ben-Itzhak (Mach. Learn., 2021)

AnchorHash: A Scalable Consistent Hash. - Gal Mendelson; Shay Vargaftik; Katherine Barabash; Dean H. Lorenz; Isaac Keslassy; Ariel Orda (IEEE/ACM Trans. Netw., 2021)

Efficient multiclass classification with duet. - Shay Vargaftik; Yaniv Ben-Itzhak (EuroMLSys@EuroSys, 2022)

EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning. - Shay Vargaftik; Ran Ben Basat; Amit Portnoy; Gal Mendelson; Yaniv Ben-Itzhak; Michael Mitzenmacher (ICML, 2022)

IIsy: Practical In-Network Classification. - Changgang Zheng; Zhaoqi Xiong; Thanh T. Bui; Siim Kaupmees; Riyad Bensoussane; Antoine Bernabeu; Shay Vargaftik; Yaniv Ben-Itzhak; Noa Zilberman (CoRR, 2022)

Automating In-Network Machine Learning. - Changgang Zheng; Mingyuan Zang; Xinpeng Hong; Riyad Bensoussane; Shay Vargaftik; Yaniv Ben-Itzhak; Noa Zilberman (CoRR, 2022)

QUIC-FL: Quick Unbiased Compression for Federated Learning. - Ran Ben Basat; Shay Vargaftik; Amit Portnoy; Gil Einziger; Yaniv Ben-Itzhak; Michael Mitzenmacher (CoRR, 2022)

ScionFL: Secure Quantized Aggregation for Federated Learning. - Yaniv Ben-Itzhak; Helen Möllering; Benny Pinkas; Thomas Schneider 003; Ajith Suresh; Oleksandr Tkachenko; Shay Vargaftik; Christian Weinert; Hossein Yalame; Avishay Yanai (CoRR, 2022)

Memento: Making Sliding Windows Efficient for Heavy Hitters. - Ran Ben Basat; Gil Einziger; Isaac Keslassy; Ariel Orda; Shay Vargaftik; Erez Waisbard (IEEE/ACM Trans. Netw., 2022)

DoCoFL: Downlink Compression for Cross-Device Federated Learning. - Ron Dorfman; Shay Vargaftik; Yaniv Ben-Itzhak; Kfir Yehuda Levy (ICML, 2023)

THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression. - Minghao Li 003; Ran Ben Basat; Shay Vargaftik; ChonLam Lao; Kevin Xu; Xinran Tang; Michael Mitzenmacher; Minlan Yu (CoRR, 2023)

Ultima: Robust and Tail-Optimal AllReduce for Distributed Deep Learning in the Cloud. - Ertza Warraich; Omer Shabtai; Khalid Manaa; Shay Vargaftik; Yonatan Piasetzky; Matty Kadosh; Lalith Suresh 001; Muhammad Shahbaz 001 (CoRR, 2023)

ScionFL: Efficient and Robust Secure Quantized Aggregation. - Yaniv Ben-Itzhak; Helen Möllering; Benny Pinkas; Thomas Schneider 003; Ajith Suresh; Oleksandr Tkachenko; Shay Vargaftik; Christian Weinert; Hossein Yalame; Avishay Yanai (IACR Cryptol. ePrint Arch., 2023)

Distributed Dispatching in the Parallel Server Model. - Guy Goren; Shay Vargaftik; Yoram Moses (IEEE/ACM Trans. Netw., 2023)

Stochastic coordination in heterogeneous load balancing systems. - Guy Goren; Shay Vargaftik; Yoram Moses (Distributed Comput., 2023)

When ML Training Cuts Through Congestion: Just-in-Time Gradient Compression via Packet Trimming. - Xiaoqi Chen; Shay Vargaftik; Ran Ben Basat (HotNets, 2024)

Beyond Throughput and Compression Ratios: Towards High End-to-end Utility of Gradient Compression. - Wenchen Han; Shay Vargaftik; Michael Mitzenmacher; Brad Karp; Ran Ben Basat (HotNets, 2024)

Accelerating Federated Learning with Quick Distributed Mean Estimation. - Ran Ben-Basat; Shay Vargaftik; Amit Portnoy; Gil Einziger; Yaniv Ben-Itzhak; Michael Mitzenmacher (ICML, 2024)

Optimal and Approximate Adaptive Stochastic Quantization. - Ran Ben-Basat; Yaniv Ben-Itzhak; Michael Mitzenmacher; Shay Vargaftik (NeurIPS, 2024)

THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression. - Minghao Li 003; Ran Ben Basat; Shay Vargaftik; ChonLam Lao; Kevin Xu; Michael Mitzenmacher; Minlan Yu (NSDI, 2024)

In-Network Machine Learning Using Programmable Network Devices: A Survey. - Changgang Zheng; Xinpeng Hong; Damu Ding; Shay Vargaftik; Yaniv Ben-Itzhak; Noa Zilberman (IEEE Commun. Surv. Tutorials, 2024)

Optimal and Near-Optimal Adaptive Vector Quantization. - Ran Ben-Basat; Yaniv Ben-Itzhak; Michael Mitzenmacher; Shay Vargaftik (CoRR, 2024)

Lucy: Think and Reason to Solve Text-to-SQL. - Nina Narodytska; Shay Vargaftik (CoRR, 2024)

Planter: Rapid Prototyping of In-Network Machine Learning Inference. - Changgang Zheng; Mingyuan Zang; Xinpeng Hong; Liam Perreault; Riyad Bensoussane; Shay Vargaftik; Yaniv Ben-Itzhak; Noa Zilberman (Comput. Commun. Rev., 2024)

IIsy: Hybrid In-Network Classification Using Programmable Switches. - Changgang Zheng; Zhaoqi Xiong; Thanh T. Bui; Siim Kaupmees; Riyad Bensoussane; Antoine Bernabeu; Shay Vargaftik; Yaniv Ben-Itzhak; Noa Zilberman (IEEE/ACM Trans. Netw., 2024)

OptiReduce: Resilient and Tail-Optimal AllReduce for Distributed Deep Learning in the Cloud. - Ertza Warraich; Omer Shabtai; Khalid Manaa; Shay Vargaftik; Yonatan Piasetzky; Matty Kadosh; Lalith Suresh 001; Muhammad Shahbaz 001 (NSDI, 2025)

HACK: Homomorphic Acceleration via Compression of the Key-Value Cache for Disaggregated LLM Inference. - Zeyu Zhang 005; Haiying Shen; Shay Vargaftik; Ran Ben Basat; Michael Mitzenmacher; Minlan Yu (CoRR, 2025)

Counter Pools: Counter Representation for Efficient Stream Processing. - Ran Ben Basat; Gil Einziger; Bilal Tyah; Shay Vargaftik (CoRR, 2025)