7 accepted papers
Unsupervised pre-training is a common step in developing computer vision models and large language models. In this setting, the absence of labels requires the use of similarity-based loss functions, such as the contrastive loss, that favor minimizing the distance between similar inputs and maximizin…
We propose reconstruction advantage measures to audit label privatization mechanisms. A reconstruction advantage measure quantifies the increase in an attacker's ability to infer the true label of an unlabeled example when provided with a private version of the labels in a dataset (e.g., aggregate o…
A well-known numerical bottleneck in the differentially-private stochastic gradient descent (DP-SGD) algorithm is the computation of the gradient norm for each example in a large input batch. When the loss function in DP-SGD is consists of an intermediate linear operation, existing methods in the li…
We consider the problem of Learning from Label Proportions (LLP), a weakly supervised classification setup where instances are grouped into i.i.d. “bags”, and only the frequency of class labels at each bag is available. Albeit, the objective of the learner is to achieve low task loss at an individu…
We study differentially private mechanisms for sharing training data in machine learning settings. Our goal is to enable learning of an accurate predictive model while protecting the privacy of each user's label. Previous work established privacy guarantees that assumed the features are public and g…
Modern statistical estimation is often performed in a distributed setting where each sample belongs to single user who shares their data with a central server. Users are typically concerned with preserving the privacy of their sample, and also with minimizing the amount of data they must transmit to…
We analyze the problem of linear bandits under heavy tailed noise. Most of of the work on linear bandits has been based on the assumption of bounded or sub-Gaussian noise. However, this assumption is often violated in common scenarios such as financial markets. We present two algorithms to tackle th…