204 citations · 476 across the 32 of their papers we have counts for
5 papers · 1 filter
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
Differentially Private Meta-Learning
Jeffrey Li, Mikhail Khodak, Sebastian Caldas +1
Parameter-transfer is a well-known and versatile approach for meta-learning, with applications including few-shot learning, federated learning, and reinforcement learning. However,…
Adaptive Gradient-Based Meta-Learning Methods
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar
We build a theoretical framework for designing and understanding practical meta-learning methods that integrates sophisticated formalizations of task-similarity with the extensive…
A Theoretical Analysis of Contrastive Unsupervised Representation Learning
Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak +2
Recent empirical works have successfully used unlabeled data to learn feature representations that are broadly useful in downstream classification tasks. Several of these methods a…
Provable Guarantees for Gradient-Based Meta-Learning
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar
We study the problem of meta-learning through the lens of online convex optimization, developing a meta-algorithm bridging the gap between popular gradient-based meta-learning and…