Efficiently learning structured distributions from untrusted batches

We study the problem, introduced by Qiao and Valiant, of learning from untrusted batches. Here, we assume m users, all of whom have samples from some underlying distribution over 1, …, n . Each user sends a batch of k i.i.d. samples from this distribution; however an є-fraction of users are untrustworthy and can send adversarially chosen responses. The goal of the algorithm is to learn in total variation distance. When k = 1 this is the standard robust univariate density estimation setting and it is well-understood that (є) error is unavoidable. Suprisingly, Qiao and Valiant gave an estimator which improves upon this rate when k is large. Unfortunately, their algorithms run in time which is exponential in either n or k .

Authors

• Sitan Chen
• Jerry Li 0001
• Ankur Moitra

Keywords

• Robust statistics
• VC complexity
• federated learning
• sum-of-squares