19 papers
SP-CACW: Convergence-Aware Client Weighting for Selfish Personalized Learning
Yaron Kiselman, Kfir Y. Levy
Collaborative learning is sustainable only when it benefits each participant. Standard federated learning optimizes a global average objective, which can under perform for clients…
Local MixVR: Breaking the Communication-Sample Dependence in Distributed Learning
Tehila Dahan, Bassel Hamoud, Roie Reshef +2
Communication overhead is a crucial bottleneck in scalable distributed learning. While existing methods aim to efficiently utilize data points, such as Local SGD, Minibatch SGD, an…
Bringing Order to Asynchronous SGD: Towards Optimality under Data-Dependent Delays with Momentum
Tehila Dahan, Roie Reshef, Sharon Goldstein +1
Asynchronous stochastic gradient descent (SGD) enables scalable distributed training but suffers from gradient staleness. Existing mitigation strategies, such as delay-adaptive lea…
Optimal Sample Complexity for Single Time-Scale Actor-Critic with Momentum
Navdeep Kumar, Tehila Dahan, Lior Cohen +4
We establish an optimal sample complexity of for obtaining an -optimal global policy using a single-timescale actor-critic (AC) algorithm in infinite-horizon disco…
Gradient-Variation Online Adaptivity for Accelerated Optimization with Hölder Smoothness
Yuheng Zhao, Yu-Hu Yan, Kfir Yehuda Levy +1
Smoothness is known to be crucial for acceleration in offline optimization, and for gradient-variation regret minimization in online learning. Interestingly, these two problems are…
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…