9 citations · 10 across the 6 of their papers we have counts for
6 papers · 1 filter
Prediction-Powered Semi-Supervised Learning with Online Power Tuning
Noa Shoham, Ron Dorfman, Shalev Shaer +2
Prediction-Powered Inference (PPI) is a recently proposed statistical inference technique for parameter estimation that leverages pseudo-labels on both labeled and unlabeled data t…
Enhancing Parallelism in Decentralized Stochastic Convex Optimization
Ofri Eisen, Ron Dorfman, Kfir Y. Levy
Decentralized learning has emerged as a powerful approach for handling large datasets across multiple machines in a communication-efficient manner. However, such methods often face…
Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers
Ron Dorfman, Naseem Yehya, Kfir Y. Levy
Byzantine-robust learning has emerged as a prominent fault-tolerant distributed machine learning framework. However, most techniques focus on the static setting, wherein the identi…
DoCoFL: Downlink Compression for Cross-Device Federated Learning
Ron Dorfman, Shay Vargaftik, Yaniv Ben-Itzhak +1
Many compression techniques have been proposed to reduce the communication overhead of Federated Learning training procedures. However, these are typically designed for compressing…
Adapting to Mixing Time in Stochastic Optimization with Markovian Data
Ron Dorfman, Kfir Y. Levy
We consider stochastic optimization problems where data is drawn from a Markov chain. Existing methods for this setting crucially rely on knowing the mixing time of the chain, whic…
Offline Meta Learning of Exploration
Ron Dorfman, Idan Shenfeld, Aviv Tamar
Consider the following instance of the Offline Meta Reinforcement Learning (OMRL) problem: given the complete training logs of conventional RL agents, trained on different…