3k citations · 24.2k across the 299 of their papers we have counts for
317 papers
Provably Optimal Learning Algorithms for Assistance Games
Nivasini Ananthakrishnan, Mark Bedaywi, Michael I. Jordan +2
This paper studies an online variant of the assistance games framework, where an informed agent and an uninformed agent repeatedly interact over timesteps to optimize a common…
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
Kaichao You, Ximei Wang, Mingsheng Long +1
Deep unsupervised domain adaptation (Deep UDA) methods successfully leverage rich labeled data in a source domain to boost the performance on related but unlabeled data in a target…
Delegating Data Collection in Decentralized Machine Learning
Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan +1
Motivated by the emergence of decentralized machine learning (ML) ecosystems, we study the delegation of data collection. Taking the field of contract theory as our starting point,…
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning
Baihe Huang, Sai Praneeth Karimireddy, Michael I. Jordan
For a federated learning model to perform well, it is crucial to have a diverse and representative dataset. However, the data contributors may only be concerned with the performanc…
Class-Conditional Conformal Prediction with Many Classes
Tiffany Ding, Anastasios N. Angelopoulos, Stephen Bates +2
Standard conformal prediction methods provide a marginal coverage guarantee, which means that for a random test point, the conformal prediction set contains the true label with a u…
Provably Personalized and Robust Federated Learning
Mariel Werner, Lie He, Michael Jordan +2
Identifying clients with similar objectives and learning a model-per-cluster is an intuitive and interpretable approach to personalization in federated learning. However, doing so…