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Found 7 papers, 2 papers with code
Leveraging Function Space Aggregation for Federated Learning at Scale
Many federated learning algorithms, including the canonical Federated Averaging (FedAvg), take a direct (possibly weighted) average of the client parameter updates, motivated by results in distributed optimization.
Efficient Parametric Approximations of Neural Network Function Space Distance
It is often useful to compactly summarize important properties of model parameters and training data so that they can be used later without storing and/or iterating over the entire dataset.
Dataset Inference for Self-Supervised Models
We introduce a new dataset inference defense, which uses the private training set of the victim encoder model to attribute its ownership in the event of stealing.
On the Difficulty of Defending Self-Supervised Learning against Model Extraction
We construct several novel attacks and find that approaches that train directly on a victim's stolen representations are query efficient and enable high accuracy for downstream models.
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift
A fundamental assumption of most machine learning algorithms is that the training and test data are drawn from the same underlying distribution.
Adaptive Risk Minimization: Learning to Adapt to Domain Shift
A fundamental assumption of most machine learning algorithms is that the training and test data are drawn from the same underlying distribution.
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos
In this paper, we study how these challenges can be alleviated with an automated robotic learning framework, in which multi-stage tasks are defined simply by providing videos of a human demonstrator and then learned autonomously by the robot from raw image observations.
