3 citations · 5 across the 3 of their papers we have counts for
3 papers
Highway Reinforcement Learning
Yuhui Wang, Miroslav Strupl, Francesco Faccio +5
Learning from multi-step off-policy data collected by a set of policies is a core problem of reinforcement learning (RL). Approaches based on importance sampling (IS) often suffer…
Improving Accelerated Federated Learning with Compression and Importance Sampling
Michał Grudzień, Grigory Malinovsky, Peter Richtárik
Federated Learning is a collaborative training framework that leverages heterogeneous data distributed across a vast number of clients. Since it is practically infeasible to reques…
Can 5th Generation Local Training Methods Support Client Sampling? Yes!
Michał Grudzień, Grigory Malinovsky, Peter Richtárik
The celebrated FedAvg algorithm of McMahan et al. (2017) is based on three components: client sampling (CS), data sampling (DS) and local training (LT). While the first two are rea…